Intelligent energy management system for new energy vehicle, and control method and related device

Abstract

An intelligent energy management system for a new energy vehicle, and a control method and a related device. The system comprises: a driving apparatus, which comprises an engine (10), a driving electric motor (20) and a generator (30), wherein the engine (10) is used for selectively outputting power to wheel ends of a vehicle, the driving electric motor (20) is used for outputting power to the wheel ends, and the generator (30) is connected to the engine (10) so as to generate electricity under the driving of the engine (10); a power battery (40), which is used for supplying electricity to the driving electric motor (20), and is charged on the basis of alternating currents output by the generator (30) or the driving electric motor (20); and a control apparatus (50), which is used for acquiring multi-domain data fusion information, predicting path-based vehicle energy consumption for a preset traveling path on the basis of the multi-domain data fusion information, planning a target SOC for each road section on the basis of road-section-based vehicle energy consumption for each road section, and controlling the driving apparatus on the basis of the target SOC for each road section and actual vehicle demands, such that the engine (10) is in an efficient operating interval during operation.

Description

New energy vehicle energy intelligent management system, control method and related equipment

[0001] This application claims priority to Chinese patent application No. 202410658157.7, filed on May 27, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of intelligent vehicle control technology, and in particular to an intelligent energy management system, control method, control device, vehicle, and storage medium for new energy vehicles. Background Technology

[0003] Current energy management strategies for hybrid electric vehicles primarily focus on meeting power demands and maintaining the battery's state of charge (SOC) as control criteria. When the vehicle is running, the energy management strategy rationally allocates power from each power source based on their efficiency characteristics to improve the driving efficiency of the powertrain. Summary of the Invention

[0004] Some embodiments of this disclosure provide an intelligent energy management system, control method, control device, vehicle, and storage medium for new energy vehicles, which can reduce fuel consumption for users and improve the driving experience.

[0005] In a first aspect, some embodiments of this disclosure provide an intelligent energy management system for new energy vehicles. The system includes a drive unit, a power battery, and a control unit. The drive unit includes an engine, a drive motor, and a generator. The engine selectively outputs power to the wheels of the vehicle; the drive motor outputs power to the wheels; and the generator is connected to the engine to generate electricity under the drive of the engine. The power battery supplies power to the drive motor and is charged based on the alternating current output from either the generator or the drive motor. The control device is configured to: acquire multi-domain data fusion information, the multi-domain fusion data information including at least cockpit domain information and power domain information; the cockpit domain information including at least user behavior information and road condition information of a preset travel route, the power domain information including at least vehicle status information; predict the route-based vehicle energy consumption of the preset travel route based on the multi-domain data fusion information, the preset travel route including multiple road segments, the route-based vehicle energy consumption including the segment-based vehicle energy consumption of the multiple road segments; plan the target SOC of each road segment based on the segment-based vehicle energy consumption of the multiple road segments with the goal of minimizing fuel consumption of the preset travel route; and control the drive device and the power battery based on the target SOC of each road segment and the actual vehicle requirements, so that the engine operates in a high-efficiency operating range.

[0006] Secondly, some embodiments of this disclosure provide a control method for an intelligent energy management system for new energy vehicles. This method includes: acquiring multi-domain fusion information, which includes at least cabin domain information and power domain information; the cabin domain information includes at least user behavior information and road condition information for a preset travel route, and the power domain information includes at least vehicle status information; predicting the total vehicle energy consumption along the preset travel route based on the multi-domain fusion information, where the preset travel route includes multiple road segments, and the total vehicle energy consumption along the route includes the total vehicle energy consumption of each road segment; planning the target SOC for each road segment based on the total vehicle energy consumption of each road segment, with the goal of minimizing fuel consumption along the preset travel route; and controlling the engine, drive motor, generator, and power battery of the new energy vehicle based on the target SOC of each road segment and the actual vehicle requirements, so that the engine operates within its high-efficiency operating range.

[0007] Thirdly, some embodiments of this disclosure provide a control device, which includes a multi-source data fusion unit, an energy consumption prediction unit, a dynamic planning unit, and an intelligent control unit. The multi-source data fusion unit is used to acquire multi-domain data fusion information, which includes at least cockpit domain information and power domain information. The cockpit domain information includes at least user behavior information and road condition information for a preset travel route, and the power domain information includes at least vehicle status information. The energy consumption prediction unit is used to predict the total vehicle energy consumption for a preset travel route based on the multi-domain data fusion information. The preset travel route includes multiple road segments, and the total vehicle energy consumption includes the total vehicle energy consumption for each of the multiple road segments. The dynamic planning unit is used to plan the target SOC for each road segment based on the total vehicle energy consumption for each of the multiple road segments, with the goal of minimizing fuel consumption along the preset travel route. The intelligent control unit is used to control the engine, drive motor, generator, and power battery of the new energy vehicle based on the target SOC for each road segment and the actual vehicle requirements, so that the engine operates within its high-efficiency operating range.

[0008] Fourthly, some embodiments of this disclosure provide a control device including a memory, a communication interface, and a processor. The memory, communication interface, and processor are interconnected; the memory stores a computer program, and the processor calls the computer program stored in the memory to implement the method described in the second aspect.

[0009] Fifthly, some embodiments of this disclosure provide a vehicle that includes a new energy vehicle energy intelligent management system for performing the system described in the first aspect.

[0010] Sixthly, some embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method of the second aspect described above.

[0011] In a seventh aspect, some embodiments of this disclosure provide an intelligent energy management system for new energy vehicles, including an engine, a drive motor, a generator, a power battery, and a control device. The engine is used to selectively output power to the vehicle's wheels; the drive motor is used to output power to the wheels; the generator is connected to the engine to generate electricity under the engine's drive. The power battery is used to supply power to the drive motor and to charge the vehicle based on the AC power output from the generator or the drive motor. The control device includes a multi-source data fusion module, an energy consumption prediction module, a dynamic programming module, and an intelligent control module. The multi-source data fusion module is used to acquire multi-domain data fusion information, which includes at least cabin domain information and power domain information. The cabin domain information includes at least user behavior information and road condition information for a preset travel route, and the power domain information includes at least vehicle status information. The energy consumption prediction module is used to predict the total vehicle energy consumption for a preset travel route based on the multi-domain data fusion information. The preset travel route includes multiple road segments, and the total vehicle energy consumption includes the total vehicle energy consumption for each of the multiple road segments. The dynamic planning module is used to plan the target SOC for each road segment based on the vehicle's energy consumption for each road segment, with the goal of minimizing fuel consumption along the preset travel route. The intelligent control module is used to control the engine, drive motor, generator, and power battery according to the target SOC for each road segment and the actual vehicle requirements, so that the engine operates within its high-efficiency range.

[0012] In some embodiments of this disclosure, the target SOC of each road segment is planned with the goal of minimizing fuel consumption along the travel route. The vehicle is controlled according to the target SOC of each road segment and the actual vehicle demand, so as to achieve a reasonable distribution of fuel and electricity in the hybrid vehicle and reduce vehicle fuel consumption and usage costs.

[0013] Meanwhile, by controlling the engine, drive motor, generator, and power battery, the engine can operate in a high-efficiency range, improving its NVH performance, avoiding frequent engine start-stop cycles, and enhancing driving comfort.

[0014] Furthermore, the system predicts the vehicle energy consumption along the preset travel route based on multi-domain fusion information. This involves fusing information from the cabin domain and the power domain to predict the vehicle energy consumption along the route, which improves the accuracy of energy consumption prediction and further enhances fuel efficiency. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in some embodiments or background technologies of this disclosure, the accompanying drawings used in some embodiments or background technologies of this disclosure will be described below.

[0016] Figure 1 is a schematic diagram of the architecture of a new energy vehicle energy intelligent management system according to some embodiments of the present disclosure;

[0017] Figure 2 is a schematic diagram of the architecture of another new energy vehicle energy intelligent management system according to some embodiments of the present disclosure;

[0018] Figure 3 is a schematic diagram of a candidate energy-saving path determination logic according to some embodiments of the present disclosure;

[0019] Figure 4 is a schematic diagram of a power replenishment strategy according to some embodiments of the present disclosure;

[0020] Figure 5 is a schematic diagram of energy consumption prediction according to some embodiments of the present disclosure;

[0021] Figure 6 is a logical schematic diagram of an energy consumption prediction method according to some embodiments of the present disclosure;

[0022] Figure 7 is a schematic diagram of road segment division according to some embodiments of the present disclosure;

[0023] Figure 8 is a schematic diagram of a predicted SOC according to some embodiments of the present disclosure;

[0024] Figure 9 is another schematic diagram of predicted SOC according to some embodiments of the present disclosure;

[0025] Figure 10 is a schematic diagram of energy management based on historical driving data according to some embodiments of the present disclosure;

[0026] Figure 11 is a schematic diagram of a partial correction logic for traffic light information fusion according to some embodiments of the present disclosure;

[0027] Figure 12 is a schematic diagram of an automatic navigation initial time update logic according to some embodiments of the present disclosure;

[0028] Figure 13 is a schematic flowchart of a control method for an intelligent energy management system for new energy vehicles according to some embodiments of the present disclosure;

[0029] Figure 14A is a schematic flowchart of another control method for an intelligent energy management system for new energy vehicles according to some embodiments of the present disclosure;

[0030] Figure 14B is a schematic flowchart of another control method for an intelligent energy management system for new energy vehicles according to some embodiments of the present disclosure.

[0031] Figure 15 is a block diagram of a vehicle according to some embodiments of the present disclosure;

[0032] Figure 16 is a schematic diagram of an energy-saving path according to some embodiments of the present disclosure;

[0033] Figure 17 is a schematic diagram of a power replenishment plan according to some embodiments of the present disclosure;

[0034] Figure 18 is a block diagram of a control device for a new energy vehicle energy intelligent management system according to some embodiments of the present disclosure;

[0035] Figure 19 is a block diagram of a control device according to some embodiments of the present disclosure. Detailed Implementation

[0036] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0037] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, components, features, and elements with the same names in different embodiments of this disclosure may have the same meaning or different meanings, the meaning of which must be determined by its interpretation in that embodiment or further in the context of that embodiment.

[0038] It should be understood that although the terms first, second, third, etc., may be used in this document to describe various types of information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if," as used herein, can be interpreted as "when," "when," or "in response to determination." Furthermore, as used herein, the singular forms "a," "one," and "the" are intended to also include the plural forms unless the context indicates otherwise.

[0039] It should be further understood that the terms "comprising" or "including" indicate the presence of a feature, step, operation, element, component, item, type, and / or group, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, types, and / or groups. The terms "or," "and / or," "including at least one of the following," etc., as used in this disclosure may be interpreted as inclusive, or mean any one or any combination thereof. For example, "including at least one of the following: A, B, C" means "any one of the following: A; B; C; A and B; A and C; B and C; A and B and C," and similarly, "A, B, or C" or "A, B, and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A and B and C." Exceptions to this definition only arise when a combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0040] It should be understood that although the steps in the flowcharts of some embodiments of this disclosure are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages, which are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0041] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0042] The energy management strategies of hybrid electric vehicles in related technologies rely solely on the vehicle's own operating conditions for energy control, which often leads to increased fuel consumption and higher energy consumption.

[0043] Therefore, some embodiments of this disclosure provide an intelligent energy management system for new energy vehicles.

[0044] Figure 1 is a schematic diagram of the architecture of a new energy vehicle energy intelligent management system according to some embodiments of the present disclosure. Referring to Figure 1, the new energy vehicle energy intelligent management system may include: a drive unit (not shown), the drive unit including an engine 10, a drive motor 20, a generator 30, a power battery 40, and a control unit 50. The drive unit is used to provide driving force for the vehicle.

[0045] The control device 50 may include at least one of a power domain control module, a cockpit domain module, and a cloud-based control module. For example, the control device may be implemented in a power domain control module (such as the VCU of the power domain control module), in a cockpit domain module (such as the host of the cockpit domain), or in a cloud server, or it may be implemented by the cooperation of several of the above modules. This disclosure does not limit the scope of the control device.

[0046] For example, engine 10 is used to selectively output power to the wheels of the vehicle. Drive motor 20 is used to output power to the wheels. Generator 30 is connected to engine 10 to generate electricity driven by engine 10. Power battery 40 is used to power drive motor 20 and to charge it according to the AC power output from generator 30 or drive motor 20.

[0047] The control device 50 is used to acquire multi-domain fusion information, which includes at least cockpit domain information and power domain information. For example, cockpit domain information includes at least user behavior information and road condition information for the preset travel route, and power domain information includes at least vehicle status information. Based on the multi-domain fusion information, the device predicts the total vehicle energy consumption for the preset travel route, which includes multiple road segments, and the total vehicle energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the device plans the target State of Charge (SOC) for each road segment based on its energy consumption. Based on the target SOC for each road segment and the actual vehicle requirements, the device controls the engine 10, drive motor 20, generator 30, and power battery 40 to ensure that the engine 10 operates within its high-efficiency operating range. User behavior information includes driving style, driving habits, and electricity usage habits.

[0048] For example, the engine 10 can be an Atkinson cycle engine. A clutch C1 is provided between the engine 10 and the wheel end. The control device 50 controls the connection and disconnection between the engine 10 and the wheel end by controlling the disengagement and engagement of the clutch C1, so that the engine 10 can selectively output power to the wheel end. This can realize direct drive of the engine 10, that is, the engine 10 directly drives the wheel end.

[0049] For example, when the control device 50 controls the clutch C1 to disengage, the engine 10 is disconnected from the wheel end, and the engine 10 will not directly output power to the wheel end. However, when the control device 50 controls the clutch C1 to engage, the engine 10 is connected to the wheel end, and the engine 10 directly outputs power to the wheel end, thus realizing direct drive of the engine 10.

[0050] Compared to traditional pure range-extended hybrid electric vehicles, this architecture features an engine direct drive path. This avoids the energy conversion losses caused by the lack of an engine direct drive path in traditional pure range-extended hybrid electric vehicles, where even if the engine is very efficient (both its speed and torque are highly efficient), it can only generate electricity through a generator and then supply it to the drive motor. It also avoids the energy conversion losses caused by the frequent charging and discharging of the power battery. This effectively improves the overall vehicle economy.

[0051] The drive motor 20 can be a flat wire motor. The stator winding of the flat wire motor uses rectangular coils, which improves the slot fill factor of the stator slots and reduces the size of the motor, thus greatly improving the power density of the motor. The drive motor 20 is directly connected to the wheel end through gears. The control device 50 controls the operation of the drive motor 20 to output power to the wheel end.

[0052] For example, the drive motor 20 and the generator 30 are arranged in parallel. Compared with other arrangements, such as the drive motor 20 and the generator 30 being arranged coaxially, the parallel arrangement in this embodiment has less requirement for motor design, making it easier to arrange high-power generators and reducing costs.

[0053] The generator 30 can be a flat wire motor. The generator 30 is located between the clutch C1 and the engine 10, and the generator 30 and the engine 10 are directly connected by gears. The control device 50 can drive the generator 30 to generate electricity by controlling the operation of the engine 10. The generated electricity can be controlled by the control device 50 to charge the power battery 40 or supply power to the drive motor 20.

[0054] In some embodiments, when the control device 50 includes a power domain control module, the power domain control module is connected to the drive motor 20 and the generator 30 respectively. The power domain control module supplies power to the drive motor 20 according to the AC power output from the generator 30. The power battery 40 is connected to the power domain control module. The power battery 40 supplies power to the drive motor 20 through the power domain control module, or charges the drive motor 20 according to the AC power output from the generator 30 or the drive motor 20. The power domain control module controls the engine 10 to work efficiently or stop according to the target SOC (State of charge, which reflects the remaining capacity of the battery) and the current SOC of the power battery 40. The engine is used to selectively output power to the wheel ends of the vehicle.

[0055] For example, if the target SOC is greater than a certain threshold of the initial SOC, and the actual vehicle demand is less than the engine operating in the efficient economic zone, then the engine 10 is controlled to drive efficiently and generate electricity, storing excess electricity in the power battery 40 and outputting power to the vehicle's wheels; if the target SOC is greater than a certain threshold of the initial SOC, and the actual vehicle demand is greater than or equal to the engine 10 operating in the efficient economic zone, then the engine 10 is controlled to operate in the efficient range and supply power to the power battery 40, with the drive motor outputting power to the vehicle's wheels, or both the engine 10 and the drive motor outputting power to the vehicle's wheels; if the target SOC is less than a certain threshold of the initial SOC, then the engine 10 is controlled to shut down.

[0056] In some embodiments, the intelligent energy management system for new energy vehicles may further include a transmission 70 and a main reducer 80. Referring to Figure 2, which is a schematic diagram of the architecture of another intelligent energy management system for new energy vehicles according to some embodiments of this disclosure, the transmission 70 may further include gears Z1, Z2, Z3, and Z4. For example, the central shaft of gear Z1 is connected to one end of clutch C1; gear Z1 meshes with gear Z2; gear Z2 meshes with gear Z3; the central shaft of gear Z3 is connected to the drive motor 20; the central shaft of gear Z2 is connected to the central shaft of gear Z4; and gear Z4 meshes with the main reduction gear of the main reducer 80. Of course, the transmission 70 may also adopt other structures, which are not limited here.

[0057] In some embodiments, the control device 50 is connected to the engine 10, drive motor 20, generator 30, power battery 40 and clutch C1 respectively, and the control device 50 can send control signals to the engine 10, drive motor 20, generator 30, power battery 40 and clutch C1 to achieve control.

[0058] The control device 50 acquires driving parameters of the hybrid vehicle, such as at least one of wheel-end torque demand, SOC of the power battery 40, and vehicle speed of the hybrid vehicle, such as wheel-end torque demand, which is also the vehicle torque demand.

[0059] The control device 50 controls the engine 10, drive motor 20 and generator 30 according to the driving parameters, so as to make the engine 10 operate in the economic zone by controlling the charging and discharging of the power battery 40.

[0060] For example, the control device 50 can select the operating mode with the lowest equivalent fuel consumption as the current operating mode of the hybrid vehicle by comparing the equivalent fuel consumption of the hybrid vehicle in series mode, parallel mode and EV mode.

[0061] It should be noted that the comparison of equivalent fuel consumption is based on the comparison when engine 10 is operating in the economic zone. For example, engine 10 is operating at 25kW in the economic zone. However, considering driving parameters such as wheel-end torque requirements, the fuel consumption in parallel mode may be lower than that in series mode and also lower than that in EV mode. In this case, the hybrid vehicle is controlled to operate in parallel mode. If the fuel consumption in EV mode is lower than that in parallel mode and also lower than that in series mode, the hybrid vehicle is controlled to operate in EV mode.

[0062] It should also be noted that equivalent fuel consumption refers to the sum of the fuel consumed by the engine 10 itself and the fuel equivalent to the electricity consumed by the power battery 40. For example, the electricity consumed by the power battery 40 can be converted into fuel based on empirical values ​​to obtain the fuel equivalent to the electricity consumed by the power battery 40. When the power battery 40 is charging, the fuel equivalent to the electricity consumed by the power battery 40 is a negative value, and when the power battery 40 is discharging, the fuel equivalent to the electricity consumed by the power battery 40 is a positive value.

[0063] In other words, the control device 50 can comprehensively judge the driving parameters of the hybrid vehicle, such as the wheel-end torque demand, the SOC of the power battery 40, the vehicle speed, and the equivalent fuel consumption of the hybrid vehicle in different operating modes. Under the condition of meeting the power demand and NVH (Noise, Vibration, Harshness), the control device 50 enables the hybrid vehicle to operate in the mode with the lowest equivalent fuel consumption, thereby making the hybrid vehicle have the lowest equivalent fuel consumption under all operating conditions and making the hybrid vehicle highly economical.

[0064] For example, in series mode, the power output between the engine 10 and the wheel ends is cut off (i.e., the clutch C1 is disengaged), and the engine 10 drives the generator 30 to generate electricity and supply it to the drive motor 20. In some cases, the engine 10 will also charge the power battery 40 with excess energy through the generator 30. In parallel mode, the engine 10 is coupled to the wheel ends (i.e., the clutch C1 is engaged). In some cases, the engine 10 will also charge the power battery 40 with excess energy through the drive motor 20. In EV mode, neither the engine 10 nor the generator 30 is working, and the power battery 40 supplies power to the drive motor 20.

[0065] Furthermore, when the hybrid vehicle operates in series, parallel, or EV mode, the charging and discharging control of the power battery 40 ensures that the engine 10 always operates within the economic zone. The equivalent fuel consumption comparison is also based on the engine 10 operating within the economic zone. This allows the engine 10 to operate in the high-efficiency zone across all operating conditions, minimizing the equivalent fuel consumption of the hybrid vehicle and effectively improving its economy. This embodiment ensures that the hybrid vehicle operates in energy-saving mode through the comprehensive control and coordination of the large-capacity power battery, engine, drive motor, and generator.

[0066] In some embodiments of this disclosure, after a user selects a travel route, during the travel process, when the vehicle enters the current road segment, it interacts with vehicles using the speed planning function within a certain range of the current road segment to exchange travel information. Using vehicle-to-everything (V2X) wireless communication technology, the vehicle's speed, road type, and other travel information are sent to nearby vehicles using this function. By using the travel information transmitted by nearby vehicles, the dimensionality and accuracy of the input information are improved.

[0067] When navigation and route exploration are enabled, the system collects interaction data from nearby vehicles to correct navigation information. This interaction data refers to vehicle-to-vehicle (V2V) data. The most commonly used V2V data is vehicle speed. This information can be communicated to surrounding vehicles via short-range wireless communication to form a convoy and enable short-range predictive control. Furthermore, communication can also be conducted via "vehicle-cloud-vehicle" communication, which is achieved through wireless cloud services. This communication is not limited by distance and can supplement map navigation with vehicle speed prediction and energy consumption prediction.

[0068] The system proactively identifies special road conditions ahead, such as traffic light intersections, long uphill sections, and congested following traffic, and promptly updates vehicle speed and SOC planning to ensure the vehicle operates at high efficiency. When navigation and route finding are not enabled, it collects interaction data from nearby vehicles and combines this data with the vehicle's historical data, as well as surrounding information gathered by sensors such as LiDAR, millimeter-wave radar, and cameras, to make short-term predictions of the vehicle's future travel. Based on these short-term predictions, it performs optimization calculations to minimize energy consumption and reduce user fuel consumption.

[0069] During the trip, when the vehicle leaves the current road segment, it uploads historical travel information through the "vehicle-cloud" communication method for relevant data statistical analysis, and supports other vehicles that have recently used the speed planning function to optimize their travel planning through the "vehicle-cloud-vehicle" method.

[0070] In some embodiments of this disclosure, multi-source information available at four levels—people, vehicles, roads, and networks—is collected, and the main factors affecting energy consumption are analyzed, including driving habits (route selection, driving style, charging habits, vehicle settings, etc.), vehicle status (vehicle parameters and load, speed, accessory power consumption, intelligent driving status, etc.), road information (slope, speed limit, road surface adhesion, etc.), and network information (traffic flow, traffic lights, Global Positioning System (GPS) positioning, Vehicle-to-Everything (V2X) information, etc.). Through road type classification, driving style identification, and rolling updates, the multi-source information is spatiotemporally aligned, and combined with the theoretical model and data model with variable weight superposition, the overall vehicle energy consumption of the user's preset route is predicted.

[0071] For example, spatiotemporal alignment refers to using multi-source information as coordinate axes based on a preset travel path (distance or time). Some factors are mainly differences in time sequence, road information is based on map navigation distance information, and network information is similar. After unifying the coordinates, the information is predicted and controlled in sequence.

[0072] Once the user determines their travel route, the vehicle receives navigation information and divides the route into multiple segments according to information attributes such as road type, road length, average speed, and congestion level. The vehicle then converts the segment information according to the data format of its calculation module and merges segments of the same type based on constraints such as average speed, road type, and congestion level. This updates the distribution of segments along the travel path, and the energy consumption of the corresponding segments is calculated by substituting the segment type into the energy consumption prediction model.

[0073] During the trip, the vehicle's position on the travel route is calculated based on the vehicle's GPS module. Based on the relative distance between the current position and the travel destination, the current nth road segment is determined, and the information of the previous n-1 road segments is updated to achieve the spatiotemporal unification of the data.

[0074] In some embodiments of this disclosure, engine 10 is used to selectively output power to the wheel ends of the vehicle. Drive motor 20 is used to output power to the wheel ends. Generator 30 is connected to engine 10 to generate electricity driven by engine 10. Power battery 40 is used to power drive motor 20 and to charge it according to the alternating current output by generator 30 or drive motor 20.

[0075] The control device 50 is used to acquire multi-domain fusion information, which includes at least cockpit domain information and power domain information. For example, cockpit domain information includes at least user behavior information and road condition information of the preset travel route, and power domain information includes at least vehicle status information. Based on the multi-domain fusion information, the device predicts the total vehicle energy consumption of the preset travel route, which includes multiple road segments. The total vehicle energy consumption includes the total vehicle energy consumption of each road segment. With the goal of minimizing fuel consumption of the preset travel route, the device plans the target SOC of each road segment based on the total vehicle energy consumption of each road segment. Based on the target SOC of each road segment and the actual vehicle demand, the device controls the engine 10, drive motor 20, generator 30, and power battery 40 to ensure that the engine 10 operates in its high-efficiency operating range.

[0076] I. Obtain multi-domain fusion information, which includes at least cockpit domain information and power domain information. For example, cockpit domain information includes at least user behavior information and road condition information of the preset travel route, while power domain information includes at least vehicle status information.

[0077] In some embodiments, user behavior information is identified using artificial intelligence (AI) algorithms. This user behavior information is then used to learn the user's electricity consumption habits and driving style, including aggressive driving, normal driving, and gentle driving. Road condition information and vehicle status information for a preset travel route are integrated. For example, road condition information may include road type or traffic flow speed, and vehicle status information may include vehicle wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

[0078] 2. Based on multi-domain fusion information, predict the vehicle energy consumption of the preset travel route. The preset travel route includes multiple road segments, and the vehicle energy consumption of the route includes the vehicle energy consumption of the multiple road segments.

[0079] For example, the method for determining a preset travel route can be:

[0080] In some embodiments, if the navigation system auto-start function is enabled and the current system time is within a preset vehicle usage time period, the navigation system will be automatically turned on, and the preset travel route will be determined based on the vehicle's current location information.

[0081] In some embodiments, when the navigation system's auto-start function is enabled, and the user, i.e. the car owner, sets the car usage time period, such as 9:00 to 10:00 AM and 5:00 to 6:00 PM, the navigation system will automatically start during these two time periods and determine the preset travel route based on the vehicle's current location information.

[0082] In some embodiments, if the navigation system auto-start function is disabled, the preset travel route is determined in response to the user's input destination.

[0083] In some embodiments, the preset travel route includes multiple road segments, which are divided according to the road condition information of each road segment. The total vehicle energy consumption of the route includes the total vehicle energy consumption of multiple road segments, and the total vehicle energy consumption of each road segment is related to the road condition information of each road segment.

[0084] In some embodiments, the division of road segments is related to the traffic information of the preset travel route.

[0085] In some embodiments, the preset travel route can be divided into multiple road segments based on road condition information, including road type and congestion level.

[0086] In some embodiments, each road segment is divided according to at least one of the road type and congestion level of a preset travel route.

[0087] In some embodiments, road sections can be classified as urban road sections, rural road sections, etc., based on road type, or as expressway sections or congested road sections, based on congestion level.

[0088] In some embodiments, in response to the user's input destination, a preset travel route is determined. This can be achieved by determining at least one candidate energy-saving route based on the vehicle's origin and destination. The predicted total vehicle energy consumption of the at least one candidate energy-saving route is lower than that of other routes. The total vehicle energy consumption is predicted based on multi-domain fusion information of each route. In response to the selection operation of the at least one candidate energy-saving route, a preset travel route is determined. The preset travel route refers to the selected candidate energy-saving route. The preset travel route includes multiple road segments, and the total vehicle energy consumption includes the segment total vehicle energy consumption of the multiple road segments.

[0089] In some embodiments, at least one candidate energy-saving path is determined based on the vehicle's starting point and ending point: the starting point of any candidate driving path is the vehicle's starting point, and the ending point of any candidate driving path is the vehicle's ending point; the total energy consumption of the vehicle on each candidate driving path is predicted based on the multi-domain fusion information of each candidate driving path; at least one candidate energy-saving path is determined from the at least one candidate driving path based on the total energy consumption of the vehicle on each candidate driving path; the total energy consumption of the vehicle on any candidate energy-saving path is less than the total energy consumption of the vehicle on other candidate driving paths other than the at least one candidate energy-saving path.

[0090] In some embodiments, based on user behavior information and road condition information and energy consumption impact information for each candidate driving path, such as the user's driving style being aggressive, the gradient of the candidate driving path, and the vehicle's speed, the total energy consumption of the vehicle on each candidate driving path is predicted, and based on the total energy consumption of the vehicle on each candidate driving path, at least one candidate energy-saving path is determined from at least one candidate driving path.

[0091] In some embodiments, determining at least one candidate driving route based on the vehicle's origin and destination can be achieved by: obtaining at least one drivable route from the vehicle's origin to its destination; determining m drivable routes from the at least one drivable route based on a first travel dimension index of each drivable route; where m is a positive integer, and the first travel dimension index of any of the m drivable routes is less than the first travel dimension index of any of the other drivable routes in the at least one drivable route; and determining at least one candidate driving route from the m drivable routes based on a second travel dimension index of the m drivable routes; where the second travel dimension index of any candidate driving route is less than the second travel dimension index of any of the other drivable routes in the m drivable routes.

[0092] In some embodiments, please refer to Figure 3, which is a schematic diagram of a candidate energy-saving path determination logic according to some embodiments of the present disclosure. Different candidate driving paths are mainly selected based on the vehicle's current location and the user's navigation destination, taking into account the influence of travel factors from the starting point to the destination, such as the expected driving distance, expected energy consumption, expected road traffic conditions, etc., and performing big data analysis based on user travel experience, actual traffic flow impact, and other information.

[0093] The most influential factor is selected as the first travel dimension indicator. The selection of the first travel dimension indicator is for the purpose of completing the trip. For example, the path distance is selected as the first travel dimension indicator according to the principle of the shortest travel distance. Based on the road connectivity in the road network, the travel routes are arranged and combined according to the first travel dimension indicator to determine m drivable paths. At the same time, in order to meet the second travel dimension indicator, such as the shortest time, the route with the shortest time in the current combination is selected, and at least one candidate travel path is determined from the m drivable paths.

[0094] In some embodiments, the first travel dimension indicator includes travel distance, and the second travel dimension indicator includes travel time.

[0095] In some embodiments, determining at least one candidate driving path from m drivable paths based on the second travel dimension index of m drivable paths can be achieved by: determining the target drivable path with the smallest second travel dimension index among the m drivable paths; selecting drivable paths from the m drivable paths whose difference between the second travel dimension index and the second travel dimension index of the target drivable path is less than a preset index threshold; and using the selected drivable paths as at least one candidate driving path.

[0096] In some embodiments, based on travel time constraints, the m candidate travel routes with the shortest travel time in the current combination are selected. The selection is based on the principle of the shortest time plus a preset indicator threshold, for example, the preset indicator threshold is 30 minutes. For example, the time variation range of 30 minutes can be updated through self-learning. Finally, n alternative routes are retained, where n is a positive integer.

[0097] In some embodiments, determining at least one candidate driving route based on the vehicle's origin and destination can be achieved by: obtaining at least one drivable route from the vehicle's origin to its destination; obtaining travel dimension indicators for each drivable route, with the weight of each travel dimension indicator corresponding to the vehicle's current travel scenario; performing a weighted calculation on each travel dimension indicator according to each weight to obtain a comprehensive travel indicator for each drivable route; and selecting at least one candidate driving route from the at least one drivable route based on the comprehensive travel indicator for each drivable route; wherein the comprehensive travel indicator of the at least one candidate driving route is less than the comprehensive travel indicators of the other drivable routes in the at least one drivable route.

[0098] In some embodiments, please refer to Figure 3. Different travel dimension indicators, such as time, distance and energy consumption, are assigned according to the importance of completing the trip in different travel scenarios, and different weights Ω1, Ω2...Ωn are assigned. For example, in short-distance travel, the shorter travel time is prioritized, so time has a larger weight and distance and energy consumption have smaller weights. The comprehensive score of the candidate routes is obtained by weighted calculation, and a certain number of candidate driving paths are retained according to the scores.

[0099] For example, when determining a preset travel route, it is also necessary to consider the vehicle's remaining driving range and the driving range to the destination. If the vehicle's remaining driving range is less than the driving range to the destination, then a refueling strategy is determined during the journey along the preset travel route. That is, when the driving range to the destination is greater than the vehicle's remaining driving range L based on predicted energy consumption... 剩余 At that time, determine the energy replenishment strategy during the journey along the preset travel route.

[0100] In some embodiments, determining a refueling strategy during a preset travel route can be achieved by: obtaining the driver's fatigue driving mileage; the fatigue driving mileage represents the mileage the driver can drive before reaching a fatigue driving state; and based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling.

[0101] In some embodiments, please refer to Figure 4, which is a schematic diagram of a replenishment strategy logic according to some embodiments of the present disclosure. When the destination mileage is greater than the vehicle's remaining combined fuel and electric range L based on predicted energy consumption, 剩余 When considering travel time as a constraint, during the selected energy-saving route, the navigation system displays information such as the distribution of networked gas stations (M1, M2, M3, M4...), the distribution of charging stations (N1, N2, N3...), and the driver's fatigue mileage Lmax.

[0102] For example, M1, M2, M3, M4..., N1, N2, N3... are based on the distance to the driver's fatigue rest location. By judging the driver's fatigue mileage, the mileage of charging stations, and the remaining combined fuel and electric range, the system plans refueling and charging, and displays the recommended charging and refueling plans on the vehicle's navigation screen. For example, the driver's fatigue mileage, which is the driver's maximum driving mileage, is obtained and updated based on driving history data; the remaining combined fuel and electric range is updated by an energy consumption prediction method.

[0103] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling can be achieved by: if the vehicle's remaining mileage is greater than or equal to the fatigue driving mileage, and the distance between the first charging address and the end point of the fatigue driving mileage is less than a first preset distance threshold, then controlling the vehicle to drive to the first charging address for charging; the distance between the first charging address and the end point of the fatigue driving mileage is less than the distance between other charging addresses and the end point of the fatigue driving mileage.

[0104] In some embodiments, please refer to Figure 4, the first preset distance threshold is a first threshold, the first charging address is N1, and the remaining electric / gasoline driving range L 剩余 If N1 is greater than or equal to Lmax, further determine if N1 - Lmax is less than the first threshold. If it is less, charge at the nearest charging address N1. If it is less than the first threshold, ignore the driver's fatigue driving mileage and proceed to the charging address N1.

[0105] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling can be achieved by: if the vehicle's remaining mileage is greater than or equal to the fatigue driving mileage, and the distance between the first charging address and the end point of the fatigue driving mileage is greater than or equal to a first preset distance threshold, then controlling the vehicle to drive to a second charging address for charging.

[0106] For example, the distance between the first charging address and the end point of the fatigue driving mileage is less than the distance between other charging addresses and the end point of the fatigue driving mileage, and the second charging address represents the previous charging address of the first charging address in the preset travel route.

[0107] In some embodiments, please refer to Figure 4, when the remaining hybrid driving range L 剩余 If the distance is greater than or equal to Lmax, and N1 - Lmax is greater than or equal to the first threshold, then charging will proceed to the next corresponding charging address on N1, i.e., the second charging address. If the distance exceeds the first threshold, then fatigue driving mileage cannot be ignored, and the vehicle will proceed to the next corresponding charging station on N1 to ensure driving safety.

[0108] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling can be achieved by: if the vehicle's remaining mileage is less than the fatigue driving mileage, and the difference between the fatigue driving mileage and the remaining mileage is less than a second preset distance threshold, then controlling the vehicle to drive to a third charging address for charging; the third charging address is located before the end of the remaining mileage, and the distance between the third charging address and the end of the remaining mileage is less than the distance between other charging addresses and the end of the fatigue driving mileage, where other charging addresses represent the remaining charging addresses other than the third charging address located before the end of the remaining mileage.

[0109] In some embodiments, please refer to Figure 4. The second preset distance threshold is the second threshold value, which is the remaining hybrid driving range L. 剩余 If it is less than Lmax, further determine L. 剩余 -Lmax is less than the second threshold; if it is less than L, then L 剩余 Charge at the nearest available charging address. If the address is below the second threshold, prioritize charging to ensure driving safety.

[0110] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling can be achieved by: if the vehicle's remaining mileage is less than the fatigue driving mileage, and the difference between the fatigue driving mileage and the remaining mileage is greater than or equal to a second preset distance threshold, then controlling the vehicle to drive to the target refueling address for refueling.

[0111] For example, the target refueling address is located before the end of the remaining driving range, and the distance between the target refueling address and the end of the remaining driving range is less than the distance between other refueling addresses and the end of the fatigue driving range. Other refueling addresses refer to the other refueling addresses other than the target refueling address among the refueling addresses located before the end of the remaining driving range.

[0112] In some embodiments, please refer to Figure 4, when the remaining hybrid driving range L 剩余 When less than Lmax, and L 剩余 If -Lmax is greater than or equal to the second threshold, then in L 剩余 Refuel at the nearest gas station. If the distance is greater than or equal to the second threshold, refuel to ensure the shortest possible travel time.

[0113] Once the preset travel route is determined, the total vehicle energy consumption for that route is predicted based on multi-domain fusion information. Predicting the total vehicle energy consumption for the preset travel route can include any one of the following five methods.

[0114] For example, please refer to Figure 5. Figure 5 is a schematic diagram of energy consumption prediction according to some embodiments of this disclosure. Figure 5a is a schematic diagram of map-revealed speed. The actual map data is not speed, but the distance and estimated travel time of each segment. The average speed of this segment is calculated, so it is discrete. Figure 5b is a schematic diagram of energy consumption prediction based directly on map reveal. Since speed is discrete, energy consumption is directly related to speed, so energy consumption is also discrete. Figure 5c is a schematic diagram of the result after planning based on map reveal speed. Speed ​​planning is to control the vehicle to travel at a planned speed, so a continuous speed can reduce energy consumption, so the planning is discrete. Figure 5d is a schematic diagram of the result of energy consumption prediction based on planned speed. For example, the steps for energy consumption prediction based on map reveal data are described in steps 1, 2, or 3 below; the steps for energy consumption prediction based on planned speed are described in the corresponding steps in step 4 below.

[0115] 1. Based on the energy consumption prediction algorithm of automotive theory, predict the total energy consumption of the vehicle along the preset travel route according to the road traffic flow speed and the static parameters of the vehicle; correct the total energy consumption of the vehicle along the route according to user behavior information, and the corrected total energy consumption of the vehicle along the route is the theoretical energy consumption required.

[0116] In some embodiments, the static parameters of the vehicle include at least: wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

[0117] In some embodiments, the theoretical energy demand is calculated as follows: driving force * road traffic flow velocity * time, where driving force F t =F f +F w +F i +F j Among them, F t Used to represent driving force, F f F is used to represent rolling resistance. w F is used to represent air resistance. i F is used to represent slope resistance. j Used to represent acceleration resistance.

[0118] In some embodiments, please refer to Figure 6, which is a logical schematic diagram of an energy consumption prediction method according to some embodiments of the present disclosure. For example, the energy consumption prediction method predicts the energy consumption of a travel route based on a fusion of vehicle theory and data-driven approaches using predicted operating condition information. For example, vehicle theory mainly calculates the main range of energy consumption prediction to ensure that the data-driven approach does not deviate too much. Road condition information may also include slope information. The vehicle's static parameters include wind resistance, rolling resistance, acceleration resistance, and slope resistance. The vehicle's static parameters may also include factors affecting energy consumption such as vehicle inherent parameters that affect energy consumption, such as driving speed, curb weight, and frontal area.

[0119] For example, the energy consumption prediction algorithm for automobiles is based on F. t =F f +F w +F i +F j It is concluded that, among them, F f For rolling resistance, F f = mgf, where m is the total mass of the vehicle, in kilograms; g is the acceleration due to gravity, which is 9.8 m / s²; f is the rolling resistance coefficient; F w For air resistance, C D The air resistance coefficient is represented by A, which represents the frontal area, measured in square meters. a For vehicle speed, the unit of vehicle speed is kilometers per hour; F i For slope resistance, F i =mgsinα, where α is the slope angle; F j To increase resistance, F j =σma, where σ is the vehicle rotational mass conversion factor; a is the vehicle acceleration, with the unit of acceleration being m / s². 2In addition to considering vehicle parameters, load variations are also taken into account, with the load estimated based on vehicle acceleration and throttle torque. If the vehicle is equipped with an inertial measurement unit (IMU), the acceleration is derived from the IMU; if the vehicle does not have an IMU, the acceleration is estimated using changes in vehicle speed.

[0120] 2. Input the road type, driving style and vehicle type information into the target energy consumption prediction model. The target energy consumption prediction model outputs the predicted total vehicle energy consumption of the preset travel route. The total vehicle energy consumption of the route is the reference demand energy consumption. For example, the target energy consumption prediction model is determined from multiple preset energy consumption prediction models based on at least one of the road type of the preset travel route or the user's driving style information.

[0121] In some embodiments, vehicle model information refers to vehicle parameters, and the target energy consumption prediction model is the data-driven part. In addition to considering driving style and driving conditions, the data-driven part also considers the vehicle's air conditioning usage, battery thermal management system and the power consumption of low-voltage accessories such as lights, instruments, fans, water pumps, multimedia audio and video, seat heating, and seat ventilation, the weather conditions ahead of the journey, such as temperature, humidity, and wind speed, the terrain conditions ahead of the journey, such as overpasses, slopes, air resistance, and track resistance, the distribution of refueling and charging addresses, and the charging conditions at the destination.

[0122] The data-driven approach first uses machine learning algorithms to derive an energy consumption prediction model through offline training. Then, it performs a data loop based on real-time running data to achieve online learning for prediction. When the model error continues to exceed a certain threshold, data is collected and uploaded to the cloud for model self-learning training to improve model accuracy. The model parameters are then updated to the vehicle-side offline model via cloud services, and the vehicle-side offline model runs in the vehicle's infotainment system.

[0123] In some embodiments, road types include: ordinary roads, expressways, highways, and congested roads.

[0124] In some embodiments, a user's driving style is categorized into aggressive, normal, and mild based on the rate of change of accelerator pedal opening and the rate of change of acceleration.

[0125] In some embodiments, please refer to Figure 6. Due to the significant differences in energy consumption among different vehicles and drivers, a two-dimensional clustering analysis is performed on all driving behavior data of a certain vehicle model according to driving style and driving conditions. For example, driving conditions are divided into ordinary roads, expressways, highways, and congested roads. Driving styles are divided into aggressive, normal, and mild based on the rate of change of accelerator pedal opening and the rate of change of acceleration. The two-dimensional cross-splitting results in 12 groups of driving data for this vehicle model. Based on the 12 groups of data, algorithms such as random forest are used to train energy consumption prediction models offline, resulting in 12 different parameter models representing energy consumption prediction models under different group classifications.

[0126] The obtained model is compressed and deployed in the vehicle controller. A driving style recognition algorithm is also deployed in the vehicle to dynamically identify the driver's driving style and driving conditions, and call the corresponding model to predict the energy consumption of the travel route.

[0127] Furthermore, after actual driving behavior occurs, by comparing the predicted energy consumption with the actual energy consumption, driving behavior data with errors exceeding a certain threshold is uploaded to the cloud, triggering cloud-based prediction model training, updating the corresponding energy consumption prediction model, and achieving closed-loop data learning.

[0128] 3. Based on the theoretical and reference energy consumption requirements of the vehicle along the preset travel route, the total vehicle energy consumption for the preset travel route is predicted. The theoretical energy consumption requirement is calculated using an energy consumption prediction algorithm based on vehicle theory, and the reference energy consumption requirement is output by the target energy consumption prediction model. The theoretical and reference energy consumption requirements are then weighted and summed to predict the total vehicle energy consumption for the preset travel route.

[0129] In some embodiments, a first weight of the vehicle's theoretical energy consumption requirement and a second weight of the reference energy consumption requirement are obtained; the vehicle's theoretical energy consumption requirement and reference energy consumption requirement are weighted according to the first weight and the second weight to predict the vehicle's total energy consumption for the route.

[0130] In some embodiments, the vehicle's total energy consumption along the route is predicted by weighting a first weight of the theoretical energy consumption demand and a second weight of the reference energy consumption demand. Since the theoretical energy consumption prediction algorithm for automobiles has calculation errors and the target energy consumption prediction model may be distorted, the two are combined. As the amount of data increases, the second weight of the reference energy consumption demand will become larger, while the first weight of the theoretical energy consumption demand will become smaller.

[0131] In some embodiments, the first weight of the theoretical energy demand and the second weight of the reference energy demand are added together to 1, and the first weight of the theoretical energy demand and the second weight of the reference energy demand are updated with the constraint that the actual vehicle energy consumption on the road segment is within a preset range, so as to obtain the updated first weight of the theoretical energy demand and the updated second weight of the reference energy demand; the first weight of the vehicle's theoretical energy demand and the second weight of the reference energy demand can be obtained by: obtaining the updated first weight of the vehicle's theoretical energy demand and the updated second weight of the reference energy demand.

[0132] In some embodiments, if the predicted total energy consumption of a vehicle on road segment n is different from the actual total energy consumption of a vehicle on road segment n, the actual total energy consumption of the vehicle on road segment n and the model identifier of the target energy consumption prediction model are sent to the server so that the server can optimize the energy consumption prediction model corresponding to the model identifier based on the actual total energy consumption of the vehicle on road segment n.

[0133] In some embodiments, the actual vehicle energy consumption on the nth road segment and the model identifier of the target energy consumption prediction model are sent to the server. The server retrains the energy consumption prediction model corresponding to the model identifier until the target energy consumption prediction model meets the preset conditions, and then updates the parameters of the energy consumption prediction model corresponding to the model identifier. If training is complete, the model is sent to the vehicle-side offline model; otherwise, the parameters of the energy consumption prediction model corresponding to the model identifier continue to be used.

[0134] In some embodiments, the vehicle's total energy consumption on road segment n is predicted in the following manner:

[0135] Obtain the first weight of the theoretical energy demand of the vehicle on the nth road segment and the second weight of the reference energy demand; n is a positive integer; perform a weighted calculation on the theoretical energy demand and reference energy demand of the vehicle on the nth road segment according to the first weight and the second weight, and predict the total energy consumption of the vehicle on the nth road segment.

[0136] In some embodiments, a first weight of the theoretical energy demand of the nth road segment and a second weight of the reference energy demand are obtained, for example, n is a positive integer. The theoretical energy demand of the nth road segment and the reference energy demand are weighted and calculated to predict the vehicle's total energy consumption in the nth road segment.

[0137] In some embodiments, after the vehicle passes through the nth road segment, the actual road segment energy consumption of the vehicle in the nth road segment is obtained; if the actual road segment energy consumption is within a threshold range, the first weight and the second weight remain unchanged, and the threshold range is determined based on the predicted road segment energy consumption of the vehicle in the nth road segment.

[0138] In some embodiments, the theoretical energy consumption and the reference energy consumption are weighted and calculated using the current weights to obtain the predicted vehicle energy consumption for the road segment. After the road segment is completed, the actual vehicle energy consumption for the road segment is obtained and compared with the predicted value. If the difference is within the threshold range, the first weight and the second weight remain unchanged.

[0139] In some embodiments, the theoretical energy consumption and reference energy consumption of the vehicle on the nth road segment are obtained; the theoretical energy consumption and reference energy consumption on the nth road segment are weighted according to the first initial weight of the theoretical energy consumption and the first initial weight of the reference energy consumption to obtain the first reference road segment vehicle energy consumption of the nth road segment; after the vehicle passes through the nth road segment, the actual road segment vehicle energy consumption of the vehicle on the nth driving road segment is obtained; if the actual road segment vehicle energy consumption is greater than the first reference road segment vehicle energy consumption, the target energy consumption prediction model is optimized.

[0140] In some embodiments, the theoretical energy demand and reference energy demand of a road segment are weighted according to their respective weights to obtain the total vehicle energy consumption of a first reference road segment. After the vehicle completes its journey on the road segment, the actual total vehicle energy consumption is compared with the total vehicle energy consumption of the first reference road segment. If the actual total vehicle energy consumption of the road segment is greater than the total vehicle energy consumption of the first reference road segment, the target energy consumption prediction model is optimized to improve accuracy.

[0141] In some embodiments, the theoretical energy consumption and reference energy consumption of the vehicle in the nth road segment are obtained; the theoretical energy consumption and reference energy consumption in the nth road segment are weighted according to the second initial weight of the theoretical energy consumption and the second initial weight of the reference energy consumption to obtain the second reference road segment vehicle energy consumption in the nth road segment; after the vehicle passes through the nth road segment, the actual road segment vehicle energy consumption in the nth road segment is obtained; if the actual road segment vehicle energy consumption is less than the second reference road segment vehicle energy consumption, the target energy consumption prediction model is optimized.

[0142] In some embodiments, the theoretical energy demand and reference energy demand of a road segment are weighted according to their respective weights to obtain the total vehicle energy consumption of a second reference road segment. After the vehicle completes its journey on the road segment, the actual total vehicle energy consumption of the road segment and the total vehicle energy consumption of the second reference road segment are compared. If the actual total vehicle energy consumption of the road segment is less than the total vehicle energy consumption of the second reference road segment, the target energy consumption prediction model is optimized to improve accuracy.

[0143] In some embodiments, obtaining the first weight of the theoretical energy consumption required by the vehicle in the nth road segment and the second weight of the reference energy consumption required by the vehicle can be achieved by: obtaining the theoretical energy consumption required by the vehicle in the nth road segment of a preset travel route and the reference energy consumption required by the vehicle; performing a weighted calculation on the theoretical energy consumption required by the vehicle in the nth road segment according to the first initial weight of the theoretical energy consumption required by the vehicle and the first initial weight of the reference energy consumption required by the vehicle to obtain the first reference road segment total vehicle energy consumption of the nth road segment; performing a weighted calculation on the theoretical energy consumption required by the vehicle in the nth road segment according to the second initial weight of the theoretical energy consumption required by the vehicle and the second initial weight of the reference energy consumption required by the vehicle to obtain the second reference road segment total vehicle energy consumption of the nth road segment; after the vehicle has traveled the nth road segment, obtaining the actual road segment total vehicle energy consumption of the vehicle in the nth road segment; if the actual road segment total vehicle energy consumption is greater than the second reference road segment total vehicle energy consumption and less than the first reference road segment total vehicle energy consumption, then updating the first weight and the second weight, and using the updated first weight as the current first weight of the theoretical energy consumption required by the vehicle, and using the updated second weight as the current second weight of the reference energy consumption required by the vehicle.

[0144] In some embodiments, please refer to Figure 6, the preset distance L is 5km, the first weight is ω1, the second weight is ω2, and the theoretical energy consumption E is... 理论 The initial weight is 0.2, referencing the energy demand E. 模型 The first initial weight is 0.8, the second initial weight of theoretical energy demand is 0.8, the second initial weight of reference energy demand is 0.2, and the reference road segment's total vehicle energy consumption is E. 总 Including the vehicle energy consumption of the first reference road segment and the vehicle energy consumption of the second reference road segment, the energy consumption prediction method is fused by a weighted summation: E 总 =ω1E 理论 +ω2E 模型 , where ω1, ω2∈[0.2,0.8].

[0145] Every 5km, the energy consumption prediction model type is re-matched and the weights ω1 and ω2 of the energy consumption prediction method are adjusted. When the actual vehicle energy consumption on the road section is 0.8E... 理论 +0.2E 模型 <E 实际1 <0.2E 理论 +0.8E 模型 Then, ω1 and ω2 are readjusted. Further adjustments are made by solving for ω1 + ω2 = 1 and E. 实际1 =ω1E 理论 +ω2E 模型 And retain that weight.

[0146] When the actual vehicle energy consumption on the road section is E 实际1 >0.2E 理论 +0.8E模型 At that time, the system determines whether the current energy consumption prediction model is correctly matched based on driving conditions and driving style. If the match is incorrect, it re-matches with 12 energy consumption prediction models; if the match is correct, it sets the E5km range accordingly. 实际1 Upload the model to the cloud service or retrain it locally on the vehicle until the target energy consumption prediction model meets the preset conditions, and then update the parameters of the energy consumption prediction model.

[0147] If training is complete, the parameters are sent to the offline model on the vehicle; otherwise, the energy consumption prediction model parameters of this type continue to be used. 实际1 <0.8E 理论 +0.2E 模型 According to E 实际1 Rematch the energy consumption prediction model with other driving conditions and driving styles. Every 5km, rematch the energy consumption prediction model type and energy consumption prediction method weights according to the aforementioned driving conditions and driving styles. If the energy consumption prediction model type of the previous segment is met, use the weights ω1 and ω2 of the energy consumption prediction method of the previous segment to predict energy consumption, and continue the above weight adjustment method and model parameter update.

[0148] 4. Based on the energy consumption prediction algorithm of automotive theory, predict the total energy consumption of the preset travel route according to driving style, road traffic flow speed, vehicle static parameters and the target speed that minimizes the total energy consumption of the vehicle along the route.

[0149] In some embodiments, vehicle status information includes at least the vehicle's static parameters and the target speed that minimizes the overall vehicle energy consumption along the route; road condition information includes at least the road traffic flow speed; and user behavior information includes at least the user's driving style. Once speed planning is performed and the target speed, i.e. the energy-saving speed, is obtained, the overall vehicle energy consumption of the preset travel route can be predicted according to the energy consumption prediction algorithm based on automotive theory, based on the driving style, road traffic flow speed, vehicle static parameters, and the target speed that minimizes the overall vehicle energy consumption along the route. The overall vehicle energy consumption calculated in this way is also relatively accurate.

[0150] In some embodiments, when the intelligent driving function is activated and speed planning is enabled, an energy consumption prediction algorithm based on automotive theory is triggered to predict the total vehicle energy consumption of a preset travel route based on user behavior information, road traffic flow speed, vehicle static parameters, and the target speed that minimizes overall vehicle energy consumption. When the intelligent driving function is activated and the navigation-assisted driving function is activated, the same energy consumption prediction algorithm is triggered to predict the total vehicle energy consumption of a preset travel route based on user behavior information, road traffic flow speed, vehicle static parameters, and the target speed that minimizes overall vehicle energy consumption. When the intelligent driving function is activated, the navigation-assisted driving function is deactivated, the adaptive cruise control function is activated, there are no vehicles ahead, and the energy-saving driving guidance function is activated, the same energy consumption prediction algorithm is triggered to predict the total vehicle energy consumption of a preset travel route based on user behavior information, road traffic flow speed, vehicle static parameters, and the target speed that minimizes overall vehicle energy consumption.

[0151] In some embodiments, when the intelligent driving function is turned off and the energy-saving driving guidance function is turned on, an energy consumption prediction algorithm based on automotive theory is triggered to predict the total vehicle energy consumption of a preset travel route based on user behavior information, road traffic flow speed, vehicle static parameters, and the target speed for minimizing total vehicle energy consumption.

[0152] In some embodiments, the energy-saving driving guidance function refers to a function used to control and guide the vehicle to travel at a target speed that minimizes the overall vehicle energy consumption along the route.

[0153] For example, the target speed is determined as follows: Using the minimum overall vehicle energy consumption along the route as the objective function, a speed sequence is generated based on the road traffic flow speed along the preset travel route and the vehicle's current speed. The current speed is the vehicle's speed at the starting point of the preset travel route. The speed sequence is then modified based on constraints, including at least driving style, to obtain the modified speed sequence. The modified speed sequence is the target speed, which is the optimal energy-saving speed.

[0154] In some embodiments, the limiting conditions may also include one or more of the following: travel duration, traffic flow speed information, acceleration limit, deceleration limit, maximum permissible speed in the area, and traffic light information.

[0155] In some embodiments, the restrictions include one or more of the following: travel time, acceleration limit, deceleration limit, maximum permissible speed in the area, traffic light information, and driver's driving style.

[0156] For example, traffic light information includes: traffic light countdown, distance to the traffic light, maximum allowed speed of vehicles passing through the area (i.e., road segment speed limit), and traffic flow speed. This information can determine acceleration and deceleration restrictions. Restriction conditions may also include one or more of the following: road gradient, speed of the vehicle in front, and distance to the vehicle in front.

[0157] In some embodiments, acceleration and deceleration limits include physical acceleration and deceleration constraints due to the characteristics of the vehicle itself, and physical limits due to road conditions; or, road conditions include asphalt, mud, sand road types, and differences in weather and humidity environmental factors; or, based on the driver's historical driving behavior data, actual driving acceleration and deceleration habits at different vehicle speeds are used as limits to ensure the driver's driving comfort.

[0158] In some embodiments, the velocity sequence is modified by limiting conditions to obtain a modified velocity sequence, which is the target velocity, and the target velocity is the optimal energy-saving velocity.

[0159] In some embodiments, the target vehicle speed is determined as follows: a smooth speed sequence is determined based on the road traffic flow speed of the preset travel route, the current vehicle speed, and constraint information. The constraint conditions include at least driving style, and the current vehicle speed is the vehicle speed at the starting point of the preset travel route. The smooth speed sequence is input into the vehicle model as the initial speed solution. The vehicle model generates a speed sequence based on the initial speed solution with the objective function of minimizing the total vehicle energy consumption along the route.

[0160] In some embodiments, a vehicle model based on a state-space matrix is ​​constructed according to vehicle dynamics. Since the model has nonlinear terms, linearization is performed near the working point to obtain a multi-segment working model. For example, the selection of the working point is determined by the working torque range. The wheel-end required torque can be calculated by the vehicle bench test coefficient formula to determine the torque range required for normal vehicle operation. The corresponding torque range is divided by a preset region, for example, by a preset 5 regions.

[0161] For example, the control input of the vehicle model is the current acceleration, and the state variables include the vehicle's speed, traffic light information, distance between the current position and the destination, the speed and acceleration of the vehicle in front, and the relative distance to obstacles, i.e., the relative distance to the nearest vehicle in front. By constraining the control input and state variables [x1,x2,...,xn]≤[δ1max,-δ1min,...,δnmax,-δnmin], where xn represents the nth state variable mentioned above, and [δnmax,-δnmin] represents the upper and lower limits of the nth state variable, the model can fit various constraints of the real environment. The vehicle model uses the minimization of overall vehicle energy consumption as the objective function, and the speed sequence is generated by solving the objective function.

[0162] In some embodiments, a smooth speed sequence is determined based on the road traffic flow speed, current vehicle speed, and constraint information of a preset travel route, and the smooth speed sequence is input into the vehicle model as the initial speed solution. This can be achieved by: obtaining the average speed based on the road traffic flow speed, current vehicle speed, and constraint information of the preset travel route; smoothing the speed changes between adjacent road segments to obtain the smooth speed sequence; correcting the speed of road segments in different driving scenarios according to driving style, road traffic flow speed, and traffic light location information to locally correct the smooth speed sequence; and determining the initial optimization range of the vehicle model based on the locally corrected smooth speed sequence, and inputting the smooth speed sequence as the initial speed solution into the vehicle model.

[0163] In some embodiments, an average speed is obtained based on the road traffic flow speed, current vehicle speed, and constraint information of a preset travel route. The speed changes between adjacent road segments are smoothed to obtain a smooth speed sequence. The speed of road segments in different driving scenarios is corrected according to driving style, road traffic flow speed, and traffic light location information to locally correct the smooth speed sequence.

[0164] For example, the driving style is the same as the driving style in the energy consumption prediction model, divided into aggressive, normal, and mild. When the aggressive driving style is used, the traffic flow speed on all road segments is increased; when the normal driving style is used, the existing traffic flow speed on all road segments is maintained; when the mild driving style is used, the traffic flow speed on all road segments is decreased. The local correction for traffic light positions is as follows: at a certain distance from the traffic light, based on the driving style (aggressive, normal, mild), acceleration is achieved with accelerations of A1, A2, and A3 respectively, or deceleration is achieved with decelerations of B1, B2, and B3 respectively. Where A1>A2>A3, |B1|>|B2|>|B3|;

[0165] The speed requirements between adjacent road segments are smoothly connected using Bessel functions, and an acceleration sequence following a Poisson distribution is established to synthesize a future path speed sequence. This acceleration sequence is then used as an initial solution to solve the objective function.

[0166] The calculation control input sequence a = [a1, a2, ..., am] is obtained, where am represents the optimal acceleration value obtained by solving the model in the target time domain. The optimal energy-saving speed is obtained from the initial speed and the optimal acceleration sequence. During the vehicle's operation, the above process is repeatedly substituted to obtain the optimal energy-saving speed sequence in the target time domain.

[0167] In some embodiments, speed correction of road segments in different driving scenarios is performed based on driving style, road traffic flow speed, and traffic light location information to locally correct the smooth speed sequence. This can be achieved by: when the target vehicle speed cannot be maintained during long-term following, the vehicle's current acceleration, current speed, obstacle speed, and relative distance to the obstacle are input into the vehicle following model. The vehicle following model uses the minimum overall vehicle energy consumption along the path and the relative distance to the obstacle greater than a preset distance threshold as the objective function to generate a locally corrected smooth speed sequence.

[0168] In some embodiments, the speed sequence is determined by minimizing the relative change value of acceleration and the overall vehicle energy consumption in the dimension of driving comfort. The current acceleration of the vehicle, the current speed, traffic light information, the distance between the current position and the destination, the speed of the vehicle in front, and the relative distance to the obstacle are input into the vehicle following model. The speed sequence is obtained by solving the vehicle following model with the objective function of minimizing the overall vehicle energy consumption of the road segment and the relative change value of acceleration being less than a preset acceleration threshold.

[0169] In some embodiments, speed corrections are applied to road segments in different driving scenarios based on driving style, road traffic flow speed, and traffic light location information to locally correct the smooth speed sequence. This can be achieved by: when passing through a traffic light intersection, inputting the vehicle's current acceleration, current speed, traffic light information, obstacle speed, and relative distance to the obstacle into the intersection speed model; and using the intersection speed model as the objective function to generate a locally corrected smooth speed sequence with the goal of minimizing the overall vehicle energy consumption along the path and ensuring that the passage time through the traffic light intersection is less than the preset expected passage time.

[0170] In some embodiments, the vehicle's current acceleration, speed, traffic light information, obstacle speed, and relative distance to obstacles are input into an intersection speed model. The goal of this model is to minimize the overall vehicle energy consumption of the road segment while ensuring that the vehicle's travel time is lower than the preset expected travel time at the intersection, thereby generating a locally corrected smooth speed sequence. This adjustment takes into account vehicles ahead, pedestrians, and other possible obstacles to optimize vehicle driving efficiency and reduce energy consumption.

[0171] 5. For any candidate driving route, if the historical database contains the total vehicle energy consumption of any candidate driving route, then the total vehicle energy consumption of any candidate driving route in the historical database shall be used as the total vehicle energy consumption of the vehicle on any candidate driving route; the historical database stores the total vehicle energy consumption of at least one driving route in the historical time period.

[0172] In some embodiments, as shown in Figure 3, a data-driven road feature sample database is constructed by collecting users' historical road data. For example, the road feature sample database records historical road information. By comparing the current road type with the historical data features, if the current road type matches the historical data features, the corresponding historical route is directly extracted and output. For example, the historical data feature matching includes road types such as urban expressways and provincial highways, the road segment, and the Global Positioning System (GPS) coordinates. The current candidate route is matched and analyzed through the road feature sample database to determine whether there are overlapping road segments. If there are, the historical route is retained as a candidate route, and the time information, distance information, and other information of the historical route are obtained.

[0173] For example, if it does not exist, the time information and distance information of the path are calculated and stored in the road feature sample database.

[0174] In some embodiments, please refer to Figure 3. Information such as road segment information, traffic information, and vehicle information of different routes are extracted. For example, the information of each road segment includes road speed limit, distance length, and gradient. Traffic information includes traffic flow and vehicle speed information. The data are input into the energy consumption prediction model to obtain the corresponding future energy consumption prediction feedback. If the route passes through a highway, the route toll is calculated. The fuel price at that time is used to convert the fuel consumption into the energy cost. The route with the lowest energy consumption is selected as the output and displayed to the user.

[0175] Third, with the goal of minimizing fuel consumption along the preset travel route, the target SOC for each road segment is planned based on the vehicle energy consumption of each segment.

[0176] In some embodiments, with the goal of minimizing fuel consumption along a preset travel route, the target SOC for each road segment is planned based on the vehicle energy consumption of each road segment. This can be achieved by: with the goal of minimizing fuel consumption along a preset travel route, the target SOC for each road segment is planned based on the initial SOC of the power battery and the vehicle energy consumption of each road segment.

[0177] In some embodiments, the target SOC of each road segment is planned based on the initial SOC of the power battery and the vehicle energy consumption of each road segment. This can be achieved by: determining the predicted SOC change of the vehicle at the end of each road segment based on the initial SOC of the power battery and the vehicle energy consumption of each road segment; determining multiple SOC change paths based on the predicted SOC change, for example, each SOC change path includes a set of SOCs; determining the SOC change path that minimizes fuel consumption of the vehicle during the planned travel route from among the multiple SOC change paths as the target SOC change path; and determining the SOC included in the target SOC change path as the target SOC of each road segment.

[0178] In some embodiments, for each road segment, the initial SOC of the power battery (i.e., the state of charge of the battery at the beginning of the road segment) and the vehicle energy consumption of the road segment (i.e., the energy consumed by the vehicle during the driving of the road segment) are considered. Based on the energy consumption of each road segment, the change in the power battery charge during the driving process is predicted. By calculating the predicted SOC change, the change in the battery charge after driving each road segment is obtained. Multiple SOC change paths are determined based on the predicted SOC change, and each SOC change path represents a possible change in the power battery charge. After determining multiple SOC change paths, the SOC change path that can achieve the lowest fuel consumption when running the preset travel route is selected as the target SOC change path. After the target SOC change path is determined, the target SOC of each road segment is determined based on the SOC values ​​included in the path.

[0179] In some embodiments, the target SOC at the end of the first segment of the preset travel route is determined based on the vehicle's initial SOC and the predicted SOC change of the first segment of the preset travel route.

[0180] The target SOC at the end of the non-first segment of the preset travel route is determined based on the predicted SOC change of the non-first segment and the target SOC at the end of the previous segment of the non-first segment.

[0181] In some embodiments, the target SOC at the end of the first road segment is determined based on the vehicle's initial SOC on the preset travel route and the predicted SOC change in the first road segment. The predicted SOC change refers to the change in battery charge after the vehicle has traveled the first road segment. By combining the initial SOC and the predicted SOC change, the target SOC at the end of the first road segment can be calculated.

[0182] The target SOC at the end of a non-first road segment is determined by the predicted SOC change for that segment, i.e., the change in battery charge after the vehicle travels that segment, combined with the target SOC at the end of the previous segment. Since the vehicle's SOC state is a continuously changing process, the impact of the previous segment needs to be considered when considering the target SOC of the current segment. By combining the predicted SOC change and the target SOC of the previous segment, the target SOC at the end of a non-first road segment can be determined.

[0183] In some embodiments, the predicted SOC change includes a first predicted SOC change and a second predicted SOC change; the upper limit of the target SOC for the first segment of the preset travel path is determined based on the initial SOC and the first predicted SOC change of the first segment; the lower limit of the target SOC for the first segment is determined based on the initial SOC and the second predicted SOC change of the first segment; the upper limit of the target SOC for non-first segments of the preset travel path is determined based on the first predicted SOC change of the non-first segment and the upper limit of the target SOC of the segment preceding the non-first segment; the lower limit of the target SOC for non-first segments is determined based on the second predicted SOC change of the non-first segment and the lower limit of the target SOC of the segment preceding the non-first segment.

[0184] In some embodiments, the State of Charge (SOC) of a target segment in a preset travel path is determined based on a first predicted SOC range and a second predicted SOC range of the target segment. When the target segment is the first segment of the preset travel path, the first predicted SOC range is determined based on the vehicle's initial SOC on the preset travel path and the predicted SOC change of the target segment. When the target segment is not the first segment of the preset travel path, the first predicted SOC range is determined based on the upper and lower limits of the target SOC of the preceding segment and the predicted SOC change of the target segment. When the target segment is the last segment of the preset travel path, the second predicted SOC range is the final SOC of the power battery when the vehicle reaches the end of the preset travel path. When the target segment is not the last segment of the preset travel path, the second predicted SOC range is determined based on the upper and lower limits of the target SOC of the following segment and the predicted SOC change of the following segment.

[0185] In some embodiments, if the target segment is the first segment of a preset travel route, its first predicted SOC range is determined based on the vehicle's SOC at the start of the route and the predicted SOC change of that segment, ensuring that the current state of the power battery at the start of the trip and the predicted energy consumption of that segment are combined.

[0186] If the target road segment is not the first segment of the preset travel route, then the first predicted SOC range needs to be combined with the upper and lower limits of the target SOC of the previous road segment, as well as the predicted SOC change of the target road segment, to ensure that the impact of the previous route is considered when calculating the target SOC.

[0187] If the target segment is the last segment of the preset travel route, its second predicted SOC range will be the battery's final SOC when the vehicle reaches the end of the route, ensuring that the expected state of charge of the power battery at the end of the vehicle's journey is taken into account to determine the second predicted SOC range.

[0188] If the target road segment is not the last road segment of the preset travel route, the second predicted SOC range will be determined based on the upper and lower limits of the target SOC of the road segment following the target road segment, as well as the predicted SOC change of the road segment following the target road segment, ensuring that the expected impact of the subsequent route is taken into account when calculating the target SOC.

[0189] In some embodiments, the upper and lower limits of the target SOC of the target road segment are determined by the intersection of the first predicted SOC range and the second predicted SOC range of the target road segment.

[0190] In some embodiments, the predicted SOC change of the target road segment is determined based on the charging and discharging power range corresponding to the target road segment; the charging and discharging power range is obtained based on the vehicle energy consumption of the corresponding road segment, the noise, vibration and harshness (NVH) limit power of the vehicle's engine, and the maximum charging and discharging power of the power battery; the total vehicle energy consumption along the route is determined based on the road condition information of the corresponding road segment.

[0191] In some embodiments, such as when the target road segment is a highway, the vehicle's total energy consumption is 10 kWh per kilometer. On highways, NVH power limits are lower because the road surface is relatively flat, and engine noise and vibration are relatively low, assumed to be 5 kW. Assuming the vehicle's maximum charge / discharge power for the battery is 50 kW, the charge / discharge power range on highways is determined to be from 5 kW to 50 kW.

[0192] Highways are typically flat and have smooth traffic flow, so the predicted energy consumption on highways is relatively low, for example, the total energy consumption of the vehicle on the route is 8 kWh per kilometer. The predicted SOC change is calculated based on the charging / discharging power range and the total vehicle energy consumption on the route. Assuming the system's prediction algorithm determines that the battery's SOC changes by -0.1 for every kilometer traveled on the highway (meaning the battery SOC decreases by 0.1 for every kilometer traveled), then for the target highway route, the predicted SOC change is determined to be -0.1, i.e., the battery SOC decreases by 0.1 for every kilometer traveled.

[0193] In some embodiments, the endpoint SOC is determined based on the initial SOC of the vehicle's power battery at the starting point of a preset travel route.

[0194] In some embodiments, the final SOC is determined based on the initial SOC of the vehicle's battery at the starting point of a preset travel route. That is, the final SOC is obtained by subtracting the predicted change in SOC from the initial battery SOC.

[0195] In some embodiments, if the starting SOC is greater than or equal to the first preset threshold, the ending SOC is the second preset threshold; if the starting SOC is less than the first preset threshold, the ending SOC is the first preset threshold; and the second preset threshold is greater than the first preset threshold.

[0196] In some embodiments, for example, the first preset threshold is 30%, and the second preset threshold is 50%. When the initial SOC is 35%, the final SOC will be set to 50%. If the initial SOC is 25%, the final SOC will be set to 30%.

[0197] In some embodiments, the vehicle’s preset travel route is divided into at least one road segment.

[0198] Determine the target state of charge (SOC) of the vehicle when it is traveling on each road segment.

[0199] The vehicle's engine and motor are controlled based on the actual and target SOC of the vehicle's power battery.

[0200] For example, a road segment can correspond to a target SOC. Based on this, the target SOC is the battery SOC that the vehicle expects to achieve at the end of driving on a road segment. During driving, the vehicle can control the remaining battery charge by switching driving modes based on this target SOC. A road segment can also correspond to at least two target SOCs. Based on this, a road segment can be divided into multiple sections, each section corresponding to a target SOC. When driving within a section, the vehicle can control the remaining battery charge by switching driving modes based on the target SOC of that section.

[0201] In some embodiments, the SOC of a target road segment in a preset travel route is determined based on a first predicted SOC range and a second predicted SOC range of the target road segment.

[0202] When the target road segment is the first road segment of the preset travel route, the first predicted SOC range of the target road segment is determined based on the change in the vehicle's initial SOC on the preset travel route and the predicted SOC of the target road segment.

[0203] If the target road segment is not the first road segment of the preset travel route, the first predicted SOC range of the target road segment is determined based on the upper and lower limits of the target SOC of the previous road segment and the change in the predicted SOC of the target road segment.

[0204] When the target segment is the last segment of the preset travel route, the second predicted SOC range of the target segment is the end SOC of the power battery when the vehicle travels to the end of the preset travel route.

[0205] If the target road segment is not the last road segment of the preset travel route, the second predicted SOC range of the target road segment is determined based on the upper and lower limits of the target SOC of the next road segment and the predicted SOC change of the next road segment.

[0206] In some embodiments, the upper and lower limits of the target SOC of the target road segment are determined by the intersection of the first predicted SOC range and the second predicted SOC range of the target road segment.

[0207] In some embodiments, the target road segment in the preset travel route includes at least one sub-road segment, each sub-road segment corresponding to sub-traffic information, and the target road segment is determined based on the sub-traffic information of the at least one sub-road segment.

[0208] For example, the target road segment can be any segment in a preset travel route. For instance, each segment of the preset travel route includes at least one sub-segment, and each sub-segment corresponds to sub-traffic condition information. For example, the sub-traffic condition information describes the traffic conditions of the corresponding sub-segment. In some embodiments of this disclosure, each road segment is determined based on the sub-traffic condition information of at least one sub-segment included in that road segment.

[0209] For example, after determining the preset travel route, you can obtain the sub-segments belonging to the preset travel route and the corresponding sub-traffic information of each sub-segment from the map.

[0210] Then, based on the sub-road condition information of each sub-segment, several adjacent sub-segments can be selected and spliced ​​together to obtain a road segment.

[0211] For example, suppose the map outputs a preset travel route that includes sub-segment 1, sub-segment 2, sub-segment 3, sub-segment 4, sub-segment 5, sub-segment 6, sub-segment 7, sub-segment 8, sub-segment 9, and sub-segment 10.

[0212] Based on the road condition information of each sub-segment, sub-segment 1 is designated as segment 1. Adjacent sub-segments 2 and 3 are combined to form segment 2. Adjacent sub-segments 4, 5, and 6 are combined to form segment 3. Adjacent sub-segments 7, 8, 9, and 10 are combined to form segment 4. This divides the preset travel route into four segments. Since each sub-segment has its own road conditions, based on the road conditions of each sub-segment, the preset travel route can be divided into at least one segment, ensuring that each segment also has its own road conditions.

[0213] It should be noted that, since the number of sub-segments output by the map is often large, directly using these sub-segments as segments for the preset travel route would result in an excessive amount of computation required for subsequent calculations of mode switching condition information (such as target SOC), failing to meet real-time requirements. However, some embodiments of this disclosure merge each sub-segment according to road conditions, resulting in fewer segmented road sections and reducing the computational load for mode switching condition information (such as target SOC).

[0214] In some embodiments, the sub-traffic information includes at least one of the following: road type, road name, road traffic signs, road speed limit, congestion level, distance length, travel time, average speed, gradient, traffic light information, and weather information.

[0215] For example, road types can include ordinary roads, expressways, highways, and congested roads. Congestion levels can include high, medium, and low to reflect different degrees of road congestion. Travel time is the time required for a vehicle to travel from the beginning to the end of a sub-segment, which can be obtained through big data analysis of historical data from multiple vehicles traveling on the sub-segment. Average speed is the average speed of vehicles traveling on the sub-segment; for example, it could be the average speed of vehicles using the aforementioned vehicle control methods that have previously traveled on the sub-segment, or it could be the average speed of multiple vehicles traveling on the sub-segment.

[0216] For example, if vehicle 1 travels at an average speed of 10 m / s on the sub-road segment, vehicle 2 travels at an average speed of 11 m / s on the sub-road segment, and vehicle 3 travels at an average speed of 9 m / s on the sub-road segment, then based on the average speeds of vehicle 1, vehicle 2, and vehicle 3, the average speed of the sub-road segment can be determined to be (10 + 11 + 9) / 3 = 10 m / s.

[0217] In some embodiments, the sub-traffic information satisfies at least one of the following conditions as well as the target road segment: the sub-traffic information includes road type, and all sub-road segments included in the target road segment have the same road type; or, the sub-traffic information includes average vehicle speed, and the average vehicle speed of all sub-road segments included in the target road segment belongs to the same speed range.

[0218] For example, the statement that all sub-segments included in the target road segment have the same road type can be understood as: if a road segment includes two sub-segments, then the two sub-segments have the same road type. Similarly, the statement that the average speed of all sub-segments included in the target road segment falls within the same speed range can be understood as: if a road segment includes at least two sub-segments, then the average speed of the two sub-segments falls within the same speed range.

[0219] It is understandable that the two constraints—that all sub-segments included in the target road segment have the same road type and that the average vehicle speed of all sub-segments included in the target road segment belongs to the same speed range—can exist individually or simultaneously.

[0220] In some embodiments, determining a road segment includes: combining at least two adjacent sub-road segments of the same type as a pre-divided road segment; and, if the average speed of the sub-road segments adjacent to the pre-divided road segment and the average speed of the sub-road segments in the pre-divided road segment are within the same speed range, combining the pre-divided road segment and the adjacent sub-road segments as a road segment in a preset travel route.

[0221] In some embodiments of this disclosure, a vehicle or server may divide a preset travel route into at least one segment, and the vehicle obtains the segmentation result.

[0222] In the process of dividing road segments, the method to determine any road segment can be as follows: at least two adjacent sub-road segments of the same type are spliced ​​together to form a pre-divided road segment. If the average speed of one or more sub-road segments adjacent to the pre-divided road segment is within the same speed range as the average speed of the sub-road segments in the pre-divided road segment, then the adjacent one or more sub-road segments are spliced ​​together with the pre-divided road segment to obtain a road segment in the preset travel route.

[0223] For example, the preset travel route includes sub-segment 1, sub-segment 2, sub-segment 3, sub-segment 4 and sub-segment 5. For example, sub-segment 1, 2 and 3 have the same road type, sub-segment 4 and sub-segment 5 have the same road type, and sub-segment 3 and sub-segment 4 have different road types.

[0224] Based on the above method of determining road segments, sub-segments 1, 2, and 3 are first combined into a pre-divided road segment 1, and sub-segments 4 and 5 are combined into another pre-divided road segment 2. Assuming that the average vehicle speeds of sub-segments 1, 2, 3, and 4 are all within the speed range of 10 m / s to 15 m / s, then sub-segments 1, 2, 3, and 4 can be combined into one road segment, and sub-segment 5 can be used as another road segment.

[0225] In some embodiments, sub-road condition information includes the length of sub-road segments. After determining road segments based on the sub-road condition information of each sub-road segment, the length of each road segment must be greater than or equal to a preset distance threshold. By constraining the length of each road segment, it can be ensured that the number of road segments is not excessive, reducing the possibility of excessive computation.

[0226] For example, when the length of a sub-segment is greater than or equal to the aforementioned distance threshold, the sub-segment is defined as a road segment; when the length of a sub-segment is less than the distance threshold, the sub-segment is combined with its adjacent sub-segments to form a road segment.

[0227] For example, assuming a distance threshold of 1 kilometer, if a sub-segment is 1.5 kilometers long, it can stand alone as a segment. If a sub-segment is 0.8 kilometers long, it is combined with an adjacent sub-segment to form a single segment, ensuring the combined segment's length is greater than or equal to 1 kilometer. If the combined length is still less than 1 kilometer after combining an adjacent sub-segment, multiple adjacent sub-segments can be combined.

[0228] In some embodiments, the target road segment in the preset travel route is obtained based on the road intervals that are successfully matched with the road condition data of the preset road conditions. The road intervals are obtained from the preset travel route based on the road condition data of the preset travel route, and the road characteristic parameters of the road intervals are determined based on the historical driving parameters of the vehicle in the road intervals.

[0229] Once a user determines a preset travel route based on the map displayed on the terminal screen, the system can automatically retrieve road condition data for that route, such as speed limits, gradients, and other signal data. Then, it performs statistical analysis on the sub-road condition information and divides the preset travel route into several road sections based on the sub-road condition information. Furthermore, it uses big data analysis to obtain historical driving parameters such as speed and acceleration of vehicles passing through the road sections. Finally, it uses the vehicle speed and acceleration retrieved from the big data to calculate the corresponding road characteristic parameters such as average speed, average acceleration, speed standard deviation, and acceleration standard deviation using basic calculation formulas.

[0230] The road feature parameters corresponding to the vehicle are compared and identified with the road condition data corresponding to the preset road conditions that are stored in advance. The road condition data of the preset road conditions that match the road feature parameters of the road section are identified as the preset road conditions corresponding to the road condition data. In this way, the preset travel path of the vehicle is divided into several road segments.

[0231] It should be noted that the road segments determined based on the preset travel route can be obtained based on road planning. For example, assuming that the road condition data of the preset travel route includes speed limit data of 60 km / h and speed limit data of 80 km / h, the preset travel route can be divided into road segment a corresponding to speed limit data of 60 km / h and road segment b corresponding to speed limit data of 80 km / h.

[0232] Taking road characteristic parameters including average vehicle speed, average acceleration, speed standard deviation, and acceleration standard deviation as an example, firstly, historical driving parameters such as vehicle speed and acceleration are obtained based on big data analysis when vehicles travel through road section a. Then, the average vehicle speed, average acceleration, speed standard deviation, and acceleration standard deviation of vehicles traveling through road section a are calculated according to the formulas for calculating the mean and standard deviation. These are compared with pre-stored road condition data corresponding to preset road conditions. When the calculated road characteristic parameters are within the range of the preset road condition data, the road section is determined as a road segment. Similarly, the road segments corresponding to road section b can be obtained.

[0233] In some embodiments, the target road segment in the preset travel route is the output of a pre-trained neural network model, and the input of the neural network model includes traffic data of the preset travel route.

[0234] For example, a neural network model can be pre-trained so that after inputting traffic data for a preset travel route, the neural network model can output at least one segment of the preset travel route, forming a segment sequence.

[0235] In some embodiments, the traffic information of the target road segment is obtained based on the sub-traffic information of the sub-road segments included in the target road segment.

[0236] For example, road segment A includes three sub-segments: sub-segment 1, sub-segment 2, and sub-segment 3. Therefore, the traffic information for road segment A is determined together based on the traffic information of sub-segment 1, sub-segment 2, and sub-segment 3.

[0237] In some embodiments, the method for determining the target SOC of a target road segment in a preset travel route includes: determining the target SOC of the target road segment based on the target SOC of the preceding road segment and the traffic information of the target road segment; or, determining the target SOC of the target road segment based on the target SOC of the next road segment and the traffic information of the next road segment.

[0238] For example, assuming the target road segment is the third road segment, the target SOC of the third road segment can be determined based on the target SOC of the second road segment and the traffic information of the third road segment. Alternatively, the target SOC of the third road segment can be determined based on the target SOC of the fourth road segment and the traffic information of the fourth road segment.

[0239] In some embodiments, the step of determining the target SOC of a target road segment based on the target SOC of the preceding road segment and the road condition information of the target road segment includes: determining the SOC change of a vehicle traveling on the target road segment based on the road condition information of the target road segment; and determining the target SOC of the target road segment based on the target SOC of the preceding road segment and the SOC change of the target road segment.

[0240] For example, assuming the target road segment is the third road segment, the change in SOC of a vehicle traveling on the third road segment can be determined based on the road condition information of the third road segment. Based on the target SOC of the second road segment and the change in SOC of the third road segment, the target SOC of the third road segment can be determined.

[0241] In some embodiments, the step of determining the target SOC of a target road segment based on the target SOC of the next road segment and the road condition information of the next road segment includes: determining the SOC change of a vehicle traveling on the next road segment based on the road condition information of the next road segment; and determining the target SOC of the target road segment based on the target SOC of the next road segment and the SOC change of the next road segment.

[0242] For example, assuming the target road segment is the third road segment, based on the road condition information of the fourth road segment, the change in SOC (State of Charge) of a vehicle traveling in the fourth road segment can be determined. Based on the target SOC of the fourth road segment and the change in SOC of the fourth road segment, the target SOC of the third road segment can be determined.

[0243] In some embodiments, the method for determining the target SOC includes: obtaining the initial SOC of the vehicle's power battery on a preset travel route; and determining the target SOC of each road segment based on the initial SOC and road condition information of each road segment.

[0244] In some embodiments of this disclosure, when the vehicle is at the starting point of a preset travel route, the actual SOC of the power battery is the aforementioned initial SOC. Please refer to Figure 7, which is a schematic diagram of road segment division according to some embodiments of this disclosure. As shown in Figure 7, the preset travel route includes road segment 1, road segment 2, road segment 3, and road segment 4. The starting point of the preset travel route is the starting point A of road segment 1. That is to say, when the vehicle reaches point A, the actual SOC of the power battery is the initial SOC of the preset travel route. Road condition information is used to reflect the road conditions of the corresponding road segment. Based on the initial SOC and the road condition information of each road segment, the target SOC of each road segment can be determined. When the vehicle is traveling on a certain road segment, the vehicle utilizes the power battery's charge with the target SOC of that road segment in mind, so that when the vehicle completes the road segment, the remaining charge of the power battery is close to the target SOC. In this way, by managing the vehicle's power battery charge through the road conditions of each road segment, the vehicle's energy consumption can be effectively reduced.

[0245] In some embodiments, both sub-road condition information and road condition information include road type; the road type of the target road segment is the target road type among the road types of each sub-road segment included in the target road segment, for example, the sub-road segment corresponding to the target road type has the highest proportion among all the sub-road segments included in the target road segment.

[0246] For example, for a certain road segment, such as road segment A, we can count the road types of each sub-segment included in road segment A. For example, there are 3 sub-segments of road type 1 and 1 sub-segment of road type 2. The road type of the sub-segment with the highest number of sub-segments is determined as the target road type, and the target road type is used as the road type of road segment A.

[0247] In some embodiments, both sub-road condition information and road condition information include average vehicle speed and distance length. The average vehicle speed of the target road segment is calculated based on the average vehicle speed and distance length of each sub-road segment in the target road segment, and the distance length of the target road segment is the sum of the distance lengths of each sub-road segment in the target road segment.

[0248] For example, for a road segment, say segment A, the lengths of all its sub-segments can be added together to obtain the total length of segment A. For a specific sub-segment, the total length of that sub-segment can be divided by its average speed to obtain the travel time required for that sub-segment. The total travel time of all its sub-segments can be added together to obtain the total travel time of segment A. Finally, the total length of segment A can be divided by its total travel time to obtain the average speed of segment A.

[0249] In some embodiments, the step of determining the target SOC of each road segment based on the initial SOC and the road condition information of each road segment includes: predicting the route vehicle energy consumption of the preset travel route based on the road condition information and energy consumption impact information of the preset travel route; and determining the target SOC of the power battery of each road segment based on the initial SOC of the power battery of each road segment and the road segment vehicle energy consumption with the goal of minimizing the fuel consumption of the preset travel route.

[0250] In some embodiments, such as a preset travel route, the route includes intercity highways, urban roads, and a short stretch of rural road. The initial state of charge (SOC) of the power battery is 60%.

[0251] Based on road condition information and energy consumption impact information of the preset travel route, the predicted vehicle energy consumption is 480 kWh for some routes on highways, 240 kWh for some routes on urban roads, and 180 kWh for some routes on rural roads. Based on the initial SOC of the power battery for each road segment and the vehicle energy consumption for that segment, with the goal of minimizing fuel consumption along the preset travel route, the target SOC of the power battery for each road segment is determined.

[0252] For example, on highways: with the goal of minimizing fuel consumption, energy consumption needs to be reduced as much as possible. For example, the target could be to maintain a State of Charge (SOC) of over 50% to cope with possible emergencies. Urban roads have higher energy consumption but slower speeds, so SOC can be increased by recovering braking energy. The target could be to maintain a SOC of over 60%. Rural roads have lower energy consumption but may have more complex road conditions, requiring a certain SOC reserve. For example, the target could be to maintain a SOC of over 55%.

[0253] In some embodiments, the step of determining the target SOC of each road segment based on the road condition information and the destination SOC of each road segment includes: determining the SOC change of the vehicle traveling on each road segment based on the road condition information of each road segment; and determining the target SOC of each road segment based on the destination SOC and the SOC change of each road segment.

[0254] In some embodiments of this disclosure, since road condition information can reflect the road conditions of a road segment, the change in SOC of a vehicle traveling on that road segment can be predicted based on the road condition information of that segment. That is, the change in SOC of the power battery when the vehicle travels from the beginning to the end of the road segment. With the end-point SOC already determined, the target SOC for each road segment can be determined based on the end-point SOC and the change in SOC for each road segment.

[0255] In some embodiments, it is assumed that the preset travel route includes k road segments, where k is a positive integer; the destination SOC is taken as the target SOC of the kth road segment; the target SOC of the (i-1)th road segment is calculated based on the target SOC of the i-th road segment and the change in SOC of the i-th road segment, where i = 2, 3, 4, ..., k.

[0256] For example, assuming k=5, since the endpoint SOC is already determined based on the starting SOC, the endpoint SOC can be directly used as the target SOC of the 5th road segment. After the target SOC of the 5th road segment is determined, the target SOC of the 4th road segment can be calculated based on the target SOC of the 5th road segment and the change in SOC of the 5th road segment. Then, based on the target SOC of the 4th road segment and the change in SOC of the 4th road segment, the target SOC of the 3rd road segment can be calculated, and so on, the target SOCs of the 3rd, 2nd, and 1st road segments can be calculated.

[0257] For example, if the target SOC of the fourth road segment is 40% and the SOC change of the fourth road segment is 5%, then the target SOC of the third road segment = the target SOC of the fourth road segment - the SOC change of the fourth road segment = 40% - 5% = 35%.

[0258] In some embodiments, traffic information includes road type, congestion level, and distance. The SOC change of the target road segment in the preset travel route is determined based on the power consumption per unit distance of the target road segment and the distance length. The power consumption per unit distance of the target road segment is determined based on the road type and congestion level of the target road segment.

[0259] For example, energy consumption per unit distance can be determined using historical vehicle data, such as a vehicle previously traveling on road segment B, where the actual energy consumption per unit distance was 'a'. If the road type of road segment A in the preset travel route is the same as that of road segment B, and the congestion level of road segment A is also the same as that of road segment B, then the energy consumption per unit distance for road segment A can be determined as 'a'. Multiplying the energy consumption per unit distance by the distance of the road segment yields the change in SOC (State of Charge) for that road segment.

[0260] In some embodiments, the power consumption per unit distance of the target road segment is obtained by querying a preset table based on the road type and congestion level of the target road segment.

[0261] For example, a pre-defined table stores the correspondence between road type, congestion level, and power consumption per unit distance. Based on this correspondence, the corresponding power consumption per unit distance can be retrieved according to the road type and congestion level of a road segment.

[0262] In some embodiments, after a vehicle travels a road of a preset length, the power consumption per unit distance to be updated in the preset table is updated based on the actual power consumption per unit distance of the vehicle on the road of the preset length.

[0263] For example, after a vehicle travels a preset distance on a road, the actual power consumption of the vehicle on that road can be obtained; based on the actual power consumption and the preset distance, the actual power consumption per unit distance can be obtained; and the power consumption per unit distance in the preset table can be updated based on the actual power consumption per unit distance.

[0264] For example, the preset distance length can be 1 kilometer. For every kilometer the vehicle travels, the actual power consumption of the vehicle on that 1-kilometer road can be obtained, thus yielding the actual power consumption per unit distance. For instance, in a preset table, the power consumption per unit distance corresponding to the road type and congestion level of that 1-kilometer road is the power consumption per unit distance to be updated. In some embodiments of this disclosure, the power consumption per unit distance to be updated in the preset table can be updated based on the actual power consumption per unit distance.

[0265] In some embodiments, the power consumption per unit distance to be updated in the preset table is updated to the actual power consumption per unit distance.

[0266] Assuming that the power consumption per unit distance to be updated in the preset table corresponds to road type 1 and congestion level 1, after the update, the power consumption per unit distance corresponding to road type 1 and congestion level 1 in the preset table will be the actual power consumption per unit distance.

[0267] In some embodiments, the unit distance power consumption to be updated in the preset table is updated to the target unit distance power consumption, which is calculated based on the unit distance power consumption to be updated, the first weight corresponding to the unit distance power consumption to be updated, the actual unit distance power consumption, and the second weight corresponding to the actual unit distance power consumption.

[0268] For example, the sum of the first weight and the second weight equals 1. Multiply the unit distance energy consumption to be updated by the first weight to obtain the first product, and multiply the actual unit distance energy consumption by the second weight to obtain the second product. The sum of the first product and the second product is taken as the target unit distance energy consumption. Assuming the unit distance energy consumption to be updated in the preset table corresponds to road type 1 and congestion level 1, after the update, the unit distance energy consumption corresponding to road type 1 and congestion level 1 in the preset table will be the target unit distance energy consumption.

[0269] In some embodiments, traffic information includes road type, congestion level, and travel time; the SOC change of the target road segment in the preset travel route is determined based on the SOC change rate of the target road segment and the travel time, and the SOC change rate of the target road segment is determined based on the road type and congestion level of the target road segment.

[0270] For example, the SOC change rate can be derived from historical vehicle data, such as a vehicle's previous journey on road segment B, during which the actual SOC change rate was 'a'. If the road type of road segment A in the preset travel route is the same as that of road segment B, and the congestion level of road segment A is also the same as that of road segment B, then the SOC change rate of road segment A can be determined as 'a'. Multiplying the SOC change rate by the travel time required for the road segment yields the SOC change amount for that road segment.

[0271] In some embodiments, the step of determining the target SOC of each road segment based on the initial SOC and the road condition information of each road segment includes: determining the target SOC of the vehicle at the end of each road segment based on the initial SOC and the road condition information of each road segment; and determining the target SOC of each road segment based on the target SOC of the vehicle at the end of each road segment.

[0272] In some embodiments of this disclosure, road condition information can reflect the road conditions of a road segment. Based on the initial SOC and the road condition information of each road segment, the target SOC at the end of each road segment can be determined. That is, when the vehicle reaches the end of each road segment, the actual SOC of the power battery should fall within a certain range, which is determined jointly by the initial SOC and the road conditions. After determining the target SOC at the end of each road segment, a SOC can be selected from the target SOC at the end of each road segment as the target SOC for that road segment.

[0273] In some embodiments, multiple SOC change paths can be determined based on the target SOC. For example, each SOC change path includes a set of SOCs. The SOC change path that minimizes energy consumption of the vehicle when running on the preset travel route among the multiple SOC change paths is determined as the target SOC change path. The SOCs included in the target SOC change path are determined as the target SOCs of each road segment.

[0274] For example, if there are 5 road segments, and one SOC is randomly selected from the target SOC of each road segment, a set of SOCs can be obtained. That is, a set of SOCs includes 5 SOCs, and this set of SOCs is a SOC change path. After determining multiple SOC change paths from the target SOCs, a target SOC change path can be determined from them. This target SOC change path can minimize the energy consumption of vehicles when running on the preset travel route.

[0275] For example, a simulation model can be used to determine which of several SOC change paths will minimize the energy consumption of the vehicle when running on a preset travel route.

[0276] In some embodiments, the target SOC at the end of the first segment of the preset travel route is determined based on the initial SOC and the traffic information of the first segment. The target SOC at the end of a non-first segment of the preset travel route is determined based on the traffic information of the non-first segment and the target SOC at the end of the segment preceding the non-first segment.

[0277] For example, based on the initial SOC and the traffic information of the first segment of the preset travel route, the target SOC of the vehicle at the end of the first segment is determined; for each segment of the preset travel route other than the first segment, based on the traffic information of the segment and the target SOC at the end of the previous segment, the target SOC of the vehicle at the end of the segment is determined.

[0278] In other words, based on the second operating condition data of the vehicle in the first road segment, the battery consumption of the vehicle under this road segment is calculated. Based on the initial battery SOC of the vehicle in the first road segment, the battery SOC at the end of the first road segment is predicted, thus determining the range of battery SOC variation under the first road segment. Then, based on the range of battery SOC variation under the first road segment, the initial battery SOC for the second road segment is determined. Combining this with the predicted battery consumption under the second road segment, the battery SOC at the end of the second road segment is calculated, thus determining the range of battery SOC variation under the second road segment. This process is repeated, and based on the range of battery SOC variation under the second road segment, the initial battery SOC for the third road segment is determined, thereby determining the target battery SOC for each road segment in the preset travel route.

[0279] In some embodiments, the upper and lower limits of the target SOC of the first road segment are determined based on the initial SOC and the traffic information of the first road segment; the upper limit of the target SOC of non-first road segments is determined based on the traffic information of non-first road segments and the upper limit of the target SOC of the preceding non-first road segment; the lower limit of the target SOC of non-first road segments is determined based on the traffic information of non-first road segments and the lower limit of the target SOC of the preceding non-first road segment.

[0280] For example, based on the initial SOC and the road condition information of the first road segment, a first SOC is determined, which is the battery SOC when the vehicle operates in hybrid mode at the end of the first road segment; based on the initial SOC and the road condition information of the first road segment, a second SOC is determined, which is the battery SOC when the vehicle operates in pure electric mode at the end of the first road segment; using the first SOC as the upper limit and the second SOC as the lower limit, the target SOC of the vehicle at the end of the first road segment is obtained.

[0281] Based on the road condition information of the road segment and the upper limit of the target SOC of the previous road segment, the third SOC is determined. The third SOC is the battery SOC of the vehicle when it ends in hybrid mode on the road segment. Based on the road condition information of the corresponding road segment and the lower limit of the target SOC of the previous road segment, the fourth SOC is determined. The fourth SOC is the battery SOC of the vehicle when it ends in pure electric mode on the road segment. Using the third SOC as the upper limit and the fourth SOC as the lower limit, the target SOC of the vehicle at the end of the road segment is obtained.

[0282] Please refer to Figure 8, which is a schematic diagram of a predicted SOC according to some embodiments of the present disclosure. The road segment division shown in Figure 8 is an example, and the first road segment is road segment 1 corresponding to segment AB.

[0283] As shown in Figure 8, the initial SOC of the vehicle at point A is F. Assuming the vehicle is in hybrid mode (using fuel throughout the section from A to B on road segment 1 with the battery charging), the first SOC at point B is determined as G, which is the upper limit of the battery SOC for road segment 1. Assuming the vehicle is in pure electric mode (using electric power throughout the section from A to B on road segment 1 with the battery discharging), the second SOC at point B is determined as I, which is the lower limit of the battery SOC for road segment 1. Therefore, the battery SOC range for road segment 1 can be determined as [I, G]. Assuming the actual battery SOC F for road segment 1 is 70%, the upper limit of battery SOC G at point B is 75%, and the lower limit of battery SOC I is 65%, then the target battery SOC for road segment 1 is [65%, 75%].

[0284] Then, based on the second operating condition data corresponding to road segment 2 and the battery target SOC corresponding to the previous road segment, namely road segment 1, the battery target SOC of the vehicle under road segment 2 is determined.

[0285] First, the upper limit of the battery SOC of road segment 1 is G, which is taken as the initial battery SOC of road segment 2. The vehicle adopts hybrid mode, that is, the vehicle uses fuel in road segment 2 and the battery is in a charging state. Then, the third SOC of point C is determined to be J, that is, the upper limit of the battery SOC of road segment 2 is J.

[0286] Then, the lower limit of the battery SOC of road segment 1 is I as the initial battery SOC of road segment 2. Assuming the vehicle is in pure electric mode, that is, the vehicle is fully charged and the battery is in a discharging state in road segment 2 from point B to point C, the fourth SOC of point C is determined to be L, that is, the lower limit of the battery SOC of road segment 2 is L. Thus, the target battery SOC of road segment 2 is determined to be [L, J].

[0287] In some embodiments, the step of determining the target SOC of each road segment based on the initial SOC and the road condition information of each road segment includes: determining the final SOC of the power battery when the vehicle travels to the end of the preset travel route based on the initial SOC; determining the target SOC of the vehicle at the end of each road segment of the preset travel route based on the initial SOC, the final SOC and the road condition information of the preset travel route; and determining the target SOC of each road segment of the preset travel route based on the target SOC.

[0288] Considering battery characteristics, when the vehicle reaches the end of the preset travel route, the remaining charge of the power battery needs to be maintained within a certain range, such as 17%-25%.

[0289] Based on this, the final SOC of the power battery when the vehicle reaches the end of the preset travel route can be determined according to the initial SOC. Given the initial and final SOCs, the target SOC for each road segment can be determined based on the initial SOC, the final SOC, and the road condition information for each segment.

[0290] In some embodiments, the target SOC of a target segment in a preset travel route is determined based on a first target SOC and a second target SOC of the target segment; when the target segment is the first segment of the preset travel route, the first target SOC of the target segment is determined based on the starting SOC and the traffic information of the target segment; when the target segment is not the first segment of the preset travel route, the first target SOC of the target segment is determined based on the first target SOC of the preceding segment and the traffic information of the target segment; when the target segment is the last segment of the preset travel route, the second target SOC of the target segment is determined based on the ending SOC and the traffic information of the target segment; when the target segment is not the last segment of the preset travel route, the second target SOC of the target segment is determined based on the second target SOC of the following segment and the traffic information of the target segment.

[0291] In some embodiments of this disclosure, a first target SOC of the vehicle at the end of each road segment is determined based on the initial SOC and the road condition information of each road segment; a second target SOC of the vehicle at the beginning of each road segment is determined based on the final SOC and the road condition information of each road segment; and a target SOC is determined based on the first target SOC and the second target SOC.

[0292] Road condition information reflects the road conditions of a segment. Based on the initial SOC and the road condition information for each segment, the first target SOC can be determined when the vehicle reaches the end of each segment. In other words, when the vehicle reaches the end of each segment, the actual SOC of the power battery should be within a certain range, determined by both the initial SOC and road conditions. Based on the end SOC and the road condition information for each segment, the second target SOC can be determined when the vehicle reaches the beginning of each segment. That is, when the vehicle reaches the beginning of each segment, the actual SOC of the power battery should be within a certain range, determined by both the end SOC and road conditions.

[0293] It should be noted that since adjacent road segments are connected end-to-end, the end of one road segment is the beginning of the next. After determining the first target SOC at the end of each road segment and the second target SOC at the beginning of each road segment, the target SOC at the end of each road segment is determined based on the first and second target SOCs. Finally, one SOC can be selected from the target SOCs at the end of each road segment as the target SOC for that road segment. For example, the first target SOC at the end of the last road segment of the preset travel route can be the end SOC of the preset travel route, and the second target SOC at the beginning of the first road segment of the preset travel route can be the starting SOC of the preset travel route.

[0294] In some embodiments, the target SOC mentioned above is the intersection of the first target SOC and the second target SOC. For example, for any segment in a preset travel path except the last segment, the first target SOC at the end of the segment can be intersected with the target SOC at the beginning of the next segment to obtain the target SOC at the end of each segment. For example, the target SOC at the end of the last segment is the end SOC of the preset travel path. Please refer to Figure 9, which is another predicted SOC schematic diagram according to some embodiments of the present disclosure. As shown in Figure 9, the final target SOC is obtained. For example, the starting SOC of the preset travel path is F, and the ending SOC of the preset travel path is U. For example, in Figure 8, the first segment is segment 1 corresponding to segment AB, and the second segment is segment 2 corresponding to segment BC. Assuming that the first target SOC at the end of segment 1 is [65%, 75%], and the second target SOC at the beginning of segment 2 is [60%, 70%], then after taking the intersection, the variation range at the end of segment 1 is [65%, 70%].

[0295] In some embodiments, the first target SOC of the target road segment is determined based on the charging and discharging power range corresponding to the target road segment, the road condition information of the target road segment, and the initial SOC. The second target SOC of the target road segment is determined based on the charging and discharging power range corresponding to the target road segment, the road condition information of the target road segment, and the final SOC. The charging and discharging power range of the target road segment is obtained based on the vehicle energy consumption of the target road segment, the noise, vibration, and harshness (NVH) limiting power of the vehicle's engine, and the maximum charging and discharging power of the power battery. The vehicle energy consumption of the target road segment is determined based on the road condition information of the target road segment.

[0296] The vehicle's total energy consumption for the road segment is determined based on road condition information. The first target SOC at the end of the road segment is determined based on road condition information, initial SOC, total energy consumption for the road segment, the vehicle's engine NVH limit power, and the maximum charge / discharge power of the battery. For example, the NVH limit power is a power threshold value that limits the engine's power to meet certain NVH performance requirements.

[0297] In some embodiments of this disclosure, for any road segment A, the vehicle's total energy consumption while traveling on road segment A can be predicted based on the road condition information of road segment A. Considering that road condition information, initial SOC, total vehicle energy consumption on the road segment, the NVH limiting power of the vehicle's engine, and the maximum charging and discharging power of the power battery all affect the charging and discharging power of the power battery, the first target SOC of the vehicle at the end of road segment A can be determined by using road condition information, initial SOC, total vehicle energy consumption on the road segment, the NVH limiting power of the vehicle's engine, and the maximum charging and discharging power of the power battery.

[0298] The vehicle's total energy consumption during its journey on the road segment is determined based on road condition information. The second target SOC at the start of the journey is determined based on road condition information, destination SOC, total vehicle energy consumption, engine NVH limit power, and maximum charge / discharge power of the power battery.

[0299] For any road segment A, the vehicle's total energy consumption on road segment A can be predicted based on the road condition information. Considering that road condition information, destination SOC, total vehicle energy consumption on the road segment, the vehicle's engine NVH limit power, and the maximum charging and discharging power of the power battery all affect the charging and discharging power of the power battery, the second target SOC of the vehicle at the beginning of road segment A can be determined by using road condition information, destination SOC, total vehicle energy consumption on the road segment, the vehicle's engine NVH limit power, and the maximum charging and discharging power of the power battery.

[0300] Based on the vehicle energy consumption, NVH power limit, and maximum charging and discharging power of each road segment, the charging and discharging power range corresponding to each road segment is obtained; based on the initial SOC, road condition information, and charging and discharging power range, the first target SOC is determined.

[0301] In some embodiments of this disclosure, the charging and discharging power range corresponding to each road segment can be obtained based on the vehicle energy consumption, NVH power limit, and maximum charging and discharging power of the road segment. Based on the initial SOC, the upper limit of the charging and discharging power range of the first road segment, and the road condition information of the first road segment, the upper limit of the first target SOC at the end of the first road segment can be calculated. Based on the initial SOC, the lower limit of the charging and discharging power range of the first road segment, and the road condition information of the first road segment, the lower limit of the first target SOC at the end of the first road segment can be calculated.

[0302] Furthermore, based on the upper limit of the first target SOC at the end of the first road segment, the upper limit of the charging and discharging power range of the second road segment, and the road condition information of the second road segment, the upper limit of the first target SOC at the end of the second road segment can be calculated; based on the lower limit of the first target SOC at the end of the first road segment, the lower limit of the charging and discharging power range of the second road segment, and the road condition information of the second road segment, the lower limit of the first target SOC at the end of the second road segment can be calculated, and so on, the first target SOC at the end of each road segment can be calculated.

[0303] Based on the vehicle energy consumption, NVH power limit, and maximum charging and discharging power of each road segment, the charging and discharging power range corresponding to each road segment is obtained; based on the destination SOC, road condition information, and charging and discharging power range, the second target SOC is determined.

[0304] In some embodiments of this disclosure, the charging and discharging power range corresponding to each road segment can be obtained based on the vehicle energy consumption, NVH power limit, and maximum charging and discharging power of the road segment. Based on the endpoint SOC, the upper limit of the charging and discharging power range of the last road segment, and the road condition information of the last road segment, the lower limit of the second target SOC at the beginning of the last road segment can be calculated. Based on the endpoint SOC, the lower limit of the charging and discharging power range of the last road segment, and the road condition information of the last road segment, the upper limit of the second target SOC at the beginning of the last road segment can be calculated. Furthermore, based on the upper limit of the second target SOC at the beginning of the last road segment, the lower limit of the charging and discharging power range of the second-to-last road segment, and the road condition information of the second-to-last road segment, the upper limit of the second target SOC at the beginning of the second-to-last road segment can be calculated; based on the lower limit of the second target SOC at the beginning of the last road segment, the upper limit of the charging and discharging power range of the second-to-last road segment, and the road condition information of the second-to-last road segment, the lower limit of the second target SOC at the beginning of the second-to-last road segment can be calculated, and so on, the second target SOC at the beginning of each road segment can be calculated.

[0305] In some embodiments, the vehicle energy consumption of the target road segment is obtained by inputting the road condition information of the target road segment and the user's driving style information into the target energy consumption prediction model and then outputting the target energy consumption prediction model. The target energy consumption prediction model is determined from multiple preset energy consumption prediction models based on the road condition information of the target road segment and the user's driving style information.

[0306] Based on road condition information and user driving style information, a target energy consumption prediction model is determined from multiple preset energy consumption prediction models. The road condition information and user driving style information are then input into the target energy consumption prediction model to obtain the vehicle energy consumption of the road segment output by the target energy consumption prediction model.

[0307] For example, road condition information includes road type, average vehicle speed, congestion level, gradient, altitude, traffic light information, and weather information. Based on the road type of road segment A and the driving style information of the vehicle's driver, a target energy consumption prediction model can be determined. Then, by inputting the road type, average vehicle speed, congestion level, gradient, altitude, traffic light information, weather information, and driving style information into the target energy consumption prediction model, the total vehicle energy consumption for road segment A can be obtained from the model's output.

[0308] For example, the road condition information, driving style information, vehicle status, and user vehicle settings can be input into the target energy consumption prediction model to obtain the vehicle energy consumption of the road segment output by the model, thereby improving the accuracy of the prediction results.

[0309] For example, vehicle conditions include vehicle weight, drag coefficient, rolling resistance coefficient, tire pressure, etc. Vehicle settings can include air conditioning settings.

[0310] In some embodiments, a SOC is selected from the target SOC corresponding to each road segment; and a SOC change path is obtained from multiple SOC change paths based on each SOC.

[0311] For example, continuing to refer to Figure 9, the initial SOC of point A is F. Assuming that point H is selected as the target SOC value within the battery target SOC [I,G], point K is selected as the target SOC value within the battery target SOC [L,J], point N is selected as the target SOC value within the battery target SOC [Q,M], and point T is selected as the target SOC value within the battery target SOC [X,R], then FHKNT is a SOC change path.

[0312] It should be noted that the more SOC values ​​there are for each road segment, the more battery SOC change paths are generated, the higher the accuracy of determining the battery SOC change path with the lowest energy consumption, and the better the energy management effect of the vehicle.

[0313] In some embodiments, determining the target SOC change path that minimizes energy consumption for vehicles traveling along a preset route from multiple SOC change paths can employ methods such as dynamic programming algorithms or the Pontryagin's minimum principle (PMP) algorithm. These algorithms use the target SOC of each road segment as the feasible region of the state variable.

[0314] For numerical calculations, the feasible region needs to be discretized, that is, the target SOC of each road segment needs to be discretized. For example, it can be discretized at equal intervals. If the difference between the maximum and minimum SOC values ​​of a certain segment is greater than 0.005, it can be discretized at intervals of 0.005; if the difference between the maximum and minimum SOC values ​​of a certain segment is less than 0.005, the SOC can be discretized in three equal intervals. The control variables are the operating mode and the engine operating point (torque, speed). For example, the operating modes include pure electric, series, and parallel.

[0315] To reduce computational requirements and accelerate the calculation process, the feasible region of the engine operating point can be simplified. In both series and parallel modes, the engine operating point adopts the control line calculated based on optimal system efficiency. Optimizing the engine operating point requires considering NVH constraints, which are simplified to constraints on engine speed only related to vehicle speed. Within the calculated feasible region, solving the optimization problem yields the target SOC change path with minimum energy consumption. The SOC included in this target SOC change path can be used as the target SOC for each segment of the preset travel path.

[0316] In some embodiments, if the starting SOC is greater than or equal to the first preset threshold, the ending SOC is the second preset threshold, which is greater than the first preset threshold; if the starting SOC is less than the first preset threshold, the ending SOC is the first preset threshold.

[0317] For example, the second preset threshold can be a pre-calibrated power-saving SOC of 25%, and the first preset threshold can be a pre-calibrated minimum allowable SOC of 17%.

[0318] It should be understood that the 25% and 17% mentioned here are only examples, and the values ​​of the power supply SOC and the minimum allowable SOC can be adjusted according to the actual situation. If the starting SOC of the preset travel route is greater than or equal to 17%, the ending SOC of the preset travel route is determined to be 25%; if the starting SOC of the preset travel route is less than 17%, the ending SOC of the preset travel route is determined to be 17%.

[0319] In some embodiments, the steps of controlling the vehicle's engine and motor based on the actual SOC and target SOC of the vehicle's power battery include: obtaining the actual SOC of the vehicle's power battery when the vehicle is traveling on a target road segment in a preset travel route, for example, the target road segment can be any road segment in the preset travel route; and controlling the vehicle to travel in pure electric mode or non-pure electric mode based on the actual SOC and the target SOC of the target road segment.

[0320] In some embodiments of this disclosure, when a vehicle travels on any segment of a preset travel route, such as segment A, segment A is designated as the target segment. During the vehicle's journey on the target segment, the actual State of Charge (SOC) of the power battery can be acquired in real time. This actual SOC is compared with the target SOC of the target segment, and the vehicle is controlled to switch to pure electric mode or non-pure electric mode based on the comparison result.

[0321] For example, a non-pure electric mode may include a hybrid mode (where both the internal combustion engine and the electric motor serve as the power source). Alternatively, a non-pure electric mode may include both a hybrid mode and a pure fuel mode. It should be understood that the hybrid mode is merely an example, and non-pure electric modes may also include other operating modes, which are not limited here.

[0322] In some embodiments, the step of controlling the vehicle to operate in pure electric mode or non-pure electric mode based on the actual SOC and the target SOC of the target road segment includes:

[0323] When the vehicle speed is greater than or equal to a preset speed threshold: when the difference between the actual SOC and the target SOC is greater than or equal to the preset difference, the vehicle is controlled to drive in pure electric mode; when the difference between the actual SOC and the target SOC is less than the preset difference, the vehicle is controlled to drive in hybrid mode.

[0324] In some embodiments of this disclosure, considering engine characteristics, the engine is not allowed to start when the vehicle speed is less than a speed threshold. Based on this, assuming a preset difference of 2%, when the vehicle speed is greater than or equal to the preset speed threshold:

[0325] (a) When the actual SOC minus the target SOC is ≥ 2%, the vehicle is switched to pure electric mode and the engine is shut down; (b) When the actual SOC minus the target SOC is ≤ 2%, the engine is started and the vehicle is switched to hybrid mode. For example, hybrid mode includes series mode and parallel mode. In some embodiments of this disclosure, when the vehicle is switched to hybrid mode, parallel mode operation is prioritized; if the vehicle does not meet the requirements for parallel mode operation, it operates in series mode.

[0326] In some embodiments, the step of controlling the vehicle to operate in pure electric mode or non-pure electric mode based on the actual SOC and the target SOC of the target road segment includes: controlling the vehicle to operate in pure electric mode when the vehicle speed is less than a speed threshold. For example, considering engine characteristics, the engine is not allowed to start when the vehicle speed is less than the speed threshold. Therefore, if the vehicle speed is less than the speed threshold, the vehicle is directly switched to pure electric mode.

[0327] In some embodiments, the vehicle speed threshold is positively correlated with the actual state of charge (SOC) of the power battery. That is, the higher the actual SOC of the power battery, the higher the corresponding vehicle speed threshold; conversely, the lower the actual SOC of the power battery, the lower the corresponding vehicle speed threshold. For example, this vehicle speed threshold can be obtained through experimental calibration.

[0328] In some embodiments, if the preset travel route includes only one road segment, the steps of controlling the vehicle's engine and motor based on the actual SOC and target SOC of the vehicle's power battery include: determining the vehicle's total energy consumption on the road segment based on road condition information; when the initial SOC is greater than the final SOC: if the SOC difference is greater than or equal to the total vehicle energy consumption, controlling the vehicle to operate in pure electric mode; if the SOC difference is less than the total vehicle energy consumption, first controlling the vehicle to operate in hybrid mode to maintain the actual SOC of the power battery as the initial SOC, and then controlling the vehicle to operate in pure electric mode. When the initial SOC is less than or equal to the final SOC, controlling the vehicle to operate in hybrid mode.

[0329] For example, the SOC difference is the difference between the initial SOC and the final SOC. If the SOC difference is greater than or equal to the vehicle's total energy consumption for the route, it means that the battery power alone can meet the user's energy needs, and therefore the vehicle can be controlled to operate in pure electric mode on the preset travel route. If the SOC difference is less than the vehicle's total energy consumption for the route, it means that the battery power alone cannot meet the user's energy needs, and therefore the vehicle can be controlled to first operate in hybrid mode on the preset travel route to maintain the actual SOC of the power battery as the initial SOC, and then control the vehicle to operate in pure electric mode.

[0330] In some embodiments, the steps of controlling the vehicle's engine and motor based on the actual SOC and target SOC of the vehicle's power battery include:

[0331] When the target SOC of the target road segment is less than the initial SOC of the target road segment: when the actual SOC of the power battery is greater than the minimum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to operate in pure electric mode on the target road segment; when the actual SOC of the power battery is equal to the minimum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to maintain the actual SOC of the power battery unchanged in hybrid mode.

[0332] When the target SOC of the target road segment is greater than the initial SOC of the target road segment: when the actual SOC of the power battery is less than the maximum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to operate in hybrid mode on the target road segment; when the actual SOC of the power battery is equal to the maximum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to maintain the actual SOC of the power battery unchanged in hybrid mode; when the actual SOC of the power battery is greater than the maximum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to operate in pure electric mode on the target road segment.

[0333] In some embodiments, the steps of controlling the vehicle's engine and motor based on the actual SOC and target SOC of the vehicle's power battery include: determining the category coefficient of the target road segment based on the road condition information of the target road segment in the preset travel route; determining the equivalent factor corresponding to the target road segment based on the target SOC and the category coefficient of the target road segment; determining the instantaneous output power of the vehicle's power battery at each moment of operation on the target road segment using the equivalent factor of the target road segment and the Equivalent Consumption Minimum Strategy (ECMS); and controlling the vehicle based on the instantaneous output power.

[0334] For example, the category coefficient indicates the road condition category of a road segment. For instance, if a road segment is classified as a highway with a medium congestion level, then the category coefficient for that segment is 1. That is, a category coefficient of 1 indicates that the road segment is a highway with a medium congestion level. For a road segment A, the equivalent factor corresponding to road segment A can be determined based on the target SOC of road segment A and its category coefficient. It should be noted that this equivalent factor is the equivalent factor in the ECMS (Equivalent Management System), and its explanation can be found in the ECMS documentation; it will not be elaborated upon here. Using the equivalent factor and the ECMS, the instantaneous output power of the vehicle's power battery at various times can be determined. Since each road segment has its own equivalent factor, when a vehicle is traveling on road segment A, the instantaneous output power of the power battery at various times during the time the vehicle is traveling on road segment A is determined based on the equivalent factor corresponding to road segment A and the ECMS.

[0335] In some embodiments, the equivalent factor corresponding to the target road segment is obtained by looking up a table based on the category coefficient of the target road segment and the target SOC.

[0336] For example, the target SOC corresponding to a target road segment can be obtained, such as any segment within a preset travel route. Based on the category coefficient and target SOC of the target road segment, the equivalent factor corresponding to the target road segment can be obtained by looking up a table. For example, the equivalent factor corresponding to the category coefficient and target SOC of the target road segment can be retrieved by looking up a table. For example, the table stores the correspondence between category coefficients, target SOC, and equivalent factors.

[0337] In some embodiments, the instantaneous output power of the power battery during operation on the target road segment is calculated according to the following formula:

[0338] Where H(u, SOC(t), t) is the Hamiltonian function obtained based on ECMS, and arg H(u, SOC(t), t) is the instantaneous output power of the power battery at time t. Let be the vehicle's engine fuel consumption rate, s(t) be the equivalent factor at time t, and SOC(t) be the SOC of the power battery at time t. Let SOC be the rate of change, and u be the fuel consumption.

[0339] For example, after obtaining the equivalence factor, the instantaneous battery output power of the hybrid vehicle corresponding to that equivalence factor can be obtained using ECMS, thereby controlling the hybrid vehicle based on the instantaneous output power of the power battery. Time t can be any time.

[0340] In some embodiments, the steps of controlling the vehicle based on the instantaneous output power include: obtaining the vehicle's required power at time t; determining the engine's instantaneous output power at time t based on the vehicle's required power, the power battery's instantaneous output power at time t, and the engine's NVH limiting power; and controlling the power battery and engine based on the power battery's instantaneous output power at time t and the engine's instantaneous output power at time t.

[0341] For example, the vehicle's power demand at time t can be determined based on the vehicle's speed and the depth to which the driver depresses the accelerator pedal. Based on the vehicle's power demand, the instantaneous output power of the battery at time t calculated using the aforementioned ECMS, and the engine's NVH limiting power, the engine's instantaneous output power at time t can be determined. Therefore, at time t, the vehicle can control the battery and engine based on their instantaneous output power at time t.

[0342] Therefore, output power can be obtained based on the equivalent factor optimized in real time, realizing optimal energy management of the entire road in the entire time domain, thereby reducing energy consumption.

[0343] In some embodiments, considering that some unexpected situations may occur when the vehicle is traveling on the preset travel route, the target SOC is re-determined if the difference between the actual SOC of the vehicle's power battery and the target SOC of the target road segment is greater than a set threshold when the vehicle is traveling on the target road segment; the target SOC is re-determined when the vehicle's position deviates from the preset travel route; and the target SOC is re-determined if the road conditions of the target road segment change when the vehicle is traveling on the target road segment.

[0344] For example, the target road segment can be any segment within a preset travel route. For instance, if the vehicle is currently operating on road segment A, and the difference between the actual State of Charge (SOC) of the vehicle's battery and the target SOC of road segment A exceeds a set threshold, the target SOC for road segment A and subsequent road segments is redefined. If the vehicle's position deviates from the preset travel route, the preset travel route changes, and a new preset travel route and its target SOC can be determined. If the road conditions on road segment A change, such as a sudden traffic jam, the target SOC for road segment A and subsequent road segments is redefined. This method can address potential unforeseen circumstances along the preset travel route, reducing vehicle energy consumption.

[0345] In some embodiments, the preset travel route includes a starting point and a destination. If the destination of the preset travel route has charging facilities, the destination SOC of the vehicle when it reaches the destination is reduced.

[0346] In some embodiments, the determination of whether a destination has charging facilities can be based on whether the destination displayed in the navigation is a charging station and the number of available charging piles. If the destination is a charging station and has available charging piles, it is determined that charging facilities are available; otherwise, they are not. Alternatively, the determination of charging facilities can be based on the historical charging behavior of the home, office, or favorite locations set in the navigation. If frequently used locations show a certain frequency of charging activity, they are determined to have charging facilities; otherwise, they are determined to not have charging facilities.

[0347] For example, one logic for judging historical charging behavior is that when a navigation end command is received, or the distance to the destination is <= 0.5km, or the destination type is home, company, or favorites, and the journey before plugging in is less than 2km and the journey time is less than 10min, and the plugging duration is greater than 5min, then it is judged that the destination has a charging station. Furthermore, if the number of fast charging times and the number of slow charging times are >= 3, then it is judged that the destination has charging conditions.

[0348] In some embodiments, when there are charging conditions at the destination, if a navigation end command is received, or the distance to the destination is <= 0.5km, or the destination type is home, company, or favorite point, and the SOC is <= balance point + 10, the device is powered off and plugged in. After the cumulative number of times the device is not plugged in with SOC >= 3, the number of fast charging times, slow charging times, and low SOC times are all reset to zero, and the destination is changed to a point where there are no charging conditions.

[0349] In some embodiments, the reduced endpoint SOC meets the vehicle's minimum permissible SOC.

[0350] In some embodiments, the minimum permissible SOC of a vehicle is the SOC required for the entire vehicle to operate.

[0351] In some embodiments, the destination of the preset travel route has charging conditions, specifically including: if there is a charging address at the destination and there is an idle charging pile at the charging address, then it is determined that the destination has charging conditions.

[0352] In some embodiments, the control device 50 is further configured to update the target SOC of the remaining road segment when the vehicle reaches the end of any road segment, based on the target SOC of the power battery in any road segment and the predicted vehicle energy consumption of the remaining road segment, with the goal of minimizing the fuel consumption of the preset travel route.

[0353] In some embodiments, after each road segment, such as 1km, the target SOC of the remaining road segment can be updated based on the target SOC of the power battery in any road segment and the vehicle energy consumption of the remaining road segment, with the goal of minimizing the fuel consumption of the preset travel route.

[0354] In some embodiments, the control device 50 is further configured to: if the road condition information is updated, re-divide the remaining travel path into road segments to obtain at least one new road segment; the remaining travel path refers to the path taken from the current location of the vehicle to the end point of the preset travel path; and update the target SOC of each new road segment based on the initial SOC of the power battery and the vehicle energy consumption of each new road segment with the goal of minimizing the fuel consumption of the preset travel path.

[0355] In some embodiments, if the congestion level in the received traffic information is updated, for example, from not congested to congested, the remaining travel path is re-divided into road segments to obtain at least one new road segment; the remaining travel path refers to the path taken from the current location of the vehicle to the end point of the preset travel path; the target SOC of each new road segment is updated based on the initial SOC of the power battery and the vehicle energy consumption of each new road segment, with the goal of minimizing the fuel consumption of the preset travel path.

[0356] In summary, the new energy vehicle energy intelligent management system control method according to some embodiments of this disclosure divides the vehicle's preset travel path into segments and determines the corresponding target SOC for each segment. This allows the vehicle to control the engine, drive motor, generator, and power battery based on the target SOC of the segment and the actual vehicle requirements when driving on each segment. This ensures that the engine operates in a high-efficiency range, reduces fuel consumption for users, and improves the driving experience.

[0357] Fourth, based on the target SOC of each road segment and the actual vehicle requirements, the engine 10, drive motor 20, generator 30 and power battery 40 are controlled so that the engine 10 operates in the high-efficiency range.

[0358] In some embodiments, the engine 10, drive motor 20, generator 30 and power battery 40 are controlled according to the target SOC of each road segment and the actual vehicle requirements, so that the speed and torque of the engine 10 work efficiently.

[0359] In some embodiments, the engine, drive motor, generator, and power battery are controlled according to the initial SOC, target SOC, and actual vehicle demand for each road segment, so that the engine operates in an efficient operating range. This can be achieved by: if the target SOC is greater than a certain threshold of the initial SOC and the actual vehicle demand is less than the initial SOC, controlling the engine to operate in an efficient and economical range, then controlling the engine to drive efficiently and generate electricity, storing excess electricity in the power battery; if the target SOC is greater than a certain threshold of the initial SOC and the actual vehicle demand is greater than or equal to the engine operating in an efficient and economical range, then controlling the engine to operate in an efficient operating range, supplying power to the power battery, and driving it via the drive motor or jointly with the engine; if the target SOC is less than a certain threshold of the initial SOC, then controlling the engine to shut down.

[0360] In some embodiments, if the target SOC is greater than a certain threshold of the initial SOC, and the actual vehicle demand is less than the vehicle demand that makes the engine operate in the efficient economic zone, the engine drives efficiently and generates electricity, storing the excess electricity in the power battery; this is hybrid mode driving. If the target SOC is greater than a certain threshold of the initial SOC, and the actual vehicle demand is greater than or equal to the vehicle demand that makes the engine operate in the efficient economic zone, the engine is controlled to operate in the efficient operating range and supplies power to the power battery, driven by the drive motor or jointly driven by the engine; this is hybrid mode driving. If the target SOC is less than a certain threshold of the initial SOC, the engine is controlled to stop; this is pure electric mode driving.

[0361] In some embodiments, the control device 50 is further configured to control the vehicle to travel on a preset path based on a target speed.

[0362] In some embodiments, the control device 50 is further configured to generate a prompt message based on the target speed for minimizing overall vehicle energy consumption. The prompt message is used to prompt the driver to control the vehicle's movement based on the target speed for minimizing overall vehicle energy consumption.

[0363] In some embodiments, the prompt information includes at least one of target vehicle speed or pedal control information.

[0364] In some embodiments, when the prompt information is the target vehicle speed, human-machine interaction can be conducted with the driver through instruments, pads, HUDs, etc. When the prompt information is pedal control information, the vehicle speed can be converted back into the form of accelerator pedal and brake pedal for human-machine interaction with the driver.

[0365] In some embodiments, the engine, drive motor, generator, and power battery are controlled based on the target SOC of each road segment, the actual vehicle requirements, and the reduced destination SOC when driving to the destination, so that the engine operates in its high-efficiency range.

[0366] In some embodiments, if the navigation system auto-start function is turned off, the navigation system is turned off, and the preset travel route is a commuter route, the control device is further configured to control the engine, drive motor, generator, and power battery based on the vehicle's historical driving data corresponding to the commuter route, so that the engine operates in a high-efficiency range.

[0367] In some embodiments, prediction is made by identifying patterns in historical driving data. For example, if a preset travel route is identified as a commuter route by identifying historical driving data, then the vehicle's driving conditions are considered commuter conditions, and these conditions are used as future travel conditions. If identification fails, the prediction fails.

[0368] For example, the driving data used for storage and prediction mainly includes speed, gradient, power demand, and other data related to vehicle energy consumption. Based on the target SOC for each road segment, actual vehicle demand, and commuting energy management strategies, the engine, drive motor, generator, and power battery are controlled to ensure that the engine operates within its efficient operating range.

[0369] In some embodiments, historical driving data includes a sequence of vehicle speeds as the vehicle travels a commuter route over a historical time period.

[0370] In some embodiments, historical driving data includes a sequence of vehicle speeds when the vehicle travels along a commuting route within a historical time period. Based on this speed sequence, the engine, drive motor, generator, and power battery are controlled according to the target SOC of each road segment, actual vehicle demand, and commuting energy management strategy, so that the engine operates in its efficient operating range.

[0371] In some embodiments, please refer to FIG10, which is a schematic diagram of energy management based on historical driving data according to some embodiments of the present disclosure.

[0372] For example, when identifying operating conditions, the average gradient and average vehicle speed per kilometer are used for identification, and the identification results and operating condition information are recorded. If the identified operating conditions are regular, operating condition prediction is performed. Based on the commuting history data of the most recent month, the future operating condition sequence is predicted, and SOC trajectory planning is performed. Based on the predicted operating condition sequence and combined with the vehicle status, the power consumption of the entire commuting route is planned. Based on the judgment of the terminal charging condition module, the terminal target SOC value is adjusted. Under the premise of meeting driving needs, the power distribution is adjusted so that the actual SOC follows the target SOC change, ultimately improving the overall vehicle economic performance on the commuting route. If the identified operating conditions are irregular, it is determined whether intelligent driving is activated. When intelligent driving is activated, the operating conditions in the short term are predicted based on perception information. When the driver releases the accelerator, intelligent driving identifies the distance and relative speed of the vehicle in front and performs zero-feedback coasting of the motor under the premise of ensuring a safe distance. When intelligent driving is not activated, prediction is performed by using a method based on historical data statistical transition probability matrix, or prediction is performed based on historical data through time-series prediction algorithm.

[0373] In some embodiments, if the navigation system auto-start function is disabled, the navigation system is turned off, and the preset travel route is not a commuting route, the control device 50 is further configured to: predict the vehicle speed within a preset time period while the vehicle is traveling on the preset travel route, and obtain the predicted vehicle speed within the preset time period; predict the component control sequence of the vehicle within the preset time period based on the predicted vehicle speed within the preset time period; and control the corresponding component according to the first control instruction in the component control sequence; the component includes at least one of the accelerator and the pedal.

[0374] In some embodiments, the component includes at least one of an accelerator and a pedal; the component control sequence includes at least one control instruction for the component; controlling the corresponding component means controlling the corresponding component to execute the first control instruction.

[0375] In some embodiments, for example, the preset time period is the next 5 to 10 seconds, the vehicle speed for the next 5 to 10 seconds is predicted using historical data or intelligent driving sensors, and the component control sequence of the vehicle within the preset time period is predicted based on the predicted vehicle speed within the preset time period. The components include at least one of the accelerator and pedal. The corresponding components are controlled according to the first control command in the component control sequence, and the optimization is carried out in sequence. Due to the optimization within the preset time period, the user's fuel consumption can be reduced.

[0376] In some embodiments, the preset time period refers to the time period elapsed since the last control of the corresponding component according to the control command. For example, after the vehicle has traveled at a predicted speed of 5 to 10 seconds, the vehicle speed for the next 5 to 10 seconds is predicted. For example, the first preset time period can be 5 to 10 seconds in the future.

[0377] In some embodiments, predicting the vehicle speed within a preset time period can be achieved by: obtaining historical driving data of the vehicle within a preset historical time period when the intelligent driving function is turned off; and predicting the vehicle speed within the preset time period based on the historical driving data.

[0378] In some embodiments, when the intelligent driving function is turned off, the vehicle speed for the next 5 to 10 seconds is predicted based on historical driving data to obtain the predicted vehicle speed for the next 5 to 10 seconds. The preset time period can be the next 5 to 10 seconds.

[0379] In some embodiments, historical driving data includes vehicle speed sequences; predicting the vehicle speed within a preset time period based on historical driving data to obtain the predicted vehicle speed within the preset time period can be achieved by: dividing the vehicle speed sequence to obtain at least one vehicle speed interval; obtaining the vehicle speed state transition probability from the target vehicle speed interval to the next vehicle speed interval corresponding to the target vehicle speed interval, thereby constructing a system state transition probability matrix; the target vehicle speed interval refers to any one of the at least one vehicle speed intervals, and the system state transition probability matrix includes at least one transition probability; and predicting the vehicle speed at each moment within the preset time period based on the system state transition probability matrix and the vehicle speed at the current moment to obtain the predicted vehicle speed within the preset time period.

[0380] In some embodiments, the vehicle speed sequence is divided to obtain at least one vehicle speed interval, and a system state transition probability matrix is ​​constructed. The system state transition probability matrix refers to the probability of the vehicle speed state corresponding to the target vehicle speed interval transitioning to the vehicle speed state corresponding to the next vehicle speed interval. Based on the system state transition probability matrix and the vehicle speed at the current moment, the vehicle speed at each moment within a preset time period is predicted to obtain the predicted vehicle speed within the preset time period.

[0381] In some embodiments, predicting the vehicle speed at each moment within a preset time period based on the system state transition probability matrix and the vehicle speed at the current moment, to obtain the predicted vehicle speed within the preset time period, includes: correcting the system state transition probability matrix based on vehicle speed limits and traffic flow speed limits; and predicting the vehicle speed at each moment within the preset time period based on the corrected system state transition probability matrix and the vehicle speed at the current moment, to obtain the predicted vehicle speed within the preset time period.

[0382] In some embodiments, the vehicle speed sequence is divided to obtain at least one vehicle speed interval, and a system state transition probability matrix is ​​constructed. The system state transition probability matrix refers to the probability of the vehicle speed state corresponding to the target vehicle speed interval transitioning to the vehicle speed state corresponding to the next vehicle speed interval. According to the vehicle's speed limit and traffic flow speed limit, the system state transition probability matrix is ​​corrected. Based on the corrected system state transition probability matrix and the vehicle's speed at the current moment, the vehicle speed at each moment within a preset time period is predicted to obtain the predicted vehicle speed within the preset time period.

[0383] For example, a rolling time window method is used to record the vehicle speed in a short time. The vehicle speed is recorded as Vt|p={vt-i:1≤i≤p}, where the value of p can be selected as 40 to meet the accuracy of short-time prediction.

[0384] By combining historical vehicle data and dividing vehicle speed into intervals, the probability pmij of the current vehicle speed interval changing to another vehicle speed interval at the next moment is calculated, and the system state transition probability matrix Pm=(pmij)n×n is constructed.

[0385] Considering the vehicle's own speed limit and the traffic flow speed limit obtained above, the system state transition probability matrix is ​​modified Pm. For example, the traffic flow speed is the average speed, which limits the maximum and minimum speed of the vehicle in the future. The vehicle's own speed limit affects the maximum acceleration or maximum deceleration of the vehicle response, limiting the speed change range between adjacent time moments.

[0386] Based on the system state transition probability matrix and the current vehicle speed, the speed range with the highest probability at the next moment is predicted. For example, if the current speed is 20 km / h, the speed range with the highest probability at the next moment is predicted to be between 20 km / h and 30 km / h. Using Vt|f=vt|t∏Pm(n), n=1,2,...,f, the future predicted speed at time f is calculated, obtaining the future predicted speed sequence, the acceleration of the vehicle in front, the speed of the vehicle in front, and the relative distance. For example, the acceleration of the vehicle in front is assumed to have a consistent trend within future moments. Substituting the above data into the vehicle's longitudinal kinematics model, it is calculated whether safe driving conditions are met: if the safety conditions are met, the predicted speed within a preset time period is output; if not, a safety warning is activated.

[0387] In some embodiments, the short-term predicted operating condition can be obtained based on the predicted vehicle speed by: predicting the vehicle's operating condition within a preset time period based on the corrected system state transition probability matrix and the predicted vehicle speed within a preset time period; the short-term predicted operating condition includes the predicted vehicle speed within the preset time period.

[0388] In some embodiments, the control device 50 is further configured to control the vehicle to brake when it is determined that the distance to or relative speed with the vehicle in front does not meet the conditions for safe driving.

[0389] In some embodiments, when the intelligent driving sensors detect that the distance to the vehicle in front and the relative speed do not meet the safe driving conditions, mechanical braking intervention control is activated to ensure the user's travel safety.

[0390] In some embodiments, if the intelligent driving function is enabled, the navigation-assisted driving function is disabled, the adaptive cruise control function is enabled, there are no vehicles ahead, and speed planning is not activated, the control device is also configured to control the vehicle to travel based on the current speed.

[0391] In some embodiments, if the intelligent driving function is enabled, the navigation-assisted driving function is disabled, the adaptive cruise control function is enabled, there is a vehicle ahead, and speed planning is not activated, the control device is further configured to: obtain the current speed of the vehicle ahead; and control the vehicle to drive based on the current speed of the vehicle ahead.

[0392] In some embodiments, the control device 50 is further configured to: when the intelligent driving function is activated, the navigation-assisted driving function is deactivated, the adaptive cruise control function is activated, there is a vehicle ahead, and speed planning is activated, trigger the execution of generating a speed sequence based on the road traffic flow speed of the preset travel route and the vehicle's current speed, with the objective function being the minimum overall vehicle energy consumption along the route; modifying the speed sequence based on constraints, the constraints including at least the driving style operation; acquiring the current speed of the vehicle ahead; determining the vehicle's control speed based on the current speed of the vehicle ahead and the vehicle's target speed; and controlling the vehicle to travel based on the control speed.

[0393] In some embodiments, if the current speed of the vehicle in front is greater than or equal to the target speed of the vehicle, then the controlled speed of the vehicle is the target speed; if the current speed of the vehicle in front is less than the target speed of the vehicle, then the controlled speed of the vehicle is the current speed of the vehicle in front.

[0394] In some embodiments, the control device 50 is further configured to, when the intelligent driving function is enabled, the navigation-assisted driving function is disabled, and the adaptive cruise control function is disabled, predict the vehicle speed within a preset time period based on the collected intelligent driving sensor data, obtain the predicted vehicle speed within the preset time period, and control the vehicle to drive based on the predicted vehicle speed.

[0395] In some embodiments, the preset time period is, for example, the next 5 to 10 seconds. Intelligent driving sensors such as lidar, millimeter-wave radar, and cameras are used to collect information about the current vehicle and its surrounding environment, calculate the predicted vehicle speed for the next 5 to 10 seconds, and control the vehicle to drive based on the predicted speed.

[0396] In some embodiments, the engine, drive motor, generator, and power battery are controlled according to the target SOC of each road segment, actual vehicle demand, traffic light information, and an energy management strategy based on navigation information fusion. This ensures that the engine operates within its efficient operating range. This can be achieved by: determining whether the vehicle has the capability to pass through the signalized intersection corresponding to the traffic light information based on the vehicle's current speed and traffic light information; if the vehicle does not have the capability to pass through the signalized intersection corresponding to the traffic light information, calculating the vehicle's drivable time; controlling the engine to operate efficiently or shut down, controlling the vehicle to travel at its current speed within the drivable time, and disengaging the mechanical brakes and activating a preset energy recovery level at the end of the drivable time.

[0397] In some embodiments, the driving time of a vehicle can be calculated by: calculating the vehicle's coasting distance; determining the vehicle's driving distance based on the coasting distance and the distance between the vehicle and the traffic light; and determining the vehicle's driving time based on the driving distance and the vehicle's current speed.

[0398] In some embodiments, the energy management strategy based on navigation information fusion also includes local correction of traffic light information fusion. Please refer to Figure 11, which is a schematic diagram of the local correction logic for traffic light information fusion according to some embodiments of this disclosure. Considering the local correction of traffic light information fusion, the phase and countdown of the preceding traffic light, as well as the vehicle speed and distance, are obtained in the navigation. The vehicle's ability to pass through the signalized intersection is determined based on the current vehicle speed, distance, and traffic light countdown. Then the vehicle can pass through the signalized intersection; if Then vehicles cannot pass through signalized intersections.

[0399] For situations where vehicles cannot pass through signalized intersections, calculate the coasting distance L in advance. 滑行 Thus, by L 行驶 =L 距离 -L 滑行 Calculate the distance L to maintain the current speed. 行驶 , where L 滑行 This indicates that at the current vehicle speed, there is no mechanical braking intervention, so the regenerative braking level is increased to 2, reducing the distance required to stop. The time t required to maintain the current vehicle speed was calculated. 行驶 When the vehicle meets L 行驶or t 行驶 When coasting, the vehicle will activate regenerative braking at level 2. If the vehicle is in Hybrid Electric Vehicle (HEV) mode, it will switch to Electric Vehicle (EV) mode. If the vehicle is in EV mode, it will remain in EV mode.

[0400] In some embodiments, the energy management strategy based on navigation information fusion also includes an automatic navigation method for commuting. When automatic navigation is enabled and the preset travel route is the commuting route, please refer to Figure 12. Figure 12 is a schematic diagram of the automatic navigation initial time update logic according to some embodiments of this disclosure.

[0401] For example, it consists of optimal commute time, commute time updates, commute route reminders, and commute destination recommendations. Its aim is to automatically activate navigation upon power-on and promptly adjust commute times and routes to meet diverse user-defined commuting needs and improve navigation efficiency. The automatic navigation method for commuting identifies the destination based on the user's preset commute cycle, start and end times, residential address, and home address, further classifying it as a commuting condition.

[0402] Commuting conditions are divided into commuting conditions and commuting conditions. When the vehicle starts, the onboard server first determines whether the initial position is met based on GPS, and then forms the commuting time period based on the preset commuting time offset by a certain time. By judging whether the current time is within the commuting cycle and commuting time period, it can identify whether it is a commuting condition, and thus automatically start navigation.

[0403] In some embodiments, the optimal commuting time is determined by recording the current commuting time and the duration of different commuting times through navigation. The current optimal commuting time is compared and recorded. After a certain update cycle, the optimal commuting time is recommended to the user through the UI.

[0404] In some embodiments, the commuting time period is updated by pre-setting a certain time offset between commuting and get off work hours to form the distribution and proportion of times when navigation is automatically turned on, times when navigation is manually turned on but not automatically turned on, and possible commuting times during the commuting time period. The interval offset correction amount for the current commuting time period is obtained. The commuting time period is updated based on the current time and the interval offset correction amount of the current commuting time period, after meeting a certain update cycle.

[0405] In some embodiments, commuting route reminders mainly involve storing and identifying historical navigation routes, such as automatic and manual commuting routes. The system analyzes the usage periods and durations of each navigation route, and, within a certain period, matches the optimal navigation route based on the current navigation time period, providing it to the user for selection.

[0406] In some embodiments, when a change in the commuting address causes automatic navigation to fail, and the commuting time period is met, the number of times the navigation destination is selected is incremented by 1. If the cumulative number of selections is >= 4, the user is reminded whether to change the navigation destination to the commuting destination, and the number of times the navigation destination is selected is cleared to 0.

[0407] When none of the above strategies are satisfied, the control device 50 is also configured to adjust the power-saving SOC based on the driver's style, the vehicle's current speed, or the vehicle's environmental information; and to control the engine, drive motor, generator, and power battery based on the comparison between the vehicle's actual SOC and the adjusted power-saving SOC, so that the engine operates in its high-efficiency range.

[0408] In some embodiments, the State of Charge (SOC) for maintaining electrical charge under different historical operating conditions is dynamically adjusted based on information such as driving style, vehicle speed, altitude, and low temperature to meet the vehicle's requirements. The operating mode, engine start-stop, and power distribution are dynamically adjusted by comparing the actual SOC with the SOC for maintaining electrical charge. The SOC for maintaining electrical charge can be the target SOC.

[0409] In some embodiments of this disclosure, the target SOC for each road segment is planned with the goal of minimizing fuel consumption along the travel route. Vehicle control is then implemented based on the target SOC for each road segment and the actual vehicle requirements, achieving a reasonable allocation of fuel and electricity in the hybrid electric vehicle, reducing fuel consumption and operating costs. Simultaneously, by controlling the engine, drive motor, generator, and power battery, the engine operates within its high-efficiency range, improving NVH performance, avoiding frequent engine start-stop cycles, and enhancing ride comfort. Furthermore, the route-based vehicle energy consumption of the preset travel route is predicted based on multi-domain fusion information, that is, by fusing cabin domain and power domain information for route-based vehicle energy consumption prediction, improving the accuracy of energy consumption prediction and further enhancing fuel efficiency.

[0410] Based on the above description, in some embodiments of this disclosure, the control device 50 can also perform preheating management.

[0411] In some embodiments, the control device 50 is further configured to adjust the temperature of the power battery based on the charging status, a preset travel route, and the user's scheduled pick-up time.

[0412] In some embodiments, the state of charging includes the current charge level and charging rate of the power battery. Different paths lead to different driving modes and battery usage, which in turn have different effects on battery heat generation. If the user expects to get into the vehicle in a short period of time, measures need to be taken to quickly regulate the battery temperature so that it can achieve optimal performance at the time of departure.

[0413] In some embodiments, a target temperature for the passenger compartment is generated based on the current passenger compartment temperature and the user's scheduled boarding time, and the passenger compartment temperature is controlled by the air conditioning system to reach the target temperature.

[0414] In some embodiments, engine coolant preheating is controlled when the target temperature of the crew compartment is greater than the current temperature of the crew compartment.

[0415] In some embodiments, if the travel is before departure, the target passenger compartment temperature is corrected based on the panel passenger compartment temperature, navigation information, outside temperature, charging status, and user boarding time to generate a target passenger compartment temperature deviation value. The deviation correction value is divided into cooling type and heating type. In some embodiments, when the air conditioning is in heating mode, the engine coolant is further used to preheat the passenger compartment. By optimizing the heating and cooling power to perform slow preheating and precooling in advance, energy loss caused by high current is reduced, thereby saving power consumption of high and low temperature air conditioning and accessories and reducing fuel consumption. At the same time, because preheating and precooling alleviate the lag problem between the target temperature of components and passenger compartment and the actual controlled temperature caused by heat capacity, the efficiency of components under high and low temperature environments and the comfort of passenger compartment are ensured.

[0416] In some embodiments, the control device 50 is further configured to adjust the temperature of the power battery based on the charging status, a preset travel route, and the user's scheduled pick-up time.

[0417] In some embodiments, the target battery temperature is adjusted before driving based on the charging status, the user's scheduled driving time, and the mileage information, thereby enabling battery thermal management in advance, improving battery efficiency during driving, and saving vehicle energy consumption.

[0418] In some embodiments, a target temperature deviation value is acquired during vehicle operation, the target temperature of the passenger compartment is corrected based on the target temperature deviation value, and the passenger compartment temperature of the vehicle is controlled to reach the corrected target temperature of the passenger compartment.

[0419] In some embodiments, the target temperature deviation value can be obtained during vehicle operation by:

[0420] During vehicle operation, temperature influencing factors are collected, including at least one of vehicle information and environmental information. Vehicle information includes at least one of window opening information, engine coolant temperature, navigation time, and target temperature deviation value. Environmental information includes at least one of weather information and outside temperature. The target temperature deviation value is determined based on the temperature deviation values ​​corresponding to the temperature influencing factors.

[0421] In some embodiments, there are multiple temperature deviation values ​​corresponding to temperature influencing factors; the target temperature deviation value is determined based on the temperature deviation values ​​corresponding to the temperature influencing factors by: obtaining the driving mileage of the preset travel route; if the driving mileage is greater than a third preset distance threshold, then the first temperature deviation value among the multiple temperature deviation values ​​is taken as the target temperature deviation value; the first temperature deviation value is less than the other temperature deviation values ​​among the multiple temperature deviation values ​​excluding the first temperature deviation value.

[0422] In some embodiments, the number of temperature deviation values ​​corresponding to temperature influencing factors is multiple;

[0423] The target temperature deviation value is determined based on the temperature deviation values ​​corresponding to the temperature influencing factors. This can be achieved by: obtaining the mileage of the preset travel route; if the mileage is less than or equal to a third preset distance threshold, and the target temperature of the passenger cabin is greater than the current passenger cabin temperature, then the first temperature deviation value among the multiple temperature deviation values ​​is taken as the target temperature deviation value; the first temperature deviation value is less than the other temperature deviation values ​​among the multiple temperature deviation values ​​excluding the first temperature deviation value.

[0424] In some embodiments, there are multiple temperature deviation values ​​corresponding to temperature influencing factors; the target temperature deviation value is determined based on the temperature deviation values ​​corresponding to the temperature influencing factors by: obtaining the mileage of the preset travel route; if the mileage is less than or equal to a third preset distance threshold, and the target temperature of the passenger cabin is less than the current passenger cabin temperature, then the second temperature deviation value among the multiple temperature deviation values ​​is taken as the target temperature deviation value; the second temperature deviation value is greater than the other temperature deviation values ​​among the multiple temperature deviation values ​​excluding the second temperature deviation value.

[0425] In some embodiments, a target temperature deviation value is generated during driving based on weather information, window opening information, engine coolant temperature, outside temperature, navigation time, and target temperature information in the passenger compartment on the dashboard, and the target temperature in the passenger compartment is corrected accordingly. When the driving mileage is greater than a third preset distance threshold, it is considered long-distance driving. To ensure better passenger compartment comfort, the deviation value with the smallest absolute value, i.e., the first temperature deviation value, is taken as the final target temperature deviation value for the passenger compartment. When the driving mileage is less than or equal to the third preset distance threshold, it is considered short-distance driving. While ensuring an acceptable passenger compartment temperature, the focus is on reducing vehicle energy consumption. In cooling mode, the deviation value with the largest absolute value, i.e., the second temperature deviation value, is taken as the final target temperature deviation value for the passenger compartment; in heating mode, the deviation value with the smallest absolute value, i.e., the first temperature deviation value, is taken as the final target temperature deviation value for the passenger compartment.

[0426] In some embodiments, the control device 50 is further configured to predict the output duration of the engine to output power, and to start the engine when the output duration exceeds a third preset duration.

[0427] In some embodiments, if there are road sections with long engine start times in the future, the engine is started in advance to preheat, thereby improving the engine's thermal efficiency during driving.

[0428] In some embodiments, the control device 50 is further configured to predict the vehicle's traffic jam time and increase the engine coolant temperature when the current time is a fourth preset time interval between the traffic jam and the traffic jam.

[0429] In some embodiments, the engine coolant temperature can be increased by reducing the engine water pump speed or reducing the engine fan speed.

[0430] In some embodiments, before a traffic jam, the target engine coolant temperature is increased, and the engine water pump and fan speeds are reduced to decrease energy consumption.

[0431] In some embodiments, adjusting the target battery temperature reduces battery thermal management energy consumption.

[0432] In some embodiments, the control device 50 is further configured to

[0433] Predict the destination of the preset travel route. When the distance between the vehicle's current position and the destination is less than the preset distance, pause the adjustment of the engine coolant temperature based on the target coolant temperature deviation, and increase the engine coolant temperature until the engine coolant temperature is higher than the preset temperature threshold before the vehicle reaches the destination.

[0434] In some embodiments, when the distance between the vehicle's current position and the destination is less than a preset distance, the destination is about to be reached. Raising the engine's target coolant temperature in advance can reduce the speed of the engine water pump and fan, thereby reducing energy consumption.

[0435] In some embodiments, the control device 50 is further configured to predict the endpoint of a preset path, and when the distance between the current position of the vehicle and the endpoint is less than a preset distance, to pause the adjustment of the power battery temperature based on the target temperature deviation of the power battery, and to adjust the power battery temperature until the temperature of the power battery is within the preset temperature range when the vehicle reaches the endpoint.

[0436] In some embodiments, before reaching the destination, the adjustment of the power battery temperature based on the target temperature deviation of the power battery is paused, and the power battery temperature is adjusted until the temperature of the power battery is within the preset temperature range when the vehicle reaches the destination, thereby saving energy consumption generated by maintaining the battery temperature.

[0437] In some embodiments, the control device 50 is further configured to predict the endpoint of a preset occurrence path, and when the distance between the current position of the vehicle and the endpoint is less than a preset distance, to pause the control of the vehicle's passenger compartment temperature to reach the passenger compartment target temperature, and to correct the passenger compartment target temperature.

[0438] In some embodiments, the target temperature of the crew cabin is adjusted before reaching the destination to reduce the energy consumption required to maintain the temperature of the crew cabin.

[0439] In some embodiments of this disclosure, preheating management is carried out before and during driving. Based on heating and cooling needs, heating and cooling power is optimized to perform slow preheating and precooling in advance, thereby reducing energy loss caused by high current, and thus saving power consumption of high and low temperature air conditioning and accessories and reducing fuel consumption.

[0440] Based on the above description, please refer to Figure 13. Figure 13 is a schematic flowchart of a control method for an intelligent energy management system for new energy vehicles according to some embodiments of the present disclosure. As shown in Figure 13, the control method for an intelligent energy management system for new energy vehicles includes, but is not limited to, steps S1301-S1304.

[0441] S1301, Obtain multi-domain fusion information, which includes at least cockpit domain information and power domain information. For example, cockpit domain information includes at least user behavior information and road condition information of the preset travel route, and power domain information includes at least vehicle status information.

[0442] For the specific steps of this solution, please refer to the specific steps of the control device 50 for acquiring multi-domain fusion information mentioned above. This solution will not repeat them here.

[0443] S1302, predict the vehicle energy consumption of the preset travel route based on multi-domain fusion information. The preset travel route includes multiple road segments, and the vehicle energy consumption of the route includes the vehicle energy consumption of the multiple road segments.

[0444] In some embodiments, if the navigation system auto-start function is enabled and the current system time is within a preset vehicle usage time period, the navigation system will be automatically turned on, and the preset travel route will be determined based on the vehicle's current location information.

[0445] In some embodiments, if the navigation system auto-start function is disabled, the preset travel route is determined in response to the user's input destination.

[0446] In some embodiments, the preset travel route includes multiple road segments, which are divided according to the road condition information of each road segment. The total vehicle energy consumption of the route includes the total vehicle energy consumption of multiple road segments, and the total vehicle energy consumption of each road segment is related to the road condition information of each road segment.

[0447] In some embodiments, the division of road segments is related to the traffic information of the preset travel route.

[0448] In some embodiments, each road segment is divided according to at least one of the road type and congestion level of a preset travel route.

[0449] In some embodiments, in response to the user's input destination, a preset travel route is determined. This can be achieved by determining at least one candidate energy-saving route based on the vehicle's origin and destination. The predicted total vehicle energy consumption of the at least one candidate energy-saving route is lower than that of other routes. The total vehicle energy consumption is predicted based on multi-domain fusion information of each route. In response to the selection operation of the at least one candidate energy-saving route, a preset travel route is determined. The preset travel route refers to the selected candidate energy-saving route. The preset travel route includes multiple road segments, and the total vehicle energy consumption includes the segment total vehicle energy consumption of the multiple road segments.

[0450] In some embodiments, at least one candidate energy-saving path is determined based on the vehicle's starting point and ending point: the starting point of any candidate driving path is the vehicle's starting point, and the ending point of any candidate driving path is the vehicle's ending point; the total energy consumption of the vehicle on each candidate driving path is predicted based on the multi-domain fusion information of each candidate driving path; at least one candidate energy-saving path is determined from the at least one candidate driving path based on the total energy consumption of the vehicle on each candidate driving path; the total energy consumption of the vehicle on any candidate energy-saving path is less than the total energy consumption of the vehicle on other candidate driving paths other than the at least one candidate energy-saving path.

[0451] In some embodiments, determining at least one candidate driving route based on the vehicle's origin and destination can be achieved by: obtaining at least one drivable route from the vehicle's origin to its destination; determining m drivable routes from the at least one drivable route based on a first travel dimension index of each drivable route; where m is a positive integer, and the first travel dimension index of any of the m drivable routes is less than the first travel dimension index of any of the other drivable routes in the at least one drivable route; and determining at least one candidate driving route from the m drivable routes based on a second travel dimension index of the m drivable routes; where the second travel dimension index of any candidate driving route is less than the second travel dimension index of any of the other drivable routes in the m drivable routes.

[0452] In some embodiments, the first travel dimension indicator includes travel distance, and the second travel dimension indicator includes travel time.

[0453] In some embodiments, determining at least one candidate driving path from m drivable paths based on the second travel dimension index of m drivable paths can be achieved by: determining the target drivable path with the smallest second travel dimension index among the m drivable paths; selecting drivable paths from the m drivable paths whose difference between the second travel dimension index and the second travel dimension index of the target drivable path is less than a preset index threshold; and using the selected drivable paths as at least one candidate driving path.

[0454] In some embodiments, determining at least one candidate driving route based on the vehicle's origin and destination can be achieved by: obtaining at least one drivable route from the vehicle's origin to its destination; obtaining travel dimension indicators for each drivable route, with the weight of each travel dimension indicator corresponding to the vehicle's current travel scenario; performing a weighted calculation on each travel dimension indicator according to each weight to obtain a comprehensive travel indicator for each drivable route; and selecting at least one candidate driving route from the at least one drivable route based on the comprehensive travel indicator for each drivable route; wherein the comprehensive travel indicator of the at least one candidate driving route is less than the comprehensive travel indicators of the other drivable routes in the at least one drivable route.

[0455] For example, when determining a preset travel route, it is also necessary to consider the vehicle's remaining driving range and the driving range to the destination. If the vehicle's remaining driving range is less than the driving range to the destination, then a refueling strategy is determined during the journey along the preset travel route. That is, when the driving range to the destination is greater than the vehicle's remaining driving range L based on predicted energy consumption... 剩余 At that time, determine the energy replenishment strategy during the journey along the preset travel route.

[0456] In some embodiments, determining a refueling strategy during a preset travel route can be achieved by: obtaining the driver's fatigue driving mileage; the fatigue driving mileage represents the mileage the driver can drive before reaching a fatigue driving state; and based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling.

[0457] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling can be achieved by: if the vehicle's remaining mileage is greater than or equal to the fatigue driving mileage, and the distance between the first charging address and the end point of the fatigue driving mileage is less than a first preset distance threshold, then controlling the vehicle to drive to the first charging address for charging; the distance between the first charging address and the end point of the fatigue driving mileage is less than the distance between other charging addresses and the end point of the fatigue driving mileage.

[0458] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling can be achieved by: if the vehicle's remaining mileage is greater than or equal to the fatigue driving mileage, and the distance between the first charging address and the end point of the fatigue driving mileage is greater than or equal to a first preset distance threshold, then controlling the vehicle to drive to a second charging address for charging; the distance between the first charging address and the end point of the fatigue driving mileage is less than the distance between other charging addresses and the end point of the fatigue driving mileage, and the second charging address represents the previous charging address of the first charging address in the preset travel path.

[0459] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling can be achieved by: if the vehicle's remaining mileage is less than the fatigue driving mileage, and the difference between the fatigue driving mileage and the remaining mileage is less than a second preset distance threshold, then controlling the vehicle to drive to a third charging address for charging; the third charging address is located before the end of the remaining mileage, and the distance between the third charging address and the end of the remaining mileage is less than the distance between other charging addresses and the end of the fatigue driving mileage, where other charging addresses represent the remaining charging addresses other than the third charging address located before the end of the remaining mileage.

[0460] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling can be achieved by: if the vehicle's remaining mileage is less than the fatigue driving mileage, and the difference between the fatigue driving mileage and the remaining mileage is greater than or equal to a second preset distance threshold, then controlling the vehicle to drive to a target refueling address for refueling; the target refueling address is located before the end of the remaining mileage, and the distance between the target refueling address and the end of the remaining mileage is less than the distance between other refueling addresses and the end of the fatigue driving mileage, where other refueling addresses represent the remaining refueling addresses other than the target refueling address among the refueling addresses located before the end of the remaining mileage.

[0461] Once the preset travel route is determined, the total vehicle energy consumption for that route is predicted based on multi-domain fusion information. Predicting the total vehicle energy consumption for the preset travel route can include any one of the following five methods.

[0462] 1. Based on the energy consumption prediction algorithm of automotive theory, predict the total energy consumption of the vehicle along the preset travel route according to the road traffic flow speed and the static parameters of the vehicle; correct the total energy consumption of the vehicle along the route according to user behavior information, and the corrected total energy consumption of the vehicle along the route is the theoretical energy consumption required.

[0463] In some embodiments, the static parameters of the vehicle include at least: wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

[0464] In some embodiments, the theoretical energy demand is calculated as follows: driving force * road traffic flow velocity * time, where driving force F t =F f +F w +F i +F j Among them, F t Used to represent driving force, F f F is used to represent rolling resistance. w F is used to represent air resistance. i F is used to represent slope resistance. j Used to represent acceleration resistance.

[0465] 2. Input the road type, driving style and vehicle type information into the target energy consumption prediction model. The target energy consumption prediction model outputs the predicted total vehicle energy consumption of the preset travel route. The total vehicle energy consumption of the route is the reference demand energy consumption. For example, the target energy consumption prediction model is determined from multiple preset energy consumption prediction models based on at least one of the road type of the preset travel route or the user's driving style information.

[0466] In some embodiments, road types include: ordinary roads, expressways, highways, and congested roads.

[0467] In some embodiments, a user's driving style is categorized into aggressive, normal, and mild based on the rate of change of accelerator pedal opening and the rate of change of acceleration.

[0468] 3. Based on the theoretical and reference energy consumption requirements of the vehicle along the preset travel route, the total vehicle energy consumption for the preset travel route is predicted. The theoretical energy consumption requirement is calculated using an energy consumption prediction algorithm based on vehicle theory, and the reference energy consumption requirement is output by the target energy consumption prediction model. The theoretical and reference energy consumption requirements are then weighted and summed to predict the total vehicle energy consumption for the preset travel route.

[0469] In some embodiments, a first weight of the vehicle's theoretical energy consumption requirement and a second weight of the reference energy consumption requirement are obtained; the vehicle's theoretical energy consumption requirement and reference energy consumption requirement are weighted according to the first weight and the second weight to predict the vehicle's total energy consumption for the route.

[0470] In some embodiments, the first weight of the theoretical energy demand and the second weight of the reference energy demand are added together to 1, and the first weight of the theoretical energy demand and the second weight of the reference energy demand are updated with the constraint that the actual vehicle energy consumption on the road segment is within a preset range, so as to obtain the updated first weight of the theoretical energy demand and the updated second weight of the reference energy demand; the first weight of the vehicle's theoretical energy demand and the second weight of the reference energy demand can be obtained by: obtaining the updated first weight of the vehicle's theoretical energy demand and the updated second weight of the reference energy demand.

[0471] In some embodiments, if the predicted total energy consumption of a vehicle on road segment n is different from the actual total energy consumption of a vehicle on road segment n, the actual total energy consumption of the vehicle on road segment n and the model identifier of the target energy consumption prediction model are sent to the server so that the server can optimize the energy consumption prediction model corresponding to the model identifier based on the actual total energy consumption of the vehicle on road segment n.

[0472] In some embodiments, the vehicle's total energy consumption on road segment n is predicted in the following manner:

[0473] Obtain the first weight of the theoretical energy demand of the vehicle on the nth road segment and the second weight of the reference energy demand; n is a positive integer; perform a weighted calculation on the theoretical energy demand and reference energy demand of the vehicle on the nth road segment according to the first weight and the second weight, and predict the total energy consumption of the vehicle on the nth road segment.

[0474] In some embodiments, after the vehicle passes through the nth road segment, the actual road segment energy consumption of the vehicle in the nth road segment is obtained; if the actual road segment energy consumption is within a threshold range, the first weight and the second weight remain unchanged, and the threshold range is determined based on the predicted road segment energy consumption of the vehicle in the nth road segment.

[0475] In some embodiments, the theoretical energy consumption and reference energy consumption of the vehicle on the nth road segment are obtained; the theoretical energy consumption and reference energy consumption on the nth road segment are weighted according to the first initial weight of the theoretical energy consumption and the first initial weight of the reference energy consumption to obtain the first reference road segment vehicle energy consumption of the nth road segment; after the vehicle passes through the nth road segment, the actual road segment vehicle energy consumption of the vehicle on the nth driving road segment is obtained; if the actual road segment vehicle energy consumption is greater than the first reference road segment vehicle energy consumption, the target energy consumption prediction model is optimized.

[0476] In some embodiments, the theoretical energy consumption and reference energy consumption of the vehicle in the nth road segment are obtained; the theoretical energy consumption and reference energy consumption in the nth road segment are weighted according to the second initial weight of the theoretical energy consumption and the second initial weight of the reference energy consumption to obtain the second reference road segment vehicle energy consumption in the nth road segment; after the vehicle passes through the nth road segment, the actual road segment vehicle energy consumption in the nth road segment is obtained; if the actual road segment vehicle energy consumption is less than the second reference road segment vehicle energy consumption, the target energy consumption prediction model is optimized.

[0477] In some embodiments, obtaining the first weight of the theoretical energy consumption required by the vehicle in the nth road segment and the second weight of the reference energy consumption required by the vehicle can be achieved by: obtaining the theoretical energy consumption required by the vehicle in the nth road segment of a preset travel route and the reference energy consumption required by the vehicle; performing a weighted calculation on the theoretical energy consumption required by the vehicle in the nth road segment according to the first initial weight of the theoretical energy consumption required by the vehicle and the first initial weight of the reference energy consumption required by the vehicle to obtain the first reference road segment total vehicle energy consumption of the nth road segment; performing a weighted calculation on the theoretical energy consumption required by the vehicle in the nth road segment according to the second initial weight of the theoretical energy consumption required by the vehicle and the second initial weight of the reference energy consumption required by the vehicle to obtain the second reference road segment total vehicle energy consumption of the nth road segment; after the vehicle has traveled the nth road segment, obtaining the actual road segment total vehicle energy consumption of the vehicle in the nth road segment; if the actual road segment total vehicle energy consumption is greater than the second reference road segment total vehicle energy consumption and less than the first reference road segment total vehicle energy consumption, then updating the first weight and the second weight, and using the updated first weight as the current first weight of the theoretical energy consumption required by the vehicle, and using the updated second weight as the current second weight of the reference energy consumption required by the vehicle.

[0478] 4. Based on the energy consumption prediction algorithm of automotive theory, predict the total energy consumption of the preset travel route according to driving style, road traffic flow speed, vehicle static parameters and the target speed that minimizes the total energy consumption of the vehicle along the route.

[0479] In some embodiments, when the intelligent driving function is activated and speed planning is enabled, an energy consumption prediction algorithm based on automotive theory is triggered to predict the total vehicle energy consumption of a preset travel route based on user behavior information, road traffic flow speed, vehicle static parameters, and the target speed that minimizes overall vehicle energy consumption. When the intelligent driving function is activated and the navigation-assisted driving function is activated, the same energy consumption prediction algorithm is triggered to predict the total vehicle energy consumption of a preset travel route based on user behavior information, road traffic flow speed, vehicle static parameters, and the target speed that minimizes overall vehicle energy consumption. When the intelligent driving function is activated, the navigation-assisted driving function is deactivated, the adaptive cruise control function is activated, there are no vehicles ahead, and the energy-saving driving guidance function is activated, the same energy consumption prediction algorithm is triggered to predict the total vehicle energy consumption of a preset travel route based on user behavior information, road traffic flow speed, vehicle static parameters, and the target speed that minimizes overall vehicle energy consumption.

[0480] In some embodiments, when the intelligent driving function is turned off and the energy-saving driving guidance function is turned on, an energy consumption prediction algorithm based on automotive theory is triggered to predict the total vehicle energy consumption of a preset travel route based on user behavior information, road traffic flow speed, vehicle static parameters, and the target speed for minimizing total vehicle energy consumption.

[0481] In some embodiments, the energy-saving driving guidance function refers to a function used to control and guide the vehicle to travel at a target speed that minimizes the overall vehicle energy consumption along the route.

[0482] For example, the target speed is determined as follows: Using the minimum overall vehicle energy consumption along the route as the objective function, a speed sequence is generated based on the road traffic flow speed along the preset travel route and the vehicle's current speed. The current speed is the vehicle's speed at the starting point of the preset travel route. The speed sequence is then modified based on constraints, including at least driving style, to obtain the modified speed sequence. The modified speed sequence is the target speed, which is the optimal energy-saving speed.

[0483] In some embodiments, the limiting conditions may also include one or more of the following: travel duration, traffic flow speed information, acceleration limit, deceleration limit, maximum permissible speed in the area, and traffic light information.

[0484] In some embodiments, acceleration and deceleration limits include physical acceleration and deceleration constraints due to the characteristics of the vehicle itself, and physical limits due to road conditions; or, road conditions include asphalt, mud, sand road types, and differences in weather and humidity environmental factors; or, based on the driver's historical driving behavior data, actual driving acceleration and deceleration habits at different vehicle speeds are used as limits to ensure the driver's driving comfort.

[0485] In some embodiments, the target vehicle speed is determined as follows: a smooth speed sequence is determined based on the road traffic flow speed of the preset travel route, the current vehicle speed, and constraint information. The constraint conditions include at least driving style, and the current vehicle speed is the vehicle speed at the starting point of the preset travel route. The smooth speed sequence is input into the vehicle model as the initial speed solution. The vehicle model generates a speed sequence based on the initial speed solution with the objective function of minimizing the total vehicle energy consumption along the route.

[0486] In some embodiments, a smooth speed sequence is determined based on the road traffic flow speed, current vehicle speed, and constraint information of a preset travel route, and the smooth speed sequence is input into the vehicle model as the initial speed solution. This can be achieved by: obtaining the average speed based on the road traffic flow speed, current vehicle speed, and constraint information of the preset travel route; smoothing the speed changes between adjacent road segments to obtain the smooth speed sequence; correcting the speed of road segments in different driving scenarios according to driving style, road traffic flow speed, and traffic light location information to locally correct the smooth speed sequence; and determining the initial optimization range of the vehicle model based on the locally corrected smooth speed sequence, and inputting the smooth speed sequence as the initial speed solution into the vehicle model.

[0487] In some embodiments, speed correction of road segments in different driving scenarios is performed based on driving style, road traffic flow speed, and traffic light location information to locally correct the smooth speed sequence. This can be achieved by: when the target vehicle speed cannot be maintained during long-term following, the vehicle's current acceleration, current speed, obstacle speed, and relative distance to the obstacle are input into the vehicle following model. The vehicle following model uses the minimum overall vehicle energy consumption along the path and the relative distance to the obstacle greater than a preset distance threshold as the objective function to generate a locally corrected smooth speed sequence.

[0488] In some embodiments, speed corrections are applied to road segments in different driving scenarios based on driving style, road traffic flow speed, and traffic light location information to locally correct the smooth speed sequence. This can be achieved by: when passing through a traffic light intersection, inputting the vehicle's current acceleration, current speed, traffic light information, obstacle speed, and relative distance to the obstacle into the intersection speed model; and using the intersection speed model as the objective function to generate a locally corrected smooth speed sequence with the goal of minimizing the overall vehicle energy consumption along the path and ensuring that the passage time through the traffic light intersection is less than the preset expected passage time.

[0489] 5. For any candidate driving route, if the historical database contains the total vehicle energy consumption of any candidate driving route, then the total vehicle energy consumption of any candidate driving route in the historical database shall be used as the total vehicle energy consumption of the vehicle on any candidate driving route; the historical database stores the total vehicle energy consumption of at least one driving route in the historical time period.

[0490] For the specific steps of this solution, please refer to the specific steps of the control device 50 predicting the vehicle energy consumption of the preset travel route based on multi-domain fusion information. This solution will not repeat them here.

[0491] S1303 aims to minimize fuel consumption along a pre-set travel route and plans the target SOC for each road segment based on the vehicle's overall energy consumption for each segment.

[0492] In some embodiments, with the goal of minimizing fuel consumption along a preset travel route, the target SOC for each road segment is planned based on the vehicle energy consumption of each road segment. This can be achieved by: with the goal of minimizing fuel consumption along a preset travel route, the target SOC for each road segment is planned based on the initial SOC of the power battery and the vehicle energy consumption of each road segment.

[0493] In some embodiments, the target SOC of each road segment is planned based on the initial SOC of the power battery and the vehicle energy consumption of each road segment. This can be achieved by: determining the predicted SOC change of the vehicle at the end of each road segment based on the initial SOC of the power battery and the vehicle energy consumption of each road segment; determining multiple SOC change paths based on the predicted SOC change, for example, each SOC change path includes a set of SOCs; determining the SOC change path that minimizes fuel consumption of the vehicle during the planned travel route from among the multiple SOC change paths as the target SOC change path; and determining the SOC included in the target SOC change path as the target SOC of each road segment.

[0494] In some embodiments, the target SOC at the end of the first segment of the preset travel route is determined based on the vehicle's initial SOC and the predicted SOC change of the first segment of the preset travel route.

[0495] The target SOC at the end of the non-first segment of the preset travel route is determined based on the predicted SOC change of the non-first segment and the target SOC at the end of the previous segment of the non-first segment.

[0496] In some embodiments, the predicted SOC change includes a first predicted SOC change and a second predicted SOC change; the upper limit of the target SOC for the first segment of the preset travel path is determined based on the initial SOC and the first predicted SOC change of the first segment; the lower limit of the target SOC for the first segment is determined based on the initial SOC and the second predicted SOC change of the first segment; the upper limit of the target SOC for non-first segments of the preset travel path is determined based on the first predicted SOC change of the non-first segment and the upper limit of the target SOC of the segment preceding the non-first segment; the lower limit of the target SOC for non-first segments is determined based on the second predicted SOC change of the non-first segment and the lower limit of the target SOC of the segment preceding the non-first segment.

[0497] In some embodiments, the State of Charge (SOC) of a target segment in a preset travel path is determined based on a first predicted SOC range and a second predicted SOC range of the target segment. When the target segment is the first segment of the preset travel path, the first predicted SOC range is determined based on the vehicle's initial SOC on the preset travel path and the predicted SOC change of the target segment. When the target segment is not the first segment of the preset travel path, the first predicted SOC range is determined based on the upper and lower limits of the target SOC of the preceding segment and the predicted SOC change of the target segment. When the target segment is the last segment of the preset travel path, the second predicted SOC range is the final SOC of the power battery when the vehicle reaches the end of the preset travel path. When the target segment is not the last segment of the preset travel path, the second predicted SOC range is determined based on the upper and lower limits of the target SOC of the following segment and the predicted SOC change of the following segment.

[0498] In some embodiments, the upper and lower limits of the target SOC of the target road segment are determined by the intersection of the first predicted SOC range and the second predicted SOC range of the target road segment.

[0499] In some embodiments, the predicted SOC change of the target road segment is determined based on the charging and discharging power range corresponding to the target road segment; the charging and discharging power range is obtained based on the vehicle energy consumption of the corresponding road segment, the noise, vibration and harshness (NVH) limit power of the vehicle's engine, and the maximum charging and discharging power of the power battery; the total vehicle energy consumption along the route is determined based on the road condition information of the corresponding road segment.

[0500] In some embodiments, the endpoint SOC is determined based on the initial SOC of the vehicle's power battery at the starting point of a preset travel route.

[0501] In some embodiments, if the starting SOC is greater than or equal to the first preset threshold, the ending SOC is the second preset threshold; if the starting SOC is less than the first preset threshold, the ending SOC is the first preset threshold; and the second preset threshold is greater than the first preset threshold.

[0502] In some embodiments, the vehicle’s preset travel route is divided into at least one road segment.

[0503] Determine the target state of charge (SOC) of the vehicle when it is traveling on each road segment.

[0504] The vehicle's engine and motor are controlled based on the actual and target SOC of the vehicle's power battery.

[0505] In some embodiments, the SOC of a target road segment in a preset travel route is determined based on a first predicted SOC range and a second predicted SOC range of the target road segment.

[0506] When the target road segment is the first road segment of the preset travel route, the first predicted SOC range of the target road segment is determined based on the change in the vehicle's initial SOC on the preset travel route and the predicted SOC of the target road segment.

[0507] If the target road segment is not the first road segment of the preset travel route, the first predicted SOC range of the target road segment is determined based on the upper and lower limits of the target SOC of the previous road segment and the change in the predicted SOC of the target road segment.

[0508] When the target segment is the last segment of the preset travel route, the second predicted SOC range of the target segment is the end SOC of the power battery when the vehicle travels to the end of the preset travel route.

[0509] If the target road segment is not the last road segment of the preset travel route, the second predicted SOC range of the target road segment is determined based on the upper and lower limits of the target SOC of the next road segment and the predicted SOC change of the next road segment.

[0510] In some embodiments, the upper and lower limits of the target SOC of the target road segment are determined by the intersection of the first predicted SOC range and the second predicted SOC range of the target road segment.

[0511] In some embodiments, the target road segment in the preset travel route includes at least one sub-road segment, each sub-road segment corresponding to sub-traffic information, and the target road segment is determined based on the sub-traffic information of the at least one sub-road segment.

[0512] In some embodiments, sub-road condition information includes at least one of the following: road type, road name, road traffic signs, road speed limit, congestion level, distance length, travel time required, average vehicle speed, gradient, traffic light information, and weather information. For example, road type may include ordinary roads, expressways, highways, and congested roads. Congestion level may include high, medium, and low to reflect different levels of road congestion. Travel time required is the time required for a vehicle to travel from the beginning to the end of the sub-road segment, which can be obtained through big data analysis based on historical data of multiple vehicles traveling on the sub-road segment. Average vehicle speed is the average speed of vehicles traveling on the sub-road segment, for example, it may be the average speed of vehicles performing the above vehicle control method that have previously traveled on the sub-road segment, or it may be the average speed of multiple vehicles traveling on the sub-road segment. For example, if vehicle 1 travels at an average speed of 10 m / s on the sub-road segment, vehicle 2 travels at an average speed of 11 m / s on the sub-road segment, and vehicle 3 travels at an average speed of 9 m / s on the sub-road segment, then based on the average speeds of vehicle 1, vehicle 2, and vehicle 3, the average speed of the sub-road segment can be determined to be (10 + 11 + 9) / 3 = 10 m / s.

[0513] In some embodiments, the sub-traffic information satisfies at least one of the following conditions as well as the target road segment: the sub-traffic information includes road type, and all sub-road segments included in the target road segment have the same road type; or, the sub-traffic information includes average vehicle speed, and the average vehicle speed of all sub-road segments included in the target road segment belongs to the same speed range.

[0514] In some embodiments, determining a road segment includes: combining at least two adjacent sub-road segments of the same type as a pre-divided road segment; and, if the average speed of the sub-road segments adjacent to the pre-divided road segment and the average speed of the sub-road segments in the pre-divided road segment are within the same speed range, combining the pre-divided road segment and the adjacent sub-road segments as a road segment in a preset travel route.

[0515] In some embodiments, sub-road condition information includes the length of sub-road segments. After determining road segments based on the sub-road condition information of each sub-road segment, the length of each road segment must be greater than or equal to a preset distance threshold. By constraining the length of each road segment, it can be ensured that the number of road segments is not excessive, reducing the possibility of excessive computation.

[0516] In some embodiments, the target road segment in the preset travel route is obtained based on the road intervals that are successfully matched with the road condition data of the preset road conditions. The road intervals are obtained from the preset travel route based on the road condition data of the preset travel route, and the road characteristic parameters of the road intervals are determined based on the historical driving parameters of the vehicle in the road intervals.

[0517] In some embodiments, the target road segment in the preset travel route is the output of a pre-trained neural network model, and the input of the neural network model includes traffic data of the preset travel route.

[0518] In some embodiments, the traffic information of the target road segment is obtained based on the sub-traffic information of the sub-road segments included in the target road segment.

[0519] In some embodiments, the method for determining the target SOC of a target road segment in a preset travel route includes: determining the target SOC of the target road segment based on the target SOC of the preceding road segment and the traffic information of the target road segment; or, determining the target SOC of the target road segment based on the target SOC of the next road segment and the traffic information of the next road segment.

[0520] In some embodiments, the step of determining the target SOC of a target road segment based on the target SOC of the preceding road segment and the road condition information of the target road segment includes: determining the SOC change of a vehicle traveling on the target road segment based on the road condition information of the target road segment; and determining the target SOC of the target road segment based on the target SOC of the preceding road segment and the SOC change of the target road segment.

[0521] In some embodiments, the step of determining the target SOC of a target road segment based on the target SOC of the next road segment and the road condition information of the next road segment includes: determining the SOC change of a vehicle traveling on the next road segment based on the road condition information of the next road segment; and determining the target SOC of the target road segment based on the target SOC of the next road segment and the SOC change of the next road segment.

[0522] In some embodiments, the method for determining the target SOC includes: obtaining the initial SOC of the vehicle's power battery on a preset travel route; and determining the target SOC of each road segment based on the initial SOC and road condition information of each road segment.

[0523] In some embodiments, both sub-road condition information and road condition information include road type; the road type of the target road segment is the target road type among the road types of each sub-road segment included in the target road segment, for example, the sub-road segment corresponding to the target road type has the highest proportion among all the sub-road segments included in the target road segment.

[0524] In some embodiments, both sub-road condition information and road condition information include average vehicle speed and distance length. The average vehicle speed of the target road segment is calculated based on the average vehicle speed and distance length of each sub-road segment in the target road segment, and the distance length of the target road segment is the sum of the distance lengths of each sub-road segment in the target road segment.

[0525] In some embodiments, the step of determining the target SOC of each road segment based on the initial SOC and the road condition information of each road segment includes: predicting the route vehicle energy consumption of the preset travel route based on the road condition information and energy consumption impact information of the preset travel route; and determining the target SOC of the power battery of each road segment based on the initial SOC of the power battery of each road segment and the road segment vehicle energy consumption with the goal of minimizing the fuel consumption of the preset travel route.

[0526] In some embodiments, the step of determining the target SOC of each road segment based on the road condition information and the destination SOC of each road segment includes: determining the SOC change of the vehicle traveling on each road segment based on the road condition information of each road segment; and determining the target SOC of each road segment based on the destination SOC and the SOC change of each road segment.

[0527] In some embodiments, it is assumed that the preset travel route includes k road segments, where k is a positive integer; the destination SOC is taken as the target SOC of the kth road segment; the target SOC of the (i-1)th road segment is calculated based on the target SOC of the i-th road segment and the change in SOC of the i-th road segment, where i = 2, 3, 4, ..., k.

[0528] In some embodiments, traffic information includes road type, congestion level, and distance. The SOC change of the target road segment in the preset travel route is determined based on the power consumption per unit distance of the target road segment and the distance length. The power consumption per unit distance of the target road segment is determined based on the road type and congestion level of the target road segment.

[0529] In some embodiments, the power consumption per unit distance of the target road segment is obtained by querying a preset table based on the road type and congestion level of the target road segment.

[0530] In some embodiments, after a vehicle travels a road of a preset length, the power consumption per unit distance to be updated in the preset table is updated based on the actual power consumption per unit distance of the vehicle on the road of the preset length.

[0531] In some embodiments, the power consumption per unit distance to be updated in the preset table is updated to the actual power consumption per unit distance.

[0532] In some embodiments, the unit distance power consumption to be updated in the preset table is updated to the target unit distance power consumption, which is calculated based on the unit distance power consumption to be updated, the first weight corresponding to the unit distance power consumption to be updated, the actual unit distance power consumption, and the second weight corresponding to the actual unit distance power consumption.

[0533] In some embodiments, traffic information includes road type, congestion level, and travel time; the SOC change of the target road segment in the preset travel route is determined based on the SOC change rate of the target road segment and the travel time, and the SOC change rate of the target road segment is determined based on the road type and congestion level of the target road segment.

[0534] In some embodiments, the step of determining the target SOC of each road segment based on the initial SOC and the road condition information of each road segment includes: determining the target SOC of the vehicle at the end of each road segment based on the initial SOC and the road condition information of each road segment; and determining the target SOC of each road segment based on the target SOC of the vehicle at the end of each road segment.

[0535] In some embodiments, multiple SOC change paths can be determined based on the target SOC. For example, each SOC change path includes a set of SOCs. The SOC change path that minimizes energy consumption of the vehicle when running on the preset travel route among the multiple SOC change paths is determined as the target SOC change path. The SOCs included in the target SOC change path are determined as the target SOCs of each road segment.

[0536] In some embodiments, the target SOC at the end of the first segment of the preset travel route is determined based on the initial SOC and the traffic information of the first segment. The target SOC at the end of a non-first segment of the preset travel route is determined based on the traffic information of the non-first segment and the target SOC at the end of the segment preceding the non-first segment.

[0537] In some embodiments, the upper and lower limits of the target SOC of the first road segment are determined based on the initial SOC and the traffic information of the first road segment; the upper limit of the target SOC of non-first road segments is determined based on the traffic information of non-first road segments and the upper limit of the target SOC of the preceding non-first road segment; the lower limit of the target SOC of non-first road segments is determined based on the traffic information of non-first road segments and the lower limit of the target SOC of the preceding non-first road segment.

[0538] Based on the road condition information of the road segment and the upper limit of the target SOC of the previous road segment, the third SOC is determined. The third SOC is the battery SOC of the vehicle when it ends in hybrid mode on the road segment. Based on the road condition information of the corresponding road segment and the lower limit of the target SOC of the previous road segment, the fourth SOC is determined. The fourth SOC is the battery SOC of the vehicle when it ends in pure electric mode on the road segment. Using the third SOC as the upper limit and the fourth SOC as the lower limit, the target SOC of the vehicle at the end of the road segment is obtained.

[0539] In some embodiments, the step of determining the target SOC of each road segment based on the initial SOC and the road condition information of each road segment includes: determining the final SOC of the power battery when the vehicle travels to the end of the preset travel route based on the initial SOC; determining the target SOC of the vehicle at the end of each road segment of the preset travel route based on the initial SOC, the final SOC and the road condition information of the preset travel route; and determining the target SOC of each road segment of the preset travel route based on the target SOC.

[0540] In some embodiments, the target SOC of a target segment in a preset travel route is determined based on a first target SOC and a second target SOC of the target segment; when the target segment is the first segment of the preset travel route, the first target SOC of the target segment is determined based on the starting SOC and the traffic information of the target segment; when the target segment is not the first segment of the preset travel route, the first target SOC of the target segment is determined based on the first target SOC of the preceding segment and the traffic information of the target segment; when the target segment is the last segment of the preset travel route, the second target SOC of the target segment is determined based on the ending SOC and the traffic information of the target segment; when the target segment is not the last segment of the preset travel route, the second target SOC of the target segment is determined based on the second target SOC of the following segment and the traffic information of the target segment.

[0541] In some embodiments, the first target SOC of the target road segment is determined based on the charging and discharging power range corresponding to the target road segment, the road condition information of the target road segment, and the initial SOC. The second target SOC of the target road segment is determined based on the charging and discharging power range corresponding to the target road segment, the road condition information of the target road segment, and the final SOC. The charging and discharging power range of the target road segment is obtained based on the vehicle energy consumption of the target road segment, the noise, vibration, and harshness (NVH) limiting power of the vehicle's engine, and the maximum charging and discharging power of the power battery. The vehicle energy consumption of the target road segment is determined based on the road condition information of the target road segment.

[0542] The vehicle's total energy consumption for the road segment is determined based on road condition information. The first target SOC at the end of the road segment is determined based on road condition information, initial SOC, total energy consumption for the road segment, the vehicle's engine NVH limit power, and the maximum charge / discharge power of the battery. For example, the NVH limit power is a power threshold value that limits the engine's power to meet certain NVH performance requirements.

[0543] The vehicle's total energy consumption during its journey on the road segment is determined based on road condition information. The second target SOC at the start of the journey is determined based on road condition information, destination SOC, total vehicle energy consumption, engine NVH limit power, and maximum charge / discharge power of the power battery.

[0544] Based on the vehicle energy consumption, NVH power limit, and maximum charging and discharging power of each road segment, the charging and discharging power range corresponding to each road segment is obtained; based on the initial SOC, road condition information, and charging and discharging power range, the first target SOC is determined.

[0545] Based on the vehicle energy consumption, NVH power limit, and maximum charging and discharging power of each road segment, the charging and discharging power range corresponding to each road segment is obtained; based on the destination SOC, road condition information, and charging and discharging power range, the second target SOC is determined.

[0546] In some embodiments, the vehicle energy consumption of the target road segment is obtained by inputting the road condition information of the target road segment and the user's driving style information into the target energy consumption prediction model and then outputting the target energy consumption prediction model. The target energy consumption prediction model is determined from multiple preset energy consumption prediction models based on the road condition information of the target road segment and the user's driving style information.

[0547] Based on road condition information and user driving style information, a target energy consumption prediction model is determined from multiple preset energy consumption prediction models. The road condition information and user driving style information are then input into the target energy consumption prediction model to obtain the vehicle energy consumption of the road segment output by the target energy consumption prediction model.

[0548] For example, the aforementioned road condition information, driving style information, vehicle status, and user vehicle settings can be input into the target energy consumption prediction model to obtain the model's output of the vehicle's energy consumption for the road segment, thereby improving the accuracy of the prediction results. For example, vehicle status includes vehicle weight, drag coefficient, rolling resistance coefficient, tire pressure, etc. Vehicle settings can include air conditioning settings.

[0549] In some embodiments, a SOC is selected from the target SOC corresponding to each road segment; and a SOC change path is obtained from multiple SOC change paths based on each SOC.

[0550] In some embodiments, determining the target SOC change path that minimizes vehicle energy consumption during a preset travel route from multiple SOC change paths can employ dynamic programming algorithms, Pontryagin's minimum principle (PMP) algorithms, or similar methods. These algorithms use the target SOC of each road segment as the feasible region of the state variables. For numerical calculation, the feasible region needs to be discretized, i.e., the target SOC of each road segment is discretized. For example, it can be discretized at equal intervals; if the difference between the maximum and minimum SOC values ​​of a segment is greater than 0.005, it is discretized at intervals of 0.005; if the difference is less than 0.005, the SOC is discretized in three equal intervals. The control variables are the operating mode and engine operating point (torque, speed), for example, the operating modes include pure electric, series, and parallel. To reduce computational requirements and accelerate the calculation process, the feasible region of the engine operating point can be simplified; in series and parallel modes, the engine operating point uses the control line calculated based on optimal system efficiency. Optimizing the engine operating point requires considering NVH constraints, which are simplified to constraints on engine speed based solely on vehicle speed. Within the feasible region, solving the optimization problem yields the target SOC change path with minimal energy consumption. The SOC included in this target SOC change path can be used as the target SOC for each segment of the preset travel route.

[0551] In some embodiments, if the starting SOC is greater than or equal to the first preset threshold, the ending SOC is the second preset threshold, which is greater than the first preset threshold; if the starting SOC is less than the first preset threshold, the ending SOC is the first preset threshold.

[0552] In some embodiments, the steps of controlling the vehicle's engine and motor based on the actual SOC and target SOC of the vehicle's power battery include: obtaining the actual SOC of the vehicle's power battery when the vehicle is traveling on a target road segment in a preset travel route, for example, the target road segment can be any road segment in the preset travel route; and controlling the vehicle to travel in pure electric mode or non-pure electric mode based on the actual SOC and the target SOC of the target road segment.

[0553] In some embodiments, the step of controlling the vehicle to operate in pure electric mode or non-pure electric mode based on the actual SOC and the target SOC of the target road segment includes:

[0554] When the vehicle speed is greater than or equal to a preset speed threshold: when the difference between the actual SOC and the target SOC is greater than or equal to the preset difference, the vehicle is controlled to drive in pure electric mode; when the difference between the actual SOC and the target SOC is less than the preset difference, the vehicle is controlled to drive in hybrid mode.

[0555] In some embodiments, the step of controlling the vehicle to operate in pure electric mode or non-pure electric mode based on the actual SOC and the target SOC of the target road segment includes: controlling the vehicle to operate in pure electric mode when the vehicle speed is less than a speed threshold. For example, considering engine characteristics, the engine is not allowed to start when the vehicle speed is less than the speed threshold. Therefore, if the vehicle speed is less than the speed threshold, the vehicle is directly switched to pure electric mode.

[0556] In some embodiments, the vehicle speed threshold is positively correlated with the actual state of charge (SOC) of the power battery. That is, the higher the actual SOC of the power battery, the higher the corresponding vehicle speed threshold; conversely, the lower the actual SOC of the power battery, the lower the corresponding vehicle speed threshold. For example, this vehicle speed threshold can be obtained through experimental calibration.

[0557] In some embodiments, if the preset travel route includes only one road segment, the steps of controlling the vehicle's engine and motor based on the actual SOC and target SOC of the vehicle's power battery include: determining the vehicle's total energy consumption on the road segment based on road condition information; when the initial SOC is greater than the final SOC: if the SOC difference is greater than or equal to the total vehicle energy consumption, controlling the vehicle to operate in pure electric mode; if the SOC difference is less than the total vehicle energy consumption, first controlling the vehicle to operate in hybrid mode to maintain the actual SOC of the power battery as the initial SOC, and then controlling the vehicle to operate in pure electric mode. When the initial SOC is less than or equal to the final SOC, controlling the vehicle to operate in hybrid mode.

[0558] In some embodiments, the steps of controlling the vehicle's engine and motor based on the actual SOC and target SOC of the vehicle's power battery include:

[0559] When the target SOC of the target road segment is less than the initial SOC of the target road segment: when the actual SOC of the power battery is greater than the minimum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to operate in pure electric mode on the target road segment; when the actual SOC of the power battery is equal to the minimum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to maintain the actual SOC of the power battery unchanged in hybrid mode.

[0560] When the target SOC of the target road segment is greater than the initial SOC of the target road segment: when the actual SOC of the power battery is less than the maximum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to operate in hybrid mode on the target road segment; when the actual SOC of the power battery is equal to the maximum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to maintain the actual SOC of the power battery unchanged in hybrid mode; when the actual SOC of the power battery is greater than the maximum permissible SOC or the target SOC of the target road segment, the vehicle is controlled to operate in pure electric mode on the target road segment.

[0561] In some embodiments, the steps of controlling the vehicle's engine and motor based on the actual SOC and target SOC of the vehicle's power battery include: determining the category coefficient of the target road segment based on the road condition information of the target road segment in the preset travel route; determining the equivalent factor corresponding to the target road segment based on the target SOC and the category coefficient of the target road segment; determining the instantaneous output power of the vehicle's power battery at each moment of operation on the target road segment using the equivalent factor of the target road segment and the Equivalent Consumption Minimum Strategy (ECMS); and controlling the vehicle based on the instantaneous output power.

[0562] In some embodiments, the equivalent factor corresponding to the target road segment is obtained by looking up a table based on the category coefficient of the target road segment and the target SOC.

[0563] In some embodiments, the instantaneous output power of the power battery during operation on the target road segment is calculated according to the following formula:

[0564] In some embodiments, the steps of controlling the vehicle based on the instantaneous output power include: obtaining the vehicle's required power at time t; determining the engine's instantaneous output power at time t based on the vehicle's required power, the power battery's instantaneous output power at time t, and the engine's NVH limiting power; and controlling the power battery and engine based on the power battery's instantaneous output power at time t and the engine's instantaneous output power at time t.

[0565] In some embodiments, considering that some unexpected situations may occur when the vehicle is traveling on the preset travel route, the target SOC is re-determined if the difference between the actual SOC of the vehicle's power battery and the target SOC of the target road segment is greater than a set threshold when the vehicle is traveling on the target road segment; the target SOC is re-determined when the vehicle's position deviates from the preset travel route; and the target SOC is re-determined if the road conditions of the target road segment change when the vehicle is traveling on the target road segment.

[0566] In some embodiments, the preset travel route includes a starting point and a destination. If the destination of the preset travel route has charging facilities, the destination SOC of the vehicle when it reaches the destination is reduced.

[0567] In some embodiments, the reduced endpoint SOC meets the vehicle's minimum permissible SOC.

[0568] In some embodiments, the destination of the preset travel route has charging conditions, including: if there is a charging address at the destination and there is an idle charging pile at the charging address, then it is determined that the destination has charging conditions.

[0569] In some embodiments, when the vehicle reaches the end of any road segment, the target SOC of the remaining road segment is updated based on the target SOC of the power battery in that road segment and the predicted vehicle energy consumption of the remaining road segment, with the goal of minimizing the fuel consumption of the preset travel route.

[0570] In some embodiments, if the traffic information is updated, the remaining travel route is re-divided into road segments to obtain at least one new road segment; the remaining travel route refers to the route taken from the current location of the vehicle to the end point of the preset travel route; the target SOC of each new road segment is updated based on the initial SOC of the power battery and the vehicle energy consumption of each new road segment, with the goal of minimizing the fuel consumption of the preset travel route.

[0571] Please refer to the steps of the above-mentioned control device 50, which aims to minimize fuel consumption along the preset travel route and plans the target SOC for each road segment based on the overall vehicle energy consumption of each road segment. This solution will not repeat the steps.

[0572] S1304 controls the engine, drive motor, generator, and power battery according to the target SOC and actual vehicle requirements for each road segment, so that the engine operates in the high-efficiency range.

[0573] In some embodiments, the engine, drive motor, generator, and power battery are controlled according to the initial SOC, target SOC, and actual vehicle demand for each road segment, so that the engine operates in an efficient operating range. This can be achieved by: if the target SOC is greater than a certain threshold of the initial SOC and the actual vehicle demand is less than the initial SOC, controlling the engine to operate in an efficient and economical range, then controlling the engine to drive efficiently and generate electricity, storing excess electricity in the power battery; if the target SOC is greater than a certain threshold of the initial SOC and the actual vehicle demand is greater than or equal to the engine operating in an efficient and economical range, then controlling the engine to operate in an efficient operating range, supplying power to the power battery, and driving it via the drive motor or in conjunction with the engine; if the target SOC is less than a certain threshold of the initial SOC, then controlling the engine to shut down.

[0574] In some embodiments, the vehicle is controlled to travel on a preset path based on a target speed.

[0575] In some embodiments, a prompt message is generated based on the target speed for minimizing overall vehicle energy consumption. The prompt message is used to prompt the driver to control the vehicle's movement based on the target speed for minimizing overall vehicle energy consumption.

[0576] In some embodiments, the prompt information includes at least one of target vehicle speed or pedal control information.

[0577] In some embodiments, the engine, drive motor, generator, and power battery are controlled based on the target SOC of each road segment, the actual vehicle requirements, and the reduced destination SOC when driving to the destination, so that the engine operates in its high-efficiency range.

[0578] In some embodiments, if the navigation system auto-start function is turned off, the navigation system is turned off, and the preset travel route is a commuter route, the control device is further configured to control the engine, drive motor, generator, and power battery based on the vehicle's historical driving data corresponding to the commuter route, so that the engine operates in a high-efficiency range.

[0579] In some embodiments, historical driving data includes a sequence of vehicle speeds as the vehicle travels a commuter route over a historical time period.

[0580] In some embodiments, if the navigation system auto-start function is disabled, the navigation system is turned off, and the preset travel route is not a commuting route, the control device 50 is further configured to: predict the vehicle speed within a preset time period while the vehicle is traveling on the preset travel route, and obtain the predicted vehicle speed within the preset time period; predict the component control sequence of the vehicle within the preset time period based on the predicted vehicle speed within the preset time period; and control the corresponding component according to the first control instruction in the component control sequence; the component includes at least one of the accelerator and the pedal.

[0581] In some embodiments, the component includes at least one of an accelerator and a pedal; the component control sequence includes at least one control instruction for the component; controlling the corresponding component means controlling the corresponding component to execute the first control instruction.

[0582] In some embodiments, the preset time period refers to the time period elapsed since the last control of the corresponding component according to the control command. For example, after a predicted vehicle speed of 5 to 10 seconds has been completed, the vehicle speed for the next 5 to 10 seconds is predicted. For example, the first preset time period can be 5 to 10 seconds in the future.

[0583] In some embodiments, predicting the vehicle speed within a preset time period can be achieved by: obtaining historical driving data of the vehicle within a preset historical time period when the intelligent driving function is turned off; and predicting the vehicle speed within the preset time period based on the historical driving data.

[0584] In some embodiments, historical driving data includes vehicle speed sequences; predicting the vehicle speed within a preset time period based on historical driving data to obtain the predicted vehicle speed within the preset time period can be achieved by: dividing the vehicle speed sequence to obtain at least one vehicle speed interval; obtaining the vehicle speed state transition probability from the target vehicle speed interval to the next vehicle speed interval corresponding to the target vehicle speed interval, thereby constructing a system state transition probability matrix; the target vehicle speed interval refers to any one of the at least one vehicle speed intervals, and the system state transition probability matrix includes at least one transition probability; and predicting the vehicle speed at each moment within the preset time period based on the system state transition probability matrix and the vehicle speed at the current moment to obtain the predicted vehicle speed within the preset time period.

[0585] In some embodiments, predicting the vehicle speed at each moment within a preset time period based on the system state transition probability matrix and the vehicle speed at the current moment, to obtain the predicted vehicle speed within the preset time period, includes: correcting the system state transition probability matrix based on vehicle speed limits and traffic flow speed limits; and predicting the vehicle speed at each moment within the preset time period based on the corrected system state transition probability matrix and the vehicle speed at the current moment, to obtain the predicted vehicle speed within the preset time period.

[0586] In some embodiments, the short-term predicted operating condition can be obtained based on the predicted vehicle speed by: predicting the vehicle's operating condition within a preset time period based on the corrected system state transition probability matrix and the predicted vehicle speed within a preset time period; the short-term predicted operating condition includes the predicted vehicle speed within the preset time period.

[0587] In some embodiments, when it is determined that the distance to or relative speed with the vehicle in front does not meet the conditions for safe driving, the vehicle is controlled to brake.

[0588] In some embodiments, if the intelligent driving function is enabled, the navigation-assisted driving function is disabled, the adaptive cruise control function is enabled, there are no vehicles ahead, and speed planning is not activated, the control device is also configured to control the vehicle to travel based on the current speed.

[0589] In some embodiments, if the intelligent driving function is enabled, the navigation-assisted driving function is disabled, the adaptive cruise control function is enabled, there is a vehicle ahead, and speed planning is not activated, the control device is further configured to: obtain the current speed of the vehicle ahead; and control the vehicle to drive based on the current speed of the vehicle ahead.

[0590] In some embodiments, when the intelligent driving function is enabled, the navigation-assisted driving function is disabled, the adaptive cruise control function is enabled, there is a vehicle ahead, and speed planning is activated, the following steps are triggered: A speed sequence is generated based on the road traffic flow speed of the preset travel route and the vehicle's current speed, with the objective function being the minimum overall vehicle energy consumption along the route. The current speed is the vehicle's speed at the starting point of the preset travel route. The speed sequence is then corrected based on constraints, including at least the driving style operation. The current speed of the vehicle ahead is obtained. Based on the current speed of the vehicle ahead and the vehicle's target speed, the control speed of the vehicle is determined. The vehicle is then controlled to travel at the control speed.

[0591] In some embodiments, when the intelligent driving function is enabled, the navigation-assisted driving function is disabled, and the adaptive cruise control function is disabled, the vehicle speed within a preset time period is predicted based on the collected intelligent driving sensor data, the predicted vehicle speed within the preset time period is obtained, and the vehicle is controlled to drive based on the predicted speed.

[0592] In some embodiments, the engine, drive motor, generator, and power battery are controlled according to the target SOC of each road segment, actual vehicle demand, traffic light information, and an energy management strategy based on navigation information fusion. This ensures that the engine operates within its efficient operating range. This can be achieved by: determining whether the vehicle has the capability to pass through the signalized intersection corresponding to the traffic light information based on the vehicle's current speed and traffic light information; if the vehicle does not have the capability to pass through the signalized intersection corresponding to the traffic light information, calculating the vehicle's drivable time; controlling the engine to operate efficiently or shut down, controlling the vehicle to travel at its current speed within the drivable time, and disengaging the mechanical brakes and activating a preset energy recovery level at the end of the drivable time.

[0593] In some embodiments, the driving time of a vehicle can be calculated by: calculating the vehicle's coasting distance; determining the vehicle's driving distance based on the coasting distance and the distance between the vehicle and the traffic light; and determining the vehicle's driving time based on the driving distance and the vehicle's current speed.

[0594] When none of the above strategies are met, the State of Charge (SOC) for maintaining battery power is adjusted based on driver style, vehicle speed, or environmental information. The engine, drive motor, generator, and power battery are controlled based on the comparison between the vehicle's actual SOC and the adjusted SOC for maintaining battery power, so that the engine operates in its high-efficiency range.

[0595] In some embodiments, the temperature of the power battery is adjusted based on the charging status, preset travel route, and user's scheduled pick-up time.

[0596] In some embodiments, a target temperature for the passenger compartment is generated based on the current passenger compartment temperature and the user's scheduled boarding time, and the passenger compartment temperature is controlled by the air conditioning system to reach the target temperature.

[0597] In some embodiments, engine coolant preheating is controlled when the target temperature of the crew compartment is greater than the current temperature of the crew compartment.

[0598] In some embodiments, the temperature of the power battery is adjusted based on the charging status, preset travel route, and user's scheduled pick-up time.

[0599] In some embodiments, a target temperature deviation value is acquired during vehicle operation, the target temperature of the passenger compartment is corrected based on the target temperature deviation value, and the passenger compartment temperature of the vehicle is controlled to reach the corrected target temperature of the passenger compartment.

[0600] In some embodiments, the target temperature deviation value can be obtained during vehicle operation by:

[0601] During vehicle operation, temperature influencing factors are collected, including at least one of vehicle information and environmental information. Vehicle information includes at least one of window opening information, engine coolant temperature, navigation time, and target temperature deviation value. Environmental information includes at least one of weather information and outside temperature. The target temperature deviation value is determined based on the temperature deviation values ​​corresponding to the temperature influencing factors.

[0602] In some embodiments, there are multiple temperature deviation values ​​corresponding to temperature influencing factors; the target temperature deviation value is determined based on the temperature deviation values ​​corresponding to the temperature influencing factors by: obtaining the driving mileage of the preset travel route; if the driving mileage is greater than a third preset di...

Claims

A new energy vehicle energy intelligent management system comprises: a driving device comprising: an engine configured to selectively output power to a wheel end of the vehicle; a driving motor configured to output power to the wheel end; and a generator connected to the engine to generate power under the drive of the engine; a power battery configured to supply power to the driving motor and to be charged according to alternating current output by one of the generator or the driving motor; and a control device configured to: obtain multi-domain data fusion information, the multi-domain fusion data information at least including cabin domain information and power domain information; wherein the cabin domain information at least includes user behavior information and road condition information of a preset travel path, and the power domain information at least includes vehicle state information; predict path vehicle energy consumption of the preset travel path according to the multi-domain data fusion information, the preset travel path comprising a plurality of road segments, and the path vehicle energy consumption comprising road segment vehicle energy consumption of the plurality of road segments; plan a target SOC of each road segment in the plurality of road segments according to the road segment vehicle energy consumption of the each road segment, with the lowest fuel consumption of the preset travel path as a target; and control the driving device and the power battery according to the target SOC of the each road segment and actual vehicle demand, so that the engine operates in a high-efficiency operating interval. The system of claim 1, wherein The vehicle state information at least includes static parameters of the vehicle, and the road condition information at least includes road traffic flow speed. The prediction of the path vehicle energy consumption of the preset travel path according to the multi-domain data fusion information comprises: predicting the path vehicle energy consumption of the preset travel path according to the road traffic flow speed and the static parameters of the vehicle according to an energy consumption prediction algorithm of a vehicle theory; and correcting the path vehicle energy consumption according to the user behavior information, the corrected path vehicle energy consumption being a theoretical demand energy consumption. The system of claim 2, wherein, The static parameters of the vehicle at least include air resistance, rolling resistance, acceleration resistance, and slope resistance of the vehicle. The system of any one of claims 1-3, wherein The vehicle state information at least includes vehicle type information, and the user behavior information at least includes a driving style of a user, and the road condition information at least includes a road type. The prediction of the path vehicle energy consumption of the preset travel path according to the multi-domain data fusion information comprises: inputting the road type, the driving style, and the vehicle type information into a target energy consumption prediction model, and outputting the predicted path vehicle energy consumption of the preset travel path from the target energy consumption prediction model, the path vehicle energy consumption being a reference demand energy consumption; wherein the target energy consumption prediction model is determined from a plurality of preset energy consumption prediction models according to at least one of the road type of the preset travel path or the driving style information of the user. The system of claim 4, wherein, The road type at least includes ordinary roads, express roads, highways, and congested roads, and the driving style of the user is at least divided into fierce, ordinary, and mild according to a change rate of an accelerator pedal opening degree and a change rate of acceleration. The system of any one of claims 1-5, wherein The path whole-vehicle energy consumption of the preset travel path is predicted according to the multi-domain data fusion information, and the method comprises the steps of: The path whole-vehicle energy consumption of the preset travel path is predicted according to the theoretical demand energy consumption and the reference demand energy consumption of the vehicle on the preset travel path. The system of claim 6, wherein, The path whole-vehicle energy consumption of the preset travel path is predicted according to the theoretical demand energy consumption and the reference demand energy consumption of the vehicle on the preset travel path, and the method comprises the steps of: A first weight of the theoretical demand energy consumption and a second weight of the reference demand energy consumption of the vehicle are obtained; and The path whole-vehicle energy consumption of the vehicle is predicted by performing a weighted operation on the theoretical demand energy consumption and the reference demand energy consumption of the vehicle according to the first weight and the second weight. The system of claim 7, wherein, The control device is further configured to: The first weight of the theoretical demand energy consumption and the second weight of the reference demand energy consumption are added to be 1, and the first weight of the theoretical demand energy consumption and the second weight of the reference demand energy consumption are updated to obtain an updated first weight of the theoretical demand energy consumption and an updated second weight of the reference demand energy consumption, with the actual road section whole-vehicle energy consumption being within a preset range as a constraint condition. The first weight of the theoretical demand energy consumption and the second weight of the reference demand energy consumption of the vehicle are obtained, and the method comprises the steps of: The updated first weight of the theoretical demand energy consumption and the updated second weight of the reference demand energy consumption of the vehicle are obtained. The system of any one of claims 6-8, wherein The vehicle state information at least includes static parameters and vehicle model information of the vehicle, and the user behavior information at least includes a driving style of the user, and the road condition information at least includes a road traffic flow speed and a road type. The theoretical demand energy consumption is obtained by the following steps: The path whole-vehicle energy consumption of the preset travel path is predicted according to the road traffic flow speed and the static parameters of the vehicle by using an energy consumption prediction algorithm of automobile theory; and The path whole-vehicle energy consumption is corrected according to the user behavior information, and the corrected path whole-vehicle energy consumption is the theoretical demand energy consumption. The reference demand energy consumption is obtained by the following steps: The road type, the driving style and the vehicle model information are input into a target energy consumption prediction model, and the path whole-vehicle energy consumption of the predicted preset travel path is output by the target energy consumption prediction model, and the path whole-vehicle energy consumption is the reference demand energy consumption; wherein the target energy consumption prediction model is determined from a plurality of preset energy consumption prediction models according to at least one of the road type of the preset travel path or the driving style information of the user. The system of any one of claims 1-9, wherein The vehicle state information at least includes static parameters and a target vehicle speed of minimum path whole-vehicle energy consumption of the vehicle, and the road condition information at least includes a road traffic flow speed, and the user behavior information at least includes a driving style of the user. The path whole-vehicle energy consumption of the preset travel path is predicted according to the multi-domain data fusion information, and the method comprises the steps of: The path whole-vehicle energy consumption of the preset travel path is predicted according to the driving style, the road traffic flow speed, the static parameters of the vehicle and the target vehicle speed of minimum path whole-vehicle energy consumption by using an energy consumption prediction algorithm of automobile theory. The system of claim 10, wherein, The control device is further configured to control the vehicle to travel on the preset path based on the target vehicle speed. The system according to claim 10 or 11, wherein The control device is further configured to generate a prompt information based on the target vehicle speed for minimizing the vehicle energy consumption, and the prompt information is used to prompt the driver to control the vehicle to travel based on the target vehicle speed for minimizing the vehicle energy consumption. The system of claim 12, wherein, The prompt information comprises at least one of a target vehicle speed or pedal control information. The system of any one of claims 10-13, wherein, The control device is further configured to perform one of the following operations: When the intelligent driving function is turned on and the vehicle speed planning is activated, trigger the operation of performing the energy consumption prediction algorithm according to the automobile theory, and predict the path vehicle energy consumption of the preset travel path according to the user behavior information, the road traffic flow speed, the static parameters of the vehicle and the target vehicle speed for minimizing the vehicle energy consumption; Or, When the intelligent driving function is turned on and the navigation assisted driving function is turned on, trigger the operation of performing the energy consumption prediction algorithm according to the automobile theory, and predict the path vehicle energy consumption of the preset travel path according to the user behavior information, the road traffic flow speed, the static parameters of the vehicle and the target vehicle speed for minimizing the vehicle energy consumption; Or, The system of any one of claims 10-14, wherein, When the intelligent driving function is turned on, the navigation assisted driving function is turned off, the adaptive cruise control function is turned on, there is no vehicle in front, and the energy-saving driving guidance function is turned on, trigger the operation of performing the energy consumption prediction algorithm according to the automobile theory, and predict the path vehicle energy consumption of the preset travel path according to the user behavior information, the road traffic flow speed, the static parameters of the vehicle and the target vehicle speed for minimizing the vehicle energy consumption. The control device is further configured to: The system of any one of claims 1-15, wherein, When the intelligent driving function is turned off and the energy-saving driving guidance function is turned on, trigger the operation of performing the energy consumption prediction algorithm according to the automobile theory, and predict the path vehicle energy consumption of the preset travel path according to the user behavior information, the road traffic flow speed, the static parameters of the vehicle and the target vehicle speed for minimizing the vehicle energy consumption. The road condition information comprises at least one of a road type, a road name, a road traffic sign, a road speed limit, a congestion level, a road length, a required time for passing, an average vehicle speed, a slope, traffic light information and weather information. The energy consumption influence information comprises the vehicle state information; or the energy consumption influence information comprises at least one of user driving style information or traffic light information, and the vehicle state information. The system of any one of claims 1-16, wherein, The actual vehicle energy demand of the vehicle on each road section comprises a vehicle power required by the vehicle to travel on each road section. The control device is further configured to: The system of any one of claims 1-17, wherein, When the vehicle travels to the end point of any road section, update the target SOC of the remaining road sections with the target SOC of the dynamic power battery in the any road section and the predicted road section vehicle energy consumption of the remaining road sections as the target of the lowest fuel consumption of the preset travel path. The control device is further configured to: If the road condition information is updated, the remaining travel path is re-divided into at least one new road segment; the remaining travel path refers to a path from the current position of the vehicle to the end point of the preset travel path; and According to the starting SOC of the power battery and the road segment vehicle energy consumption of each new road segment in the at least one new road segment, the target SOC of each new road segment is updated with the lowest fuel consumption of the preset travel path as the target. The system of any one of claims 1-18, wherein If the navigation system self-start function is closed, the navigation system is closed, and the preset travel path is a commuting route, the control device is further configured to control the engine, the drive motor, the generator and the power battery according to the historical driving data corresponding to the commuting route of the vehicle, so that the engine works in the high-efficiency working interval. The system of any one of claims 1-19, wherein If the navigation system self-start function is closed, the navigation system is closed, and the preset travel path is not a commuting route, the control device is further configured to: During the driving of the vehicle on the preset travel path, the vehicle speed of the vehicle in a preset time period is predicted to obtain the predicted vehicle speed of the vehicle in the preset time period; According to the predicted vehicle speed in the preset time period, the control sequence of the parts of the vehicle in the preset time period is predicted; and According to the first control instruction in the part control sequence, the corresponding part is controlled; wherein the part includes at least one of the throttle and the pedal. The system of claim 20, wherein, The prediction of the vehicle speed of the vehicle in the preset time period to obtain the predicted vehicle speed of the vehicle in the preset time period includes: When the intelligent driving function is closed, the historical driving data of the vehicle in a preset historical time period is obtained; and According to the historical driving data, the vehicle speed of the vehicle in the preset time period is predicted to obtain the predicted vehicle speed of the vehicle in the preset time period. The system according to claim 20 or 21, wherein, The control device is further configured to control the vehicle to brake when it is determined that the distance from the front vehicle or the relative speed of the front vehicle does not meet the safe driving condition. The system of any one of claims 1-22, wherein The control device is further configured to perform one of the following: if the intelligent driving function is turned on, the navigation assisted driving function is turned off, the adaptive cruise control function is turned on, there is no vehicle in front, and the vehicle speed planning is not activated, the vehicle is controlled to drive based on the current vehicle speed; Or, If the intelligent driving function is turned on, the navigation assisted driving function is turned off, the adaptive cruise control function is turned on, there is a vehicle in front, and the vehicle speed planning is not activated, the current vehicle speed of the vehicle in front of the vehicle is obtained, and the vehicle is controlled to drive based on the current vehicle speed of the vehicle in front. The system of any one of claims 1-23, wherein The control device is further configured to perform one of the following: When the intelligent driving function is turned on, the navigation auxiliary driving function is turned off, the adaptive cruise control function is turned on, there is a vehicle in front, and the vehicle speed planning is activated, a target function of minimizing the whole vehicle energy consumption along the path is triggered, a speed sequence is generated according to the road traffic flow speed of the preset travel path and the current vehicle speed of the vehicle, and the first speed in the speed sequence is taken as the target vehicle speed operation; The current vehicle speed of the vehicle in front is obtained; And According to the current vehicle speed of the front vehicle and the target vehicle speed of the vehicle, the control vehicle speed of the vehicle is determined; the vehicle is controlled to travel based on the control vehicle speed; Or, When the intelligent driving function is turned on, the navigation auxiliary driving function is turned off, and the adaptive cruise control function is turned off, the vehicle speed of the vehicle in a preset time period is predicted according to the collected intelligent driving sensor data, the predicted vehicle speed of the vehicle in the preset time period is obtained, and the vehicle is controlled to travel based on the predicted vehicle speed. The system of any one of claims 1-24, wherein The control device is further configured to adjust the power preservation SOC according to the driver style, the current vehicle speed of the vehicle, or the environment information of the vehicle; and control the engine, the drive motor, the generator and the power battery according to the comparison result of the actual SOC of the vehicle and the adjusted power preservation SOC, so that the engine works in the high-efficiency working interval. The system of any one of claims 1-25, wherein, The control device is further configured to perform one of the following: Adjust the temperature of the power battery according to the charging state, the preset travel path and the user's scheduled pick-up time; Or, Predict the output duration of the engine output power, and start the engine when the output duration is greater than a third preset duration; Or Predict the traffic jam time of the vehicle, and increase the water temperature of the engine when the current time and the traffic jam time interval is a fourth preset duration. The system of any one of claims 1-26, wherein The control device is further configured to predict the end point of the preset travel path, and when the distance between the current position of the vehicle and the end point is less than a preset distance, suspend adjusting the water temperature of the engine according to the target water temperature deviation of the engine, and increase the water temperature of the engine until the water temperature of the engine is higher than a preset temperature threshold before the vehicle reaches the end point. The system of any one of claims 1-27, wherein The control device is further configured to predict the end point of the preset travel path, and when the distance between the current position of the vehicle and the end point is less than a preset distance, suspend adjusting the temperature of the power battery according to the target temperature deviation of the power battery, and adjust the temperature of the power battery until the temperature of the power battery is within a preset temperature interval when the vehicle reaches the end point. The system of claim 28, wherein, The control device is further configured to predict the end point of the preset travel path, and when the distance between the current position of the vehicle and the end point is less than a preset distance, suspend controlling the passenger compartment temperature of the vehicle to reach the passenger compartment target temperature, and correct the passenger compartment target temperature. A control method of a new energy vehicle energy intelligent management system, comprising: Obtaining multi-domain data fusion information, the multi-domain data fusion information at least including cabin domain information and power domain information; wherein, the cabin domain information includes self-learned user driving habit information and preset travel path road condition information, and the power domain information at least includes vehicle state information; Predicting path vehicle energy consumption of the preset travel path according to the multi-domain data fusion information, the preset travel path including a plurality of road segments, and the path vehicle energy consumption including road segment vehicle energy consumption of the plurality of road segments; Planning target SOC of each road segment according to the road segment vehicle energy consumption of the each road segment with the lowest oil consumption of the preset travel path as a target; Controlling an engine, a drive motor, a generator and a power battery of the new energy vehicle according to the target SOC of the each road segment and actual vehicle demand, so that the engine works in a high-efficiency working interval. The method of claim 30, wherein, The planning target SOC of each road segment according to the initial SOC of the power battery of the each road segment and the road segment vehicle energy consumption with the lowest oil consumption of the preset travel path as a target includes: The planning target SOC of each road segment according to the initial SOC of the power battery of the each road segment and the road segment vehicle energy consumption with the lowest oil consumption of the preset travel path as a target includes: The controlling the engine, the drive motor, the generator and the power battery according to the target SOC of the each road segment and actual vehicle demand, so that the engine works in the high-efficiency working interval includes: The controlling the engine, the drive motor, the generator and the power battery according to the initial SOC, the target SOC of the each road segment and actual vehicle demand, so that the engine works in the high-efficiency working interval. The method of claim 31, wherein, The planning target SOC of each road segment according to the initial SOC of the power battery of the each road segment and the road segment vehicle energy consumption includes: Determining a predicted SOC change amount of the vehicle at the end of the each road segment according to the initial SOC of the power battery of the each road segment and the road segment vehicle energy consumption of the each road segment; Determining a plurality of SOC change paths according to the predicted SOC change amount, wherein each SOC change path in the plurality of SOC change paths includes a group of SOCs; Determining a target SOC change path in the plurality of SOC change paths that can make the oil consumption of the vehicle in the preset travel path operation be the lowest as a target SOC change path; and Determining the SOC included in the target SOC change path as the target SOC of the each road segment. The method of claim 32, wherein, The target SOC at the end of a first road segment of the preset travel path is determined according to an initial SOC of the vehicle and a predicted SOC change amount of the first road segment; The target SOC at the end of a non-first road segment of the preset travel path is determined according to a predicted SOC change amount of the non-first road segment and a target SOC at the end of a previous road segment of the non-first road segment. The method of claim 33, wherein, The predicted SOC change amount includes a first predicted SOC change amount and a second predicted SOC change amount; and the upper limit value of the target SOC of the first road segment of the preset travel path is determined according to the starting SOC and the first predicted SOC change amount of the first road segment. The lower limit value of the target SOC of the first road segment is determined according to the starting SOC and the second predicted SOC change amount of the first road segment. The upper limit value of the target SOC of the non-first road segment of the preset travel path is determined according to the first predicted SOC change amount of the non-first road segment and the upper limit value of the target SOC of the previous road segment of the non-first road segment. The lower limit value of the target SOC of the non-first road segment is determined according to the second predicted SOC change amount of the non-first road segment and the lower limit value of the target SOC of the previous road segment of the non-first road segment. The method of claim 34, wherein, The SOC of the target road segment in the preset travel path is determined according to the first predicted SOC range of the target road segment and the second predicted SOC range of the target road segment. In a case where the target road segment is the first road segment of the preset travel path, the first predicted SOC range of the target road segment is determined according to the starting SOC of the vehicle at the starting point of the preset travel path and the predicted SOC change amount of the target road segment. In a case where the target road segment is not the first road segment of the preset travel path, the first predicted SOC range of the target road segment is determined according to the upper limit value, the lower limit value of the target SOC of the previous road segment of the target road segment and the predicted SOC change amount of the target road segment. In a case where the target road segment is the last road segment of the preset travel path, the second predicted SOC range of the target road segment is the terminal SOC of the power battery when the vehicle travels to the terminal point of the preset travel path. In a case where the target road segment is not the last road segment of the preset travel path, the second predicted SOC range of the target road segment is determined according to the upper limit value, the lower limit value of the target SOC of the next road segment of the target road segment and the predicted SOC change amount of the next road segment of the target road segment. The method of claim 34, wherein, The upper limit value and the lower limit value of the target SOC of the target road segment are determined by the intersection of the first predicted SOC range of the target road segment and the second predicted SOC range of the target road segment. The method of claim 34, wherein, The terminal SOC is determined according to the starting SOC of the power battery of the vehicle at the starting point of the preset travel path. The method of claim 37, wherein, In a case where the starting SOC is greater than or equal to a first preset threshold value, the terminal SOC is a second preset threshold value; In a case where the starting SOC is less than the first preset threshold value, the terminal SOC is the first preset threshold value; The second preset threshold value is greater than the first preset threshold value. The method of any one of claims 30-38, wherein, The division of the road segments is related to the road condition information of the preset travel path, and the road segments are divided according to at least one of the road type and the congestion level of the preset travel path. The method of any one of claims 30-39, wherein, Further comprising: The preset travel path includes a start point and an end point, and if the end point of the preset travel path meets the charging condition, the end point SOC when the vehicle travels to the end point is reduced. The method of claim 40, further comprising: controlling the engine, the drive motor, the generator and the power battery according to the target SOC of each section, the actual vehicle demand and the reduced end point SOC when the vehicle travels to the end point, so that the engine works in the high-efficiency working interval. The method of claim 41, wherein, The end point of the preset travel path meeting the charging condition includes: if there is a charging address at the end point, and there is a charging pile in the idle state in the charging address, it is determined that the end point meets the charging condition. The method of any one of claims 31-42, wherein, The controlling the engine, the drive motor, the generator and the power battery according to the start SOC, the target SOC and the actual vehicle demand of each section, so that the engine works in the high-efficiency working interval, includes: if the target SOC is greater than the start SOC by a certain threshold value, and the actual vehicle demand is less than the demand for the engine to work in the high-efficiency working interval, controlling the engine to be in the high-efficiency working interval and drive the vehicle, or controlling the engine to drive the generator or the drive motor to generate electricity, and storing the excess electricity to the power battery; if the target SOC is greater than the start SOC by a certain threshold value, and the actual vehicle demand is greater than or equal to the demand for the engine to work in the high-efficiency working interval, controlling the engine to be in the high-efficiency working interval and driven by the drive motor, or driven by the drive motor and the engine together; and if the target SOC is less than the start SOC by a certain threshold value, controlling the engine to be shut down. The method of any one of claims 30-43, wherein, The determination method of the preset travel path includes: if the navigation system self-start function is turned on, and the current system time is located in the pre-set vehicle time period, automatically starting the navigation system, and determining the preset travel path according to the current location information of the vehicle; and if the navigation system self-start function is turned off, determining the preset travel path in response to the end point input by the user. The method of claim 44, wherein, The determining the preset travel path in response to the end point input by the user includes: determining at least one candidate energy-saving path based on the start point and the end point of the vehicle; wherein the path vehicle energy consumption predicted by the at least one candidate energy-saving path is less than the path vehicle energy consumption predicted by other paths, and the path vehicle energy consumption is predicted according to multi-domain data fusion information of each path; and determining the preset travel path in response to the selection operation on the at least one candidate energy-saving path; wherein the preset travel path refers to the selected candidate energy-saving path; the preset travel path includes the plurality of sections, and the path vehicle energy consumption includes the section vehicle energy consumption of the plurality of sections. The method of any one of claims 30-45, further comprising: If the remaining drivable mileage of the vehicle is less than the driving mileage to the end point, a power compensation strategy during driving of the preset travel path is determined. The method of claim 46, wherein, The determination of the power compensation strategy during driving of the preset travel path comprises: obtaining a fatigue driving mileage of a driver, wherein the fatigue driving mileage represents a drivable mileage before the driver reaches a fatigue driving state; and based on the fatigue driving mileage and the remaining drivable mileage of the vehicle, controlling the vehicle to drive to a target charging address for charging or a target refueling address for refueling. The method of any one of claims 44-47, wherein, The determination of the at least one candidate energy-saving path based on the start point and the end point of the vehicle comprises: a start point of any candidate travel path is the start point of the vehicle, and an end point of the any candidate travel path is the end point; predicting path whole-vehicle energy consumption of the vehicle on each candidate travel path in the at least one candidate travel path according to multi-domain data fusion information of the each candidate travel path; and determining the at least one candidate energy-saving path from the at least one candidate travel path based on the path whole-vehicle energy consumption of the vehicle on the each candidate travel path, wherein path whole-vehicle energy consumption of the vehicle on any candidate energy-saving path in the at least one candidate energy-saving path is less than path whole-vehicle energy consumption of the vehicle on other candidate travel paths in the at least one candidate travel path except the at least one candidate energy-saving path. The method of claim 48, wherein, The determination of the at least one candidate travel path based on the start point and the end point of the vehicle comprises: obtaining at least one drivable path from the start point to the end point of the vehicle; determining m drivable paths from the at least one drivable path based on a first travel dimension index of each drivable path in the at least one drivable path, wherein m is a positive integer, the first travel dimension index of any drivable path in the m drivable paths is less than the first travel dimension index of other drivable paths in the at least one drivable path except the m drivable paths; and determining the at least one candidate travel path from the m drivable paths based on a second travel dimension index of the m drivable paths, wherein the second travel dimension index of any candidate travel path is less than the second travel dimension index of other drivable paths in the m drivable paths except the at least one candidate travel path. The method of claim 49, wherein, The determination of the at least one candidate travel path based on the start point and the end point of the vehicle comprises: obtaining at least one drivable path from the start point to the end point of the vehicle; obtaining each travel dimension index of each drivable path, each weight of each travel dimension index corresponding to a current travel scenario of the vehicle; performing weighted operation on each travel dimension index according to each weight to obtain a travel comprehensive index of each drivable path; and According to the trip comprehensive index of each drivable path, at least one candidate driving path is screened from the at least one drivable path; wherein the trip comprehensive index of the at least one candidate driving path is less than the trip comprehensive index of other drivable paths except the at least one candidate driving path in the at least one drivable path. The method of any one of claims 30-50, wherein, The vehicle state information at least includes static parameters of the vehicle and a target vehicle speed of minimum path vehicle energy consumption, the road condition information at least includes road traffic flow speed, and the user behavior information at least includes driving style of the user; The path vehicle energy consumption of the preset trip path is predicted according to the multi-domain data fusion information, including: According to the driving style, the road traffic flow speed, the static parameters of the vehicle and the target vehicle speed of minimum path vehicle energy consumption, the path vehicle energy consumption of the preset trip path is predicted according to the energy consumption prediction algorithm of automobile theory. The method of claim 51, wherein, The target vehicle speed is determined in the following way: Taking the minimum path vehicle energy consumption as the objective function, a speed sequence is generated according to the road traffic flow speed of the preset trip path and the current vehicle speed of the vehicle at the starting point of the preset trip path, wherein the current vehicle speed is the vehicle speed of the vehicle at the starting point of the preset trip path. And The speed sequence is corrected based on the limiting conditions to obtain a corrected speed sequence, wherein the limiting conditions at least include the driving style. The method of claim 52, wherein, The limiting conditions further include one or more of the following: trip duration, traffic flow speed information, acceleration limit, deceleration limit, regional maximum allowable speed or traffic light information. The method of any one of claims 50-53, wherein, The target vehicle speed is determined in the following way: A smooth speed sequence is determined based on the road traffic flow speed of the preset trip path, the current vehicle speed and the limiting condition information, wherein the limiting conditions at least include the driving style, and the current vehicle speed is the vehicle speed of the vehicle at the starting point of the preset trip path; The smooth speed sequence is input as an initial speed solution to a vehicle model; And A speed sequence is generated according to the initial speed solution by the vehicle model taking the minimum path vehicle energy consumption as the objective function. The method of claim 54, wherein, The smooth speed sequence is determined based on the road traffic flow speed of the preset trip path, the current vehicle speed and the limiting condition information, and the smooth speed sequence is input as the initial speed solution to the vehicle model, including: Based on the road traffic flow speed of the preset trip path, the current vehicle speed and the limiting condition information, an average speed is obtained, the speed change between adjacent road sections is smoothed to obtain the smooth speed sequence; According to the driving style, the road traffic flow speed and the traffic light position information, the speed of the road section of different driving scenarios is corrected to locally correct the smooth speed sequence; and Based on the locally corrected smooth speed sequence, an initial optimization range of the vehicle model is determined, and the smooth speed sequence is input as the initial speed solution to the vehicle model. The method of claim 55, wherein, The speed correction of the road section of different driving scenarios according to the driving style, the road traffic flow speed and the traffic light position information to locally correct the smooth speed sequence, including: When the long-time following occurs and the target vehicle speed cannot be maintained, the current acceleration of the vehicle, the current vehicle speed, the obstacle speed, and the relative distance to the obstacle are input into a vehicle following model, and the vehicle following model is used to generate the locally corrected smooth speed sequence, with the minimum path total vehicle energy consumption and the relative distance to the obstacle being greater than a preset distance threshold as the objective function. The method of claim 55 or 56, wherein, The speed correction of the road section in different driving scenarios according to the driving style, the road traffic flow speed, and the traffic light position information is used to locally correct the smooth speed sequence, including: When passing through a traffic light intersection, the current acceleration of the vehicle, the current vehicle speed, traffic light information, obstacle speed, and the relative distance to the obstacle are input into an intersection vehicle speed model, and the intersection vehicle speed model is used to generate a locally corrected smooth speed sequence, with the minimum path total vehicle energy consumption and the traffic light intersection passing time being less than a preset intersection expected passing time as the objective function. A new energy vehicle energy intelligent management system, comprising: An engine configured to selectively output power to a wheel end of the vehicle; A drive motor configured to output power to the wheel end; A generator connected to the engine to generate electricity under the driving of the engine; A power battery configured to supply power to the drive motor and to be charged by alternating current output from one of the generator or the drive motor; and A control device, comprising: A multi-source data fusion module configured to obtain multi-domain data fusion information, the multi-domain data fusion information including at least cabin domain information and power domain information; wherein the cabin domain information includes at least user behavior information and road condition information of a preset travel path, and the power domain information includes at least vehicle state information; An energy consumption prediction module configured to predict path total vehicle energy consumption of the preset travel path according to the multi-domain data fusion information, the preset travel path including a plurality of road sections, and the path total vehicle energy consumption including road section total vehicle energy consumption of the plurality of road sections; A dynamic programming module configured to program target SOC of each road section in the plurality of road sections according to the road section total vehicle energy consumption of the each road section, with the lowest fuel consumption of the preset travel path as the target; An intelligent control module configured to control the engine, the drive motor, the generator, and the power battery according to the target SOC of the each road section and actual total vehicle demand, so that the engine operates in a high-efficiency operating interval. The memory, the communication interface, and the processor are connected to each other; the memory stores a computer program, and the processor invokes the computer program stored in the memory to implement the method according to any one of claims 30-56. A control device comprising a memory, a communication interface, and a processor; wherein A control device, comprising: ​ A multi-source data fusion unit is configured to acquire multi-domain data fusion information, which at least includes cabin domain information and power domain information; wherein the cabin domain information at least includes user behavior information and road condition information of a preset travel path, and the power domain information at least includes vehicle state information; An energy consumption prediction unit is configured to predict path vehicle energy consumption of the preset travel path according to the multi-domain data fusion information, the preset travel path including a plurality of road segments, and the path vehicle energy consumption including road segment vehicle energy consumption of the plurality of road segments; A dynamic programming unit is configured to program target SOC of each road segment according to road segment vehicle energy consumption of each road segment in the plurality of road segments, with the lowest fuel consumption of the preset travel path as a target; and An intelligent control unit is configured to control an engine, a drive motor, a generator and a power battery of a new energy vehicle according to target SOC and actual vehicle demand of each road segment, so that the engine works in a high-efficiency working interval. A vehicle includes a new energy vehicle energy intelligent management system according to any one of claims 1-30. A computer readable storage medium stores a computer program, which is executed by a processor to implement the method according to any one of claims 30-57. A computer program product includes a computer program, which is adapted to be loaded by a processor and execute the method according to any one of claims 30-57.

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