New energy vehicle energy intelligent management method and system, and related device

Abstract

A new energy vehicle energy intelligent management method and system, and a related device. The method comprises: determining at least one candidate energy-saving path on the basis of a starting point and an ending point of a vehicle, wherein path-based vehicle energy consumption predicted on the basis of the at least one candidate energy-saving path is lower than path-based vehicle energy consumption predicted on the basis of other paths, and the path-based vehicle energy consumption is predicted on the basis of road condition information of each path and energy consumption impact information; in response to a selection operation on the at least one candidate energy-saving path, determining a preset travel path, wherein the preset travel path comprises a plurality of road sections, and the path-based vehicle energy consumption comprises road section-based vehicle energy consumption of the plurality of road sections; and by taking the minimization of fuel consumption along the preset travel path as an objective, controlling an operation state of an engine on the basis of an initial SOC of a power battery and the road section-based vehicle energy consumption at each road section and an actual vehicle demand of the vehicle, so that the engine is in an efficient operation range during operation.

Description

Intelligent energy management methods, systems and related equipment for new energy vehicles

[0001] This application claims priority to Chinese patent application No. 202410672579.X, 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 control technology, and in particular to a method, system and related equipment for intelligent energy management of 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, method, and related equipment for new energy vehicles, which can reduce fuel consumption for users and improve the driving experience.

[0005] Firstly, some embodiments of this disclosure provide a method for intelligent energy management of new energy vehicles. The method includes: determining at least one candidate energy-saving path based on the vehicle's starting point and destination; the predicted vehicle energy consumption of the at least one candidate energy-saving path is less than the predicted vehicle energy consumption of other paths, the vehicle energy consumption being predicted based on road condition information and energy consumption impact information for each path. In response to the selection of at least one candidate energy-saving path, a preset travel path is determined; the preset travel path refers to the selected candidate energy-saving path; the preset travel path includes multiple road segments, and the vehicle energy consumption includes the segment-level vehicle energy consumption of the multiple road segments. With the goal of minimizing fuel consumption along the preset travel path, the engine's operating state is controlled based on the initial SOC of the power battery in each of the multiple road segments, the segment-level vehicle energy consumption, and the vehicle's actual overall vehicle demand, so that the engine operates within its high-efficiency operating range.

[0006] Secondly, some embodiments of this disclosure provide an intelligent energy management system for new energy vehicles. This 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; the generator is connected to the engine to generate electricity under the engine's drive; and the power battery supplies power to the drive motor and charges it based on the current output by the generator or drive motor. The control unit is used to: determine at least one candidate energy-saving path based on the vehicle's starting point and ending point; the predicted total vehicle energy consumption of at least one candidate energy-saving path is less than the predicted total vehicle energy consumption of other paths, and the total vehicle energy consumption is predicted based on road condition information and energy consumption impact information for each path. In response to the selection operation of at least one candidate energy-saving path, a preset travel path is determined; the preset travel path refers to the selected candidate energy-saving path; the preset travel path includes multiple road segments, and the total vehicle energy consumption includes the total vehicle energy consumption of the multiple road segments. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial SOC of the power battery in each section, the vehicle's overall energy consumption in that section, and the vehicle's actual overall needs, so that the engine operates in its most efficient range.

[0007] Thirdly, embodiments of this disclosure provide an intelligent energy management device for new energy vehicles, comprising: a determining unit and a controlling unit. The determining unit is used to determine at least one candidate energy-saving path based on the vehicle's starting point and destination; the predicted total vehicle energy consumption of the at least one candidate energy-saving path is lower than the predicted total vehicle energy consumption of other paths, and the total vehicle energy consumption is predicted based on road condition information and energy consumption impact information for each path. The determining unit is also used to determine a preset travel path in response to the selection operation of at least one candidate energy-saving path; the preset travel path refers to the selected candidate energy-saving path; the preset travel path includes multiple road segments, and the total vehicle energy consumption includes the total vehicle energy consumption of the multiple road segments. The controlling unit is used to control the engine's operating state based on the initial SOC of the power battery and the total vehicle energy consumption of each road segment, and the actual vehicle demand, with the goal of minimizing fuel consumption along the preset travel path, so that the engine operates within its high-efficiency operating range.

[0008] Fourthly, embodiments of this disclosure provide a control device, which includes a memory, a communication interface, and a processor, wherein the memory, the communication interface, and the 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 first aspect.

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

[0010] In a sixth aspect, embodiments of this disclosure provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0011] In a seventh aspect, embodiments of this disclosure provide a computer program product, which includes a computer program stored in a computer storage medium; a processor of a control device reads the computer program from the computer storage medium and executes the computer program, causing the control device to perform the method described in the first aspect.

[0012] In some embodiments of this disclosure, a travel route with the lowest overall vehicle energy consumption is selected based on the vehicle's origin and destination. At the same time, 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 to achieve a reasonable distribution of fuel and electricity in the hybrid electric vehicle, thereby reducing vehicle fuel consumption and operating costs. Meanwhile, by controlling the engine's operating state, the engine is kept in a high-efficiency operating range, improving the engine's NVH performance, avoiding frequent engine start-stop, and enhancing driving comfort. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments or background art of this disclosure, the accompanying drawings used in the embodiments or background art of this disclosure will be described below.

[0014] 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;

[0015] 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;

[0016] Figure 3 is a block diagram of another intelligent energy management system for new energy vehicles according to some embodiments of the present disclosure;

[0017] Figure 4 is a schematic flowchart of a new energy vehicle energy intelligent management method according to some embodiments of the present disclosure;

[0018] Figure 5 is a schematic diagram of a candidate energy-saving path determination logic according to some embodiments of the present disclosure;

[0019] Figure 6 is a schematic diagram of energy consumption prediction according to some embodiments of the present disclosure;

[0020] Figure 7 is a logical schematic diagram of an energy consumption prediction method according to some embodiments of the present disclosure;

[0021] Figure 8 is a schematic diagram of a power replenishment strategy according to some embodiments of the present disclosure;

[0022] Figure 9 is a schematic diagram of road segment division according to some embodiments of the present disclosure;

[0023] Figure 10 is a schematic diagram of a predicted SOC according to some embodiments of the present disclosure;

[0024] Figure 11 is another schematic diagram of predicted SOC according to some embodiments of the present disclosure;

[0025] Figure 12 is a schematic diagram of energy management based on historical driving data according to some embodiments of the present disclosure;

[0026] Figure 13 is a schematic diagram of a partial correction logic for traffic light information fusion according to some embodiments of the present disclosure;

[0027] Figure 14 is a schematic diagram of an automatic navigation initial time update logic according to some embodiments of the present disclosure;

[0028] Figure 15A is a schematic flowchart of another intelligent energy management method for new energy vehicles according to some embodiments of the present disclosure;

[0029] Figure 15B is a schematic flowchart of another intelligent energy management method for new energy vehicles according to some embodiments of the present disclosure;

[0030] Figure 16 is a block diagram of a vehicle according to some embodiments of the present disclosure;

[0031] Figure 17 is a schematic diagram of an energy-saving path according to some embodiments of the present disclosure;

[0032] Figure 18 is a schematic diagram of a power replenishment plan according to some embodiments of the present disclosure;

[0033] Figure 19 is a structural schematic diagram of a smart energy management device for a new energy vehicle according to some embodiments of the present disclosure;

[0034] Figure 20 is a schematic diagram of the structure of a control device according to some embodiments of the present disclosure. Detailed Implementation

[0035] 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.

[0036] 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 specific meaning of which must be determined by its interpretation in that embodiment or further in conjunction with the context of that embodiment.

[0037] It should be understood that although the terms first, second, third, etc., may be used herein to describe various information, such information should not be limited to these terms. These terms are used only 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, may be interpreted as "when," "when," or "in response to determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to also include the plural forms unless the context indicates otherwise. It should be further understood that the terms "comprising," "including," indicate the presence of a feature, step, operation, element, component, item, kind, and / or group, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, kinds, and / or groups. The terms "or," "and / or," "including at least one of," etc., 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." 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 occur when the combination of elements, functions, steps, or operations is inherently mutually exclusive in some way.

[0038] 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.

[0039] 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).”

[0040] Currently, the energy management strategies for hybrid electric vehicles in related technologies mainly 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. However, such energy management strategies, which rely solely on the vehicle's operating conditions for energy control, often lead to increased fuel consumption and higher energy consumption.

[0041] Therefore, some embodiments of this disclosure provide an intelligent energy management system for new energy vehicles.

[0042] 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 device, which includes an engine 10, a drive motor 20, a generator 30, a power battery 40, and a control device 50. The drive device is used to provide driving force for the vehicle. 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 may be implemented by the cooperation of several of the above modules; the present disclosure does not limit this.

[0043] 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 under the drive of engine 10.

[0044] The power battery 40 is used to power the drive motor 20 and to charge it according to the current output by the generator 30 or the drive motor 20.

[0045] The control device 50 is configured to: determine at least one candidate energy-saving path based on the vehicle's starting point and ending point; the predicted total vehicle energy consumption of the at least one candidate energy-saving path is less than the predicted total vehicle energy consumption of other paths, and the total vehicle energy consumption is predicted based on road condition information and energy consumption impact information of each path; in response to the selection operation of at least one candidate energy-saving path, determine a preset travel path; the preset travel path refers to the selected candidate energy-saving path; the preset travel path includes multiple road segments, and the total vehicle energy consumption includes the total vehicle energy consumption of multiple road segments; with the goal of minimizing fuel consumption of the preset travel path, control the operating state of the engine 10 based on the initial SOC of the power battery 40 of each road segment, the total vehicle energy consumption of the road segment, and the actual vehicle demand of the vehicle, so that the engine operates in a high-efficiency operating range.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] In some embodiments, 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.

[0051] 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.

[0052] 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, and the power domain control module supplies power to the drive motor 20 according to the current output by the generator 30.

[0053] 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 current output by 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.

[0054] The selective output of power from the engine to the vehicle's wheels includes: if the target SOC is greater than a certain threshold of the initial SOC, and the actual vehicle demand is less than the requirement for the engine to operate in its efficient operating range, then the engine 10 is controlled to operate in its efficient operating range and drive the vehicle; or the engine 10 is controlled to drive the generator or drive motor 20 to generate electricity, and the excess electricity is stored in the power battery 40 and output 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 requirement for the engine to operate in its efficient operating range, then the engine 10 is controlled to operate in its efficient operating range and is driven by the drive motor 20; or the drive motor 20 and the engine 10 jointly drive the vehicle. The engine's efficient operating range refers to a specific engine speed and torque range with high overall operating efficiency considering common operating conditions. The drive motor 20 outputs power to the vehicle's wheels, or the drive motor 20 and the engine 10 jointly output 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 stop, and the engine 10 does not output power to the vehicle's wheels.

[0055] In some embodiments, the intelligent energy management system for new energy vehicles may further include a transmission 70 and a main reducer 80. Figure 2 is a schematic diagram of the architecture of another intelligent energy management system for new energy vehicles according to some embodiments of the present disclosure. Referring to Figure 2, 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 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.

[0056] 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.

[0057] 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. Wheel-end torque demand is also the total vehicle torque demand.

[0058] The control unit 50 controls the engine 10, drive motor 20, and generator 30 based on driving parameters to ensure that the engine 10 operates in the economic zone by controlling the charging and discharging of the power battery 40. For example, the control unit 50 can compare the equivalent fuel consumption of the hybrid vehicle in series mode, parallel mode, and EV mode to select the operating mode with the lowest equivalent fuel consumption as the current operating mode of the hybrid vehicle.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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 exchanges travel information data with vehicles using speed planning functions within a certain range of the current road segment.

[0065] It should be noted that speed planning refers to the function of calculating the target speed. The target speed is determined in the following way: with the goal of minimizing the overall energy consumption of the vehicle along the route, a speed sequence is generated based on the road traffic flow speed of 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 corrected based on constraints, which include at least driving style.

[0066] The corrected speed sequence is the target vehicle speed, which is the optimal energy-saving speed. 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 leveraging the travel information transmitted from nearby vehicles, the dimensionality and accuracy of the input information are improved.

[0067] When navigation and route finding are enabled, the system collects interaction data from nearby vehicles to correct navigation information. This interaction data refers to vehicle-to-vehicle (V2V) data, with vehicle speed being the most commonly used data. This information can be communicated to surrounding vehicles via short-range wireless communication, enabling platooning and short-range predictive control. Alternatively, vehicle-to-cloud (V2V) communication via wireless cloud services can supplement map navigation with speed and energy consumption predictions. The system proactively identifies special road conditions such as traffic lights, long uphill sections, and congested traffic, updating vehicle speed and SOC planning to ensure efficient vehicle operation. When navigation and route finding are disabled, the system collects interaction data from nearby vehicles and combines it with historical vehicle data and surrounding information from sensors such as LiDAR, millimeter-wave radar, and cameras to make short-term predictions of future travel. Based on these short-term predictions, it performs optimization calculations to minimize energy consumption and reduce fuel consumption for the user.

[0068] During the trip, when the vehicle leaves the current road segment, please refer to Figure 3. Figure 3 is a block diagram of another new energy vehicle energy intelligent management system according to some embodiments of this disclosure. Through the "vehicle-cloud" communication method, historical travel information is uploaded 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.

[0069] 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. These include 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, 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 theoretical and data models with variable weights to predict the vehicle's energy consumption for a user's preset route. For example, spatiotemporal alignment refers to using the preset travel route (distance or time) as the coordinate axis for multi-source information. 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 sequentially.

[0070] 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.

[0071] 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.

[0072] Based on the above description, Figure 4 is a schematic flowchart of a new energy vehicle energy intelligent management method according to some embodiments of the present disclosure. As shown in Figure 4, the new energy vehicle energy intelligent management method includes, but is not limited to, steps S401-S403.

[0073] S401. Based on the vehicle's starting point and ending point, at least one candidate energy-saving path is determined; the total vehicle energy consumption predicted by at least one candidate energy-saving path is less than the total vehicle energy consumption predicted by other paths. The total vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path.

[0074] Candidate energy-saving paths can be determined in the following ways:

[0075] In some embodiments, at least one candidate energy-saving path is determined based on the vehicle's starting point and ending point: at least one candidate driving 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 road condition information and energy consumption impact 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.

[0076] In some embodiments, based on user behavior information and road condition information and energy consumption impact information for each candidate driving path, such as energy consumption impact information including the user's driving style being aggressive driving, and road condition information including the gradient of the candidate driving path and the vehicle's speed, the vehicle's total energy consumption on each candidate driving path is predicted, and based on the vehicle's total energy consumption on each candidate driving path, at least one candidate energy-saving path is determined from at least one candidate driving path.

[0077] 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.

[0078] In some embodiments, please refer to Figure 5, which is a schematic diagram of a candidate energy-saving route determination logic according to some embodiments of the present disclosure. Based on the navigation destination and the vehicle's current location, the route combination model obtains routes 1 to n, and combines these N routes according to road factor indicators, such as time factors or distance factors. Combining the vehicle's remaining mileage and historical data samples, the route filtering model retains and displays the n routes according to a comprehensive weight score, for example, retaining 3 routes, route 1, route 2, and route 3. Information on each segment of different routes (road speed limit, distance length, gradient), traffic information (traffic flow speed), and vehicle information are extracted to predict energy consumption and obtain the route with the latest energy consumption.

[0079] For example, different candidate routes are selected primarily based on the vehicle's current location and the user's navigation destination, taking into account travel factors from the starting point to the destination. These factors include estimated travel distance, estimated energy consumption, and estimated road conditions. Big data analysis is conducted based on user travel experience and actual traffic flow impacts. The most influential factor is selected as the first travel dimension indicator. The selection of the first travel dimension indicator aims to complete the trip; for example, path distance is chosen as the first travel dimension indicator based on the principle of shortest travel distance. Based on the road connectivity in the road network, travel routes are arranged and combined according to the first travel dimension indicator to determine m drivable routes. Simultaneously, to satisfy the second travel dimension indicator, such as the shortest travel time, the route with the shortest travel time among the current combinations is selected. At least one candidate route is determined from the m drivable routes.

[0080] In some embodiments, the first travel dimension indicator includes travel distance, and the second travel dimension indicator includes travel time.

[0081] 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.

[0082] 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. 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.

[0083] 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.

[0084] In some embodiments, please refer to Figure 5. 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 the time weight is larger and the distance and energy consumption weight is smaller. The comprehensive score of the candidate route is obtained by weighted calculation, and a certain number of candidate driving paths are retained according to the score.

[0085] For example, the vehicle energy consumption predicted by at least one candidate energy-saving path is less than the vehicle energy consumption predicted by other paths. The vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path. The vehicle energy consumption can include any one of the following five methods.

[0086] For example, please refer to Figure 6. Figure 6 is a schematic diagram of energy consumption prediction according to some embodiments of this disclosure. Figure 6a is a schematic diagram of speed information revealed by a map. 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 6b is a schematic diagram of energy consumption prediction based on map-revealed data. Since speed is discrete, energy consumption is directly related to speed, so energy consumption is also discrete. Figure 6c is a schematic diagram of the result of speed planning based on map-revealed speed information. Speed ​​planning is to control the vehicle to travel at a planned speed, so a continuous speed can reduce energy consumption. Therefore, the planning is discrete. Figure 6d is a schematic diagram of the result of energy consumption prediction based on the planned speed.

[0087] For example, for the steps of energy consumption prediction based on map-revealed data, please refer to steps 1, 2, or 3 below; for the steps of energy consumption prediction based on planned speed, please refer to the steps corresponding to step 4 below.

[0088] 1. Based on the energy consumption prediction algorithm of automotive theory, the total energy consumption of the vehicle along the preset travel route is predicted according to the road traffic flow speed and the static parameters of the vehicle. The total energy consumption of the vehicle along the route is the theoretical energy consumption required.

[0089] In some embodiments, energy consumption impact information includes vehicle status information, which includes at least the vehicle's static parameters, and road condition information includes at least the road traffic flow speed. The total vehicle energy consumption along a route is predicted based on the road condition information and energy consumption impact information for each route. This can be achieved by: predicting the total vehicle energy consumption of a preset travel route based on the road traffic flow speed and the vehicle's static parameters using an energy consumption prediction algorithm according to automotive theory. The total vehicle energy consumption along a route is the theoretical energy consumption requirement.

[0090] In some embodiments, the static parameters of the vehicle include at least: wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

[0091] 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.

[0092] In some embodiments, please refer to FIG7, which is a schematic diagram of the logic of an energy consumption prediction method according to some embodiments of the present disclosure. The energy consumption prediction method predicts the energy consumption of travel routes based on the prediction of operating conditions and the fusion of vehicle theory and data-driven approaches.

[0093] For example, automotive 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 can also include slope information. Vehicle static parameters include wind resistance, rolling resistance, acceleration resistance, and slope resistance. Vehicle static parameters can also include inherent vehicle parameters that affect energy consumption, such as driving speed, curb weight, and frontal area.

[0094] For example, by acquiring vehicle parameters and combining them with load changes, if the vehicle is equipped with an inertial measurement unit (IMU), the acceleration can be directly acquired; if the vehicle does not have an IMU, the acceleration is estimated based on the vehicle speed. Combined with throttle torque and vehicle acceleration, energy consumption prediction theory is performed, which is the theoretical energy consumption prediction of automobiles.

[0095] Through offline training, 12 energy consumption prediction models are formed by combining two dimensions: a certain vehicle model, driving style, and driving conditions. The model is matched with the driving conditions and driving style, and energy consumption is predicted. If the predicted energy consumption is greater than the upper limit and the predicted energy consumption is the actual energy consumption data, then the energy consumption prediction model is determined. The energy consumption prediction model is trained in the cloud based on the actual energy consumption data, and the model parameters are updated.

[0096] If the predicted energy consumption does not exceed the upper limit, energy consumption prediction is performed through theoretical calculations and data-driven predictions. The theoretical calculations are based on automotive theory, while the data-driven predictions are based on the target energy consumption prediction model. Based on these calculations, if the actual energy consumption is within a certain range, the weights are adjusted. If the actual energy consumption is outside this range, the model matching is checked. If incorrect, it is re-matched among 12 energy consumption prediction models; if correct, the model is uploaded to the cloud server for retraining, and the parameters are updated on the vehicle.

[0097] Among them, the energy consumption prediction algorithm of automobile theory is composed of F t =F f +F w +F i +F j It is derived that 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. 2 In 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 directly obtained; if the vehicle does not have an IMU, the acceleration is estimated from the vehicle speed.

[0098] 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. 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.

[0099] In some embodiments, energy consumption impact information includes vehicle status information, which includes at least the user's driving style and vehicle model information, and road condition information, which includes at least the road type. The total vehicle energy consumption along a route is predicted based on the road condition information and energy consumption impact information for each route. This can be achieved by: inputting the road type, driving style, and vehicle model information into a target energy consumption prediction model, and outputting the predicted total vehicle energy consumption for the preset travel route from the target energy consumption prediction model. The total vehicle energy consumption along the route is a reference demand energy consumption. The target energy consumption prediction model is determined from multiple preset energy consumption prediction models based on at least one of the road type or the user's driving style information for the preset travel route.

[0100] 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.

[0101] 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.

[0102] In some embodiments, road types include: ordinary roads, expressways, highways, and congested roads.

[0103] 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.

[0104] In some embodiments, as shown in Figure 7, due to significant differences in energy consumption among different vehicles and drivers, a two-dimensional clustering analysis is performed on all driving behavior data for a specific vehicle model, categorized by driving style and driving conditions. Driving conditions are divided into ordinary roads, expressways, highways, and congested roads. Driving styles are categorized as aggressive, normal, and mild based on the rate of change of accelerator pedal opening and acceleration. This two-dimensional cross-splitting results in 12 groups of driving data for that vehicle model. Based on these 12 groups, algorithms such as random forests are used to train energy consumption prediction models offline, resulting in 12 models with different parameters representing energy consumption prediction models under different classification groups. The obtained models are compressed and deployed in the vehicle controller, where a driving style recognition algorithm is deployed to dynamically identify the driver's driving style and driving conditions, and to call the corresponding model for energy consumption prediction of the travel route.

[0105] 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 are uploaded to the cloud, triggering cloud-based prediction model training, updating the corresponding energy consumption prediction model, and achieving closed-loop data learning.

[0106] 3. Based on the theoretical and reference energy consumption requirements of the vehicle on the preset travel route, the total energy consumption of the vehicle on the preset travel route is predicted.

[0107] The theoretical energy consumption requirement is calculated using an energy consumption prediction algorithm based on automotive theory. The reference energy consumption requirement is then obtained by outputting the target energy consumption prediction model. The theoretical energy consumption requirement and the reference energy consumption requirement are weighted and added together to predict the total vehicle energy consumption for the preset travel route.

[0108] In some embodiments, vehicle status information includes at least vehicle static parameters and vehicle model information, user behavior information includes at least user driving style, and road condition information includes at least road traffic flow speed and road type; theoretical energy consumption is obtained through the following steps: according to the energy consumption prediction algorithm of automobile theory, based on road traffic flow speed and vehicle static parameters, predict the total vehicle energy consumption of the preset travel route, and the total vehicle energy consumption of the route is the theoretical energy consumption.

[0109] The reference demand energy consumption is obtained through the following steps: inputting road type, driving style and vehicle type information into the target energy consumption prediction model, and outputting the predicted whole vehicle energy consumption of the preset travel route from the target energy consumption prediction model. The whole vehicle energy consumption of the route is the reference demand energy consumption. 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.

[0110] In some embodiments, the total energy consumption of a vehicle along a preset travel route can be predicted based on the vehicle's theoretical energy consumption and reference energy consumption along the preset travel route by: obtaining a first weight of the vehicle's theoretical energy consumption and a second weight of the reference energy consumption; and performing a weighted calculation on the vehicle's theoretical energy consumption and reference energy consumption based on the first weight and the second weight to predict the vehicle's total energy consumption along the route.

[0111] 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.

[0112] 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.

[0113] In some embodiments, if the error between the predicted vehicle energy consumption on the nth road segment and the actual vehicle energy consumption on the nth road segment is greater than a certain threshold, 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 so that the server can optimize the energy consumption prediction model corresponding to the model identifier based on the actual vehicle energy consumption on the nth road segment.

[0114] 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.

[0115] In some embodiments, the division of road segments is related to the traffic information of a preset travel route; each road segment is divided according to at least one of the road type and congestion level of the preset travel route.

[0116] In some embodiments, the road condition information may include road type, congestion level, etc., and the preset travel route may be divided into multiple road segments according to the road type and congestion level; according to the road type, it may be divided into urban road segments, rural road segments, etc., and according to the congestion level, it may be divided into expressway segments or congested road segments, etc.

[0117] In some embodiments, road types include at least: ordinary roads, expressways, highways, and congested roads; the user's driving style is divided into at least three categories: aggressive, normal, and mild, based on the rate of change of accelerator pedal opening and the rate of change of acceleration.

[0118] In some embodiments, the vehicle's total energy consumption on road segment n is predicted in the following manner:

[0119] 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.

[0120] 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, where 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In some embodiments, please refer to Figure 7, 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 and ω2 ∈ [0.2, 0.8]. Every 5 km, 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 segment 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 this weight. When the actual vehicle energy consumption E on the road segment 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 to retrain this type of energy consumption model and update the parameters to the vehicle until the target energy consumption prediction model meets the preset conditions. Then update the parameters of this type of energy consumption prediction model. If training is complete, distribute the model offline to the vehicle; otherwise, continue using the parameters of this type of energy consumption prediction model. When E 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.

[0129] 4. Based on the energy consumption prediction algorithm of automotive theory, predict the total energy consumption of the preset travel route according to the road traffic flow speed, vehicle static parameters and the target speed that minimizes the total energy consumption of the vehicle along the route.

[0130] In some embodiments, energy consumption impact information includes vehicle status information, which 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. The overall vehicle energy consumption along the route is predicted based on the road condition information and energy consumption impact information for each route. This can be achieved by: using an energy consumption prediction algorithm based on automotive theory, and predicting the overall vehicle energy consumption of a preset travel route based on the road traffic flow speed, the vehicle's static parameters, and the target speed that minimizes the overall vehicle energy consumption along the route.

[0131] In some embodiments, according to the energy consumption prediction algorithm of automotive theory, the total energy consumption of a preset travel route is predicted based on driving style, road traffic flow speed, vehicle static parameters, and the target vehicle speed that minimizes the total energy consumption of the route.

[0132] 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.

[0133] In some embodiments, 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, average vehicle speed, gradient, traffic light information, and weather information; energy consumption impact information includes vehicle status information, or energy consumption impact information includes at least one of user driving style information or traffic light information and vehicle status information; the actual vehicle demand for each road segment includes: the total vehicle power required for the vehicle to travel on each road segment.

[0134] 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 energy consumption of a preset travel route based on road traffic flow speed, vehicle static parameters, and the target speed that minimizes the total energy consumption of the route; or, when the intelligent driving function is activated and navigation-assisted driving function is activated, an energy consumption prediction algorithm based on automotive theory is triggered to predict the total energy consumption of a preset travel route based on road traffic flow speed, vehicle static parameters, and the target speed that minimizes the total energy consumption of the route; or, when the intelligent driving function is activated, navigation-assisted driving function is deactivated, adaptive cruise control function is activated, there are no vehicles ahead, and energy-saving driving guidance function is activated, an energy consumption prediction algorithm based on automotive theory is triggered to predict the total energy consumption of a preset travel route based on road traffic flow speed, vehicle static parameters, and the target speed that minimizes the total energy consumption of the route.

[0135] 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 energy consumption of a preset travel route based on road traffic flow speed, vehicle static parameters, and the target vehicle speed that minimizes the total energy consumption of the route.

[0136] 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.

[0137] For example, the target vehicle speed is determined in the following way: with the goal of minimizing the overall vehicle energy consumption along the route, a speed sequence is generated based on the road traffic flow speed of 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.

[0138] In some embodiments, the speed sequence is modified based on constraints, including at least driving style, to obtain a modified speed sequence.

[0139] 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.

[0140] In some embodiments, the limiting conditions include one or more of the following: travel time, acceleration limit, deceleration limit, maximum permissible speed in the area, traffic light information, and the driver's driving style. For example, traffic light information includes: traffic light countdown, distance to the traffic light, etc.; the maximum permissible speed in the area is the road segment speed limit; acceleration limit, deceleration limit, etc., can be determined by traffic flow speed; and the limiting 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.

[0141] 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.

[0142] In some embodiments, the speed sequence is modified by limiting conditions to obtain a modified speed sequence, which is the target speed, and the target speed is the optimal energy-saving speed.

[0143] 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 information includes 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 is used to generate a speed sequence based on the initial speed solution, with the objective function of minimizing the overall vehicle energy consumption along the route.

[0144] 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.

[0145] The control input for 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 achieves fitting to 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.

[0146] 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.

[0147] 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.

[0148] 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|;

[0149] 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.

[0150] 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 vehicle 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 vehicle speed sequence in the target time domain.

[0151] 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.

[0152] 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.

[0153] 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 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; using the intersection speed model as the objective function to minimize the overall vehicle energy consumption along the path and ensure that the passage time through the traffic light intersection is less than the preset expected passage time, and generating a locally corrected smooth speed sequence.

[0154] 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.

[0155] 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; wherein, the historical database stores the total vehicle energy consumption of at least one driving route within a historical time period.

[0156] In some embodiments, as shown in Figure 5, a data-driven road feature sample database is constructed by collecting user historical data samples, i.e., historical road data. The road feature sample database records historical road information. By comparing the current road type with the historical data features, if the current road matches the historical data features, the corresponding historical route is directly extracted and output. 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.

[0157] 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.

[0158] In some embodiments, information on each segment of different routes, traffic information, and vehicle information are extracted. The information on each segment includes information such as road speed limits, distance length, and gradient. The traffic information includes traffic flow and vehicle speed information. The data are input into the energy consumption prediction model to obtain corresponding future energy consumption prediction feedback. If the route passes through a highway, the toll fee is calculated, and the fuel price at that time is used to convert it into fuel consumption and added to the energy cost. The route with the lowest energy consumption is selected as the output and displayed to the user.

[0159] S402, in response to the selection operation of at least one candidate energy-saving route, a preset travel route is determined; wherein, 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 of the route includes the total vehicle energy consumption of the multiple road segments.

[0160] 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.

[0161] 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.

[0162] In some embodiments, if the navigation system auto-start function is disabled, a preset travel route is determined in response to the user's input destination.

[0163] For example, when determining a preset travel route, it is also necessary to consider the vehicle's remaining mileage and the mileage to the destination. If the vehicle's remaining mileage is less than the mileage to the destination, then a refueling strategy is determined during the journey along the preset travel route. That is, when the mileage to the destination is greater than the vehicle's remaining mileage L based on predicted energy consumption, a refueling strategy is determined during the journey along the preset travel route.

[0164] In some embodiments, determining a refueling strategy during a preset travel route can be achieved by: obtaining the driver's fatigue driving mileage; wherein the fatigue driving mileage represents the mileage that the driver can drive before reaching a fatigue driving state; and based on the fatigue driving mileage and the vehicle's remaining mileage, recommending that the vehicle drive to a target charging address for charging or a target refueling address for refueling.

[0165] In some embodiments, please refer to Figure 8, which is a schematic diagram of a power replenishment strategy according to an embodiment of this disclosure. When the mileage to the destination is greater than the vehicle's remaining combined fuel and electric range L based on predicted energy consumption, the route with the lowest energy consumption is determined. Based on historical driving data, the driver's longest driving mileage Lmax is obtained, and candidate charging addresses N1, N2, N3...Nn and candidate refueling addresses M1, M2, M3...Mm are searched along the route. The remaining combined fuel and electric range L is updated based on energy consumption prediction.

[0166] M1, M2, M3...Mm, and N1, N2, N3...Nn are based on the distance from the driver's fatigue rest location. By judging the driver's fatigue mileage, the mileage of the charging address distribution, and the remaining combined fuel and electric mileage, the system plans refueling and charging, and displays the recommended charging and refueling plan on the vehicle's navigation screen. The driver's fatigue mileage, i.e. the driver's maximum driving mileage, is obtained based on driving history data to obtain the driver's longest driving mileage Lmax; the remaining combined fuel and electric mileage is updated by the energy consumption prediction method.

[0167] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, it is recommended that the vehicle drive to a target charging address for charging or a target refueling address for refueling. This 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.

[0168] In some embodiments, as shown in Figure 8, the first preset distance threshold is a first threshold, and the first charging address is N1. When the remaining electric driving range L is greater than Lmax, it is further determined that N1-Lmax is less than the first threshold. If it is less, charging is performed at the nearest charging address N1. When it is less than the first threshold, the driver's fatigue driving range can be ignored, and charging is performed at the nearest charging address N1.

[0169] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, it is recommended that the vehicle drive to a target charging address for charging or a target refueling address for refueling. This 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; wherein, 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.

[0170] In some embodiments, as shown in Figure 8, when the remaining driving range L is greater than Lmax, and N1 - Lmax is greater than or equal to a first threshold, charging is performed at the corresponding charging address above N1, i.e., the second charging address. When the remaining driving range is greater than or equal to the first threshold, fatigue driving mileage cannot be ignored, and charging is performed at the corresponding charging address above N1 to ensure driving safety.

[0171] In some embodiments, based on fatigue driving mileage and the vehicle's remaining mileage, it is recommended that the vehicle drive to a target charging address for charging or a target refueling address for refueling. This 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.

[0172] For example, the third charging address is located before the end of the remaining driving range, and the distance between the third charging address and the end of the remaining driving range is less than the distance between other charging addresses and the end of the fatigue driving range. Other charging addresses represent the remaining charging addresses other than the third charging address among the charging addresses located before the end of the remaining driving range.

[0173] In some embodiments, as shown in Figure 8, the second preset distance threshold is the second threshold. When the remaining electric driving range L is less than Lmax, it is further determined that L remaining - Lmax is less than the second threshold. If it is less, the vehicle is charged at the nearest charging address before L remaining. If it is less than the second threshold, charging is prioritized to ensure driving safety.

[0174] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, it is recommended that the vehicle drive to a target charging address for charging or a target refueling address for refueling. This 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.

[0175] 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 represent the other refueling addresses other than the target refueling address among the refueling addresses located before the end of the remaining driving range.

[0176] In some embodiments, as shown in Figure 8, when the remaining driving range Lremaining is less than Lmax, and Lremaining - Lmax is greater than or equal to a second threshold, refueling is performed at the nearest refueling address preceding Lremaining. If Lremaining is greater than or equal to the second threshold, refueling is performed to ensure the shortest possible travel time.

[0177] The S403 aims to minimize fuel consumption along a preset travel route. It controls the engine's operating state based on the initial SOC of the power battery in each road segment, the vehicle's overall energy consumption in that segment, and the vehicle's actual overall needs, ensuring that the engine operates within its high-efficiency range.

[0178] In some embodiments, with the goal of minimizing fuel consumption along a preset travel route, the engine's operating state is controlled based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, and the vehicle's actual overall needs, so that the engine's speed and torque fall within the efficient operating range.

[0179] In some embodiments, with the goal of minimizing fuel consumption along a preset travel route, the engine's operating state is controlled based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, and the vehicle's actual overall demand. This ensures that the engine operates within its efficient operating range. This can be achieved by: setting the goal of minimizing fuel consumption along a preset travel route and planning the target SOC for each road segment based on the initial SOC of the power battery for that segment and the vehicle's overall energy consumption for that segment; and controlling the engine's operating state based on the initial SOC, target SOC, and actual vehicle demand for each road segment, ensuring that the engine operates within its efficient operating range.

[0180] 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, each SOC change path including 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.

[0181] 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.

[0182] In some embodiments, the target SOC at the end of the first segment of the preset travel path is determined based on the vehicle's initial SOC on the preset travel path and the predicted SOC change of the first segment; the target SOC at the end of a non-first segment of the preset travel path is determined based on the predicted SOC change of the non-first segment and the target SOC at the end of the segment preceding the non-first segment.

[0183] In some embodiments, the target SOC at the end of the first segment is determined based on the vehicle's initial SOC along the preset travel path and the predicted SOC change for the first segment. The predicted SOC change refers to the change in battery charge after the vehicle has traveled the first segment. By combining the initial SOC and the predicted SOC change, the target SOC at the end of the first segment can be calculated. For segments other than the first segment, the target SOC is determined based on the predicted SOC change for that segment, i.e., the change in battery charge after the vehicle has traveled 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 determining the target SOC for the current segment. By combining the predicted SOC change and the target SOC of the previous segment, the target SOC at the end of segments other than the first segment can be determined.

[0184] 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.

[0185] 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.

[0186] 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 in that segment, ensuring that the current state of the battery at the start of the trip and the predicted energy consumption of that segment are considered. If the target segment is not the first segment of the preset travel route, then the first predicted SOC range needs to combine the upper and lower limits of the target SOC of the previous segment, as well as the predicted SOC change of the target segment, ensuring that the impact of the previous leg of the journey is considered when calculating the target SOC. 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 battery at the end of the trip is considered when determining the second predicted SOC range. If the target segment is not the last 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 segment following the target segment, as well as the predicted SOC change of the segment following the target segment, ensuring that the expected impact of the subsequent leg of the journey is considered when calculating the target SOC.

[0187] 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.

[0188] 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.

[0189] 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, the NVH power limit is lower because the road surface is relatively flat and the engine noise and vibration are relatively small, assuming it is 5 kW. Assuming the maximum charging and discharging power of the vehicle's power battery is 50 kW, the charging and discharging power range on highways is determined to be 5 kW to 50 kW.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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%.

[0195] In some embodiments, the vehicle’s preset travel route is divided into at least one road segment.

[0196] Determine the target state of charge (SOC) of the vehicle when it is traveling on each road segment.

[0197] The vehicle's engine and motor are controlled based on the actual and target SOC of the vehicle's power battery.

[0198] 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.

[0199] 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.

[0200] When the target road segment is the first 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.

[0201] 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 preceding road segment and the predicted SOC change of the target road segment.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] For example, after determining a preset travel route, the sub-segments belonging to that route and the corresponding traffic information for each sub-segment can be obtained from the map. Then, based on the traffic information of each sub-segment, several adjacent sub-segments can be selected and spliced ​​together to obtain a single road segment.

[0208] 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. Based on the traffic 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; and 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 traffic conditions, based on the traffic 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 traffic conditions.

[0209] 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).

[0210] 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.

[0211] For example, road types can include ordinary roads, expressways, highways, and congested roads. Congestion levels can be categorized into high, medium, and low to reflect different degrees of road congestion.

[0212] The travel time required is the time it takes for a vehicle to travel from the beginning to the end of a sub-segment. This time can be obtained through big data analysis based on historical data of multiple vehicles traveling on the sub-segment.

[0213] The average vehicle speed is the average speed of a vehicle traveling on a sub-road segment. For example, it could be the average speed of a vehicle that has previously traveled on a sub-road segment while performing the vehicle control method described above, or it could be the average speed of multiple vehicles traveling on a sub-road segment. For instance, if vehicle 1's average speed on a sub-road segment is 10 m / s, vehicle 2's average speed on a sub-road segment is 11 m / s, and vehicle 3's average speed on a sub-road segment is 9 m / s, 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] In some embodiments of this disclosure, a vehicle or server may divide a preset travel route into at least one road segment, and the vehicle obtains the segmentation result. During the segmentation process, determining any given road segment can be achieved by: concatenating at least two adjacent sub-road segments of the same type into 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 concatenating the adjacent one or more sub-road segments with the pre-divided road segment to obtain one road segment from the preset travel route.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] Road characteristic parameters include, for example, average vehicle speed, average acceleration, speed standard deviation, and acceleration standard deviation. First, based on big data analysis, historical driving parameters such as vehicle speed and acceleration during the passage of vehicles through road section a are obtained. Then, the average vehicle speed, average acceleration, speed standard deviation, and acceleration standard deviation for vehicles passing through road section a are calculated using the mean and standard deviation formulas. These are compared with pre-stored road condition data corresponding to preset road conditions. When the calculated road characteristic parameters fall 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] In some embodiments of this disclosure, when the vehicle is at the starting point of the preset travel route, the actual SOC of the power battery is the aforementioned initial SOC. Figure 9 is a schematic diagram of road segment division according to some embodiments of this disclosure. As shown in Figure 9, the preset travel route, i.e. the pre-driving road, 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.

[0241] In other words, when the vehicle reaches point A, the actual State of Charge (SOC) of the power battery is the initial SOC of the preset travel route. Road condition information reflects the road conditions of the corresponding road segments. Based on the initial SOC and the road condition information for each road segment, the target SOC for each segment can be determined. When the vehicle is traveling on a certain road segment, it utilizes the power battery's charge with the target SOC of that segment in mind, ensuring that when the vehicle completes the segment, the remaining charge of the power battery is close to the target SOC. Thus, by managing the vehicle's power battery charge through the road conditions of each road segment, energy consumption can be effectively reduced.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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 total vehicle energy consumption of the preset travel route based on the road condition information, user behavior information and vehicle status 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 total vehicle energy consumption of the road segment with the goal of minimizing the fuel consumption of the preset travel route.

[0247] In some embodiments, the preset travel route includes intercity highways, urban roads, and a short section of rural roads. The initial SOC of the power battery is 60%. Based on road condition information, user behavior information, and vehicle status information of the preset travel route, the predicted vehicle energy consumption is 480 kWh for the highway portion of the route, 240 kWh for the urban road portion, and 180 kWh for the rural road portion.

[0248] Based on the initial SOC of the power battery and the vehicle energy consumption of each road segment, with the goal of minimizing fuel consumption for the preset travel route, the target SOC of the power battery for each road segment is determined. For example, for highways: with the goal of minimizing fuel consumption, energy consumption needs to be reduced as much as possible. For example, the target is to keep the SOC above 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 can be to keep the SOC above 60%. Rural roads have lower energy consumption but may have more complex road conditions, requiring a certain SOC reserve. For example, the target is to keep the SOC above 55%.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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 segment. After determining the target SOC of the 5th segment, the target SOC of the 4th segment can be calculated based on the target SOC of the 5th segment and the change in SOC of the 5th segment. Then, based on the target SOC of the 4th segment and the change in SOC of the 4th segment, the target SOC of the 3rd segment can be calculated, and so on, to calculate the target SOCs of the 3rd, 2nd, and 1st segments. For example, if the target SOC of the 4th segment is 40% and the change in SOC of the 4th segment is 5%, then the target SOC of the 3rd segment = the target SOC of the 4th segment - the change in SOC of the 4th segment = 40% - 5% = 35%.

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] 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.

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] For example, if there are 5 road segments, randomly selecting one SOC from the target SOC of each road segment results in a set of SOCs, which comprises 5 SOCs. This set of SOCs constitutes a SOC change path. After determining multiple SOC change paths from the target SOCs, a target SOC change path can be selected that minimizes the vehicle's energy consumption during the preset travel route. For instance, a simulation model can be used to determine which SOC change path minimizes the vehicle's energy consumption during the preset travel route.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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.

[0275] 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.

[0276] Figure 10 is a schematic diagram of predicted SOC according to some embodiments of the present disclosure. Taking the road segment division shown in Figure 10 as an example, the first road segment is road segment 1 corresponding to segment AB. As shown in Figure 10, the initial SOC of the vehicle at point A is F. Taking the vehicle in hybrid mode (i.e., the vehicle uses fuel and the battery is charging in road segment 1 from point A to point B), the first SOC at point B is determined to be G, that is, the upper limit of the battery SOC in road segment 1 is G. Taking the vehicle in pure electric mode (i.e., the vehicle uses electricity and the battery is discharging in road segment 1 from point A to point B), the second SOC at point B is determined to be I, that is, the lower limit of the battery SOC in road segment 1 is I. Thus, the battery variation range in road segment 1 can be determined to be [I, G]. Assuming that the actual battery SOC F in 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 in road segment 1 is [65%, 75%].

[0277] Then, based on the second operating condition data corresponding to road segment 2 and the target SOC of the battery corresponding to the preceding road segment, i.e., road segment 1, the target SOC of the vehicle under road segment 2 is determined. First, the upper limit of the battery SOC of road segment 1, G, is used as the initial battery SOC of road segment 2. Assuming the vehicle uses hybrid mode (i.e., the vehicle uses fuel entirely and the battery is charging) under road segment 2, the third SOC at point C is determined to be J, meaning the upper limit of the battery SOC of road segment 2 is J. Then, the lower limit of the battery SOC of road segment 1, I, is used as the initial battery SOC of road segment 2. Assuming the vehicle uses pure electric mode (i.e., the vehicle is fully charged and the battery is discharging) under road segment 2 from point B to point C, the fourth SOC at point C is determined to be L, meaning the lower limit of the battery SOC of road segment 2 is L. Therefore, the target battery SOC under road segment 2 is determined to be [L, J].

[0278] 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.

[0279] Considering battery characteristics, when a vehicle reaches the end of a preset travel route, the remaining charge of the power battery needs to remain within a certain range, such as 17%-25%. Based on this, the final SOC of the power battery when the vehicle reaches the end of the preset travel route can be determined based on the initial SOC. With the initial and final SOCs determined, the target SOC for each road segment can be determined based on the initial SOC, the final SOC, and road condition information for each segment.

[0280] 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.

[0281] 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.

[0282] 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.

[0283] 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.

[0284] 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 the preset travel route 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 endpoint SOC of the preset travel route.

[0285] Figure 11 is another schematic diagram of predicted SOC according to some embodiments of the present disclosure. As shown in Figure 11, the final target SOC is obtained. For example, the starting SOC of the preset travel route is F, and the ending SOC of the preset travel route is U. For example, in Figure 10, the first road segment is road segment 1 corresponding to segment AB, and the second road segment is road segment 2 corresponding to segment BC. Assuming that the first target SOC at the end of road segment 1 is [65%, 75%], and the second target SOC at the beginning of road segment 2 is [60%, 70%], then after taking the intersection, the change range at the end of road segment 1 is [65%, 70%].

[0286] 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.

[0287] 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.

[0288] 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.

[0289] The vehicle's total energy consumption on the road segment is determined based on the road condition information. The second target SOC at the start of the road segment is determined based on the road condition information, the final SOC, the total energy consumption, the engine's NVH limit power, and the maximum charging and discharging power of the power battery.

[0290] 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.

[0291] 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.

[0292] 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. Similarly, 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. Further, 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. Similarly, 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. This process can be repeated to calculate the first target SOC at the end of each road segment.

[0293] 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.

[0294] 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.

[0295] 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.

[0296] 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.

[0297] 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.

[0298] 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.

[0299] In some embodiments, the aforementioned road condition information, driving style information, vehicle status, and user vehicle setting habits can be input together into the target energy consumption prediction model to obtain the vehicle energy consumption for the road segment output by the model, thereby improving the accuracy of the prediction results. For example, vehicle status includes vehicle weight, drag coefficient, rolling resistance coefficient, tire pressure, etc. Vehicle setting habits can include air conditioning settings.

[0300] 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.

[0301] For example, continuing to refer to Figure 11, 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.

[0302] 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.

[0303] 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 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. For numerical calculations, the feasible region needs to be discretized, i.e., the target SOC of each road segment is discretized.

[0304] For example, it can be discretized at equal intervals. If the difference between the maximum and minimum values ​​of a certain segment of SOC is greater than 0.005, it is discretized at intervals of 0.005; if the difference between the maximum and minimum values ​​of a certain segment of SOC is less than 0.005, the SOC is 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.

[0305] 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.

[0306] 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.

[0307] For example, the second preset threshold could be a pre-calibrated power supply SOC of 25%, and the first preset threshold could be a pre-calibrated minimum allowable SOC of 17%. It should be understood that 25% and 17% here are merely examples, and the values ​​can be adjusted according to actual circumstances. If the starting SOC of the preset travel route is greater than or equal to 17%, then 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%, then the ending SOC of the preset travel route is determined to be 17%.

[0308] 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.

[0309] 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.

[0310] In some embodiments, the non-pure electric mode may include a hybrid mode (where the internal combustion engine and electric motor work together as a power source). Alternatively, the non-pure electric mode may include a hybrid mode and a pure fuel mode. It should be understood that the hybrid mode is merely an example, and the non-pure electric mode may also include other operating modes, which are not limited here.

[0311] 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:

[0312] 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.

[0313] 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:

[0314] (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.

[0315] 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.

[0316] 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.

[0317] 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.

[0318] 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.

[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:

[0320] 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.

[0321] 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.

[0322] 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.

[0323] 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 and has a medium congestion level, then the category coefficient for that road segment is 1. That is, a category coefficient of 1 indicates that the road segment is a highway and has 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 the category coefficient of road segment A.

[0324] It should be noted that this equivalent factor is the equivalent factor in ECMS, and the explanation in ECMS can be found there; it will not be repeated here. Using the equivalent factor and 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 ECMS.

[0325] 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.

[0326] 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.

[0327] 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:

[0328] Where H(u, SOC(t), t) is the Hamiltonian function obtained based on ECMS, and argH(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.

[0329] 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.

[0330] 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.

[0331] 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.

[0332] 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.

[0333] 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.

[0334] 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.

[0335] 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.

[0336] In some embodiments, the engine's operating state is 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 a high-efficiency operating range.

[0337] 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.

[0338] In some embodiments, the determination of whether a destination has charging conditions can be based on whether the destination displayed in the navigation is a charging address and the number of available charging stations at that address. If the destination is a charging address and has available charging stations, it is determined that charging conditions are met; otherwise, charging conditions are not met. The determination of charging conditions can also be based on the historical charging behavior of the home, office, and favorite locations set in the navigation. If the frequently used home location has a certain frequency of charging activity, it is determined that charging conditions are met; otherwise, charging conditions are not met.

[0339] 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.

[0340] 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.

[0341] In some embodiments, the reduced endpoint SOC meets the vehicle's minimum permissible SOC.

[0342] In some embodiments, the minimum permissible SOC of a vehicle is the SOC required for the entire vehicle to operate.

[0343] 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 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.

[0344] 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.

[0345] 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.

[0346] 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.

[0347] 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.

[0348] In some embodiments, the engine's operating state is controlled based on the initial SOC, target SOC, and actual vehicle demand for each road segment, ensuring the engine operates within its 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 requirement for the engine to operate within its efficient operating range, then controlling the engine to operate within its efficient operating range and driving the vehicle, or controlling the engine to drive a generator or drive motor to generate electricity and store excess power in the 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 requirement for the engine to operate within its efficient operating range, then controlling the engine to operate within its efficient operating range and driving it with the drive motor, or having the drive motor and engine jointly drive the vehicle; if the target SOC is less than a certain threshold of the initial SOC, then controlling the engine to shut down.

[0349] 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.

[0350] In some embodiments, the vehicle is controlled to travel on a preset travel route based on a target speed.

[0351] 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.

[0352] In some embodiments, the prompt information includes at least one of target vehicle speed or pedal control information.

[0353] 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.

[0354] 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 commuting route, the method further includes controlling the engine's operating state based on the vehicle's historical driving data corresponding to the commuting route, so that the engine operates in a high-efficiency operating range.

[0355] In some embodiments, predictions are made by identifying patterns in historical driving data. For example, if a preset travel route is identified as a commuter route based on historical driving data, then the vehicle's driving conditions are considered commuter conditions, and these conditions are used as the future travel conditions. If identification fails, the prediction fails. The driving data used for storage and prediction mainly includes data related to vehicle energy consumption, such as speed, gradient, and power demand.

[0356] In some embodiments, historical driving data includes a sequence of vehicle speeds as the vehicle travels a commuter route over a historical time period.

[0357] 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's operating state is 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.

[0358] In some embodiments, please refer to FIG12, which is a schematic diagram of energy management based on historical driving data according to some embodiments of the present disclosure. 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 destination charging condition module, the 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 economy 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.

[0359] In some embodiments, if the navigation system auto-start function is off, the navigation system is off, and the preset travel route is not a commuter route: while the vehicle is traveling on the preset travel route, the vehicle speed within a preset time period is predicted to obtain the predicted vehicle speed within the preset time period; based on the predicted vehicle speed within the preset time period, the component control sequence of the vehicle within the preset time period is predicted; based on the first control instruction in the component control sequence, the corresponding component is controlled; the component includes at least one of the accelerator and the pedal.

[0360] 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.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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.

[0369] For example, a rolling time window method is used for short-term vehicle speed recordings, denoted as vehicle speed Vt|p={vt-i:1≤i≤p}, where the p value can be selected as 40 to meet the accuracy requirements of short-term prediction. Combining historical vehicle data, the vehicle speed is divided into intervals, and the probability pmij of the current speed interval changing to another speed interval in the next moment is calculated, constructing the system state transition probability matrix Pm=(pmij)n×n.

[0370] 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.

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] 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: control the vehicle to travel based on the current speed.

[0376] 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 method further includes: obtaining the current speed of the vehicle ahead; and controlling the vehicle to drive based on the current speed of the vehicle ahead.

[0377] 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 operations are triggered: The operation generates 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 of minimizing the overall vehicle energy consumption along the route; the speed sequence is then used as the target speed; the current speed of the vehicle ahead is obtained; the control speed of the vehicle is determined based on the current speed of the vehicle ahead and the target speed of the vehicle; and the vehicle is controlled to travel at the control speed.

[0378] 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.

[0379] 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.

[0380] 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.

[0381] In some embodiments, based on the target SOC of each road segment, actual vehicle demand, traffic light information, and an energy management strategy based on navigation information fusion, the engine start-stop is controlled so that the engine operates in an 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 stop, controlling the vehicle to travel at the vehicle's 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.

[0382] 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.

[0383] 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 13, 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. Whether the road conditions to the next intersection are clear is obtained; if clear, the distance to the next intersection, the current vehicle speed, and the traffic light countdown information are obtained. When... Then the vehicle can pass through the signalized intersection; if The vehicle cannot proceed through the signalized intersection. To address this issue, the coasting distance L should be calculated 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 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 engages at regenerative braking level 2. If the vehicle is in Hybrid Electric Vehicle (HEV) mode, it switches to Electric Vehicle (EV) mode; otherwise, it remains in EV mode. This applies when the vehicle meets L... 行驶 No 行驶 Then, it is the continued driving phase.

[0384] In some embodiments, the energy management strategy based on navigation information fusion also includes an automatic navigation method for commuting. When automatic navigation is activated and the preset travel route is the commuting route, please refer to Figure 14, which is a schematic diagram of an automatic navigation initial time update logic according to some embodiments of this disclosure. For example, it consists of optimal commuting time, commuting time period update, commuting route reminder, and commuting destination recommendation, aiming to automatically activate navigation upon power-on and promptly correct commuting time periods and commuting routes, thereby meeting different user-customized commuting needs and improving commuting navigation efficiency.

[0385] The automatic navigation method for commuting identifies the destination based on the user's preset commuting cycle, start and end times, residential address, and home address, further classifying it as a commuting situation. Commuting situations are divided into start and end times. When the vehicle starts, the onboard server first determines whether the initial position is met based on GPS, then uses preset start and end times offset by a certain time to form the start and end time periods. By determining whether the current time falls within the commuting cycle and end time period, it identifies whether it is a commuting situation, thus automatically starting navigation.

[0386] For example, the optimal commute time is recorded through navigation. If the commute time is greater than the average commute time in the navigation, the current optimal commute time is recorded. After a certain update cycle, the UI recommends the optimal initial time to the user. Whether the user accepts the recommendation and makes a change or not, the user-set initial time is updated. If the commute time is not greater than the average commute time in the navigation, the user-set initial time is retrieved.

[0387] For example, in the commuting time update, when the initial GPS positioning meets the home or office criteria, the user-set initial time is obtained and a commuting time is formed. When the commuting time exceeds the deviation threshold, the actual vehicle usage time is recorded, and when the calibration cycle is met, the commuting time is updated by the interval offset correction amount. When the commuting time does not exceed the deviation threshold, the commuting time is formed directly. When the calibration cycle is not met, the commuting time is formed directly.

[0388] For example, commuting route reminders mainly involve storing and recognizing historical navigation routes. Users set commuting times and routes, such as time period one corresponding to route one and time period two corresponding to route two. The system records historical navigation routes and start times to obtain the travel time for each navigation route. When the calibration period is met, the system allocates the optimal navigation route based on the time period corresponding to the current navigation time. When the calibration period is not met, the system records historical navigation routes and start times to obtain the travel time for each navigation route.

[0389] For example, the commuting destination recommendation can be implemented through manual navigation, automatic navigation, and manual re-entry of the destination. If the commuting time is met, the destination selection count is incremented by 1. If the destination selection count is greater than or equal to 4, the destination selection count is reset to 0, and the user is prompted to change the destination to their commuting destination. If the commuting time is not met, the process is ignored and the process ends.

[0390] When none of the above strategies are met, the State of Charge (SOC) for maintaining vehicle power is adjusted based on driver style, current vehicle speed, or environmental information. The engine's operating state is controlled based on the comparison between the vehicle's actual SOC and the adjusted SOC for maintaining vehicle power, ensuring that the engine operates within its high-efficiency range.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] In some embodiments, if it is before the trip, the passenger compartment temperature is corrected based on the passenger compartment temperature, navigation information, outside temperature, charging status and user boarding time to generate a target passenger compartment temperature deviation value before the trip, and the passenger compartment target temperature is generated. The deviation correction value is divided into cooling type and heating type.

[0397] In some embodiments, engine coolant is further utilized to preheat the passenger compartment when the air conditioning is in heating mode. By optimizing heating and cooling power for slow preheating and precooling, energy loss due to high current is reduced, thereby saving power consumption of the high and low temperature air conditioning and accessories and reducing fuel consumption. At the same time, preheating and precooling alleviate the lag between the target temperature and the actual controlled temperature of components and passenger compartment caused by heat capacity, ensuring the efficiency of components under high and low temperature environments and the comfort of the passenger compartment.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] In some embodiments, the target temperature deviation value can be obtained during vehicle operation by:

[0402] During vehicle operation, temperature-influencing factors are collected, including at least one of vehicle information and environmental information. For example, 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 value corresponding to the temperature-influencing factors.

[0403] 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.

[0404] In some embodiments, the number of temperature deviation values ​​corresponding to temperature influencing factors is multiple;

[0405] 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.

[0406] 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.

[0407] 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.

[0408] In some embodiments, the duration for which the engine is to output power is predicted, and the engine is started when the output duration exceeds a third preset duration.

[0409] 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.

[0410] In some embodiments, the vehicle's traffic jam time is predicted, and when the traffic jam time interval is greater than a fourth preset duration, the engine coolant temperature is increased.

[0411] In some embodiments, the engine coolant temperature can be increased by reducing the engine water pump speed or reducing the engine fan speed.

[0412] 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.

[0413] In some embodiments, adjusting the target battery temperature reduces battery thermal management energy consumption.

[0414] In some embodiments, the destination of a preset travel route is predicted. When the distance between the vehicle's current location and the destination is less than a preset distance, the adjustment of the engine's coolant temperature based on the target coolant temperature deviation is paused, and the engine's coolant temperature is increased until the engine's coolant temperature is higher than a preset temperature threshold before the vehicle reaches the destination.

[0415] 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.

[0416] In some embodiments, the destination of a preset travel route is predicted. When the distance between the vehicle's current location and the destination is less than a preset distance, 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.

[0417] 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.

[0418] In some embodiments, the destination of a preset travel route is predicted. When the distance between the vehicle's current location and the destination is less than a preset distance, the control of the vehicle's passenger compartment temperature to reach the target passenger compartment temperature is paused, and the target passenger compartment temperature is corrected.

[0419] 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.

[0420] In some embodiments of this disclosure, a travel route with the lowest overall vehicle energy consumption is selected based on the vehicle's origin and destination. Simultaneously, a target State of Charge (SOC) for each road segment is planned with the goal of minimizing fuel consumption along the travel route. Vehicle control is implemented based on the target SOC of each road segment and actual vehicle demand, achieving a rational allocation of fuel and electricity in the hybrid vehicle, reducing fuel consumption and operating costs. Furthermore, by controlling the engine's operating state, the engine operates within its high-efficiency range, improving NVH performance, avoiding frequent engine start-stop cycles, and enhancing ride comfort. Preheating management is implemented before and during driving. Based on heating and cooling needs, heating and cooling power is optimized for slow, preheating and precooling in advance, reducing energy loss due to high current, thereby saving power consumption of high and low temperature air conditioning and accessories and reducing fuel consumption.

[0421] Based on the above description, Figure 15A is a schematic flowchart of another intelligent energy management method for new energy vehicles according to some embodiments of the present disclosure, and Figure 15B is a schematic flowchart of another intelligent energy management method for new energy vehicles according to some embodiments of the present disclosure; as shown in Figures 15A and 15B, the intelligent energy management method for new energy vehicles includes, but is not limited to, steps S1501-S1534.

[0422] S1501, the user powers on the vehicle.

[0423] S1502, Determine whether automatic navigation is enabled.

[0424] In some embodiments, the vehicle has an automatic navigation switch. When the automatic navigation is turned on, the vehicle is powered on and it is determined whether the automatic navigation is turned on. If the automatic navigation is not turned on, the following step S1503 is executed. If the automatic navigation is turned on, the following step S1533 is executed.

[0425] S1503 determines whether the user wants navigation.

[0426] In some embodiments, when automatic navigation is not enabled, it is determined whether the user manually enables navigation. If the user does not enable navigation, step S1504 is executed. If the user manually enables navigation, step S1509 is executed.

[0427] S1504, determine whether it is a commuting condition.

[0428] In some embodiments, driving conditions are identified by recognizing patterns in historical driving data. If the driving condition is a commuting condition, step S1508 is executed; if the driving condition is not a commuting condition, step S1505 is executed.

[0429] S1505, determine whether intelligent driving sensors are present.

[0430] In some embodiments, if the vehicle has intelligent driving sensors, step S1507 is executed; if the vehicle does not have intelligent driving sensors, step S1506 is executed. For example, intelligent driving sensors can be sensors such as lidar, millimeter-wave radar, and cameras.

[0431] S1506 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's energy consumption for that segment, the vehicle's actual needs, and a speed prediction control strategy based on historical data, ensuring that the engine operates within its high-efficiency range.

[0432] In some embodiments, for scenarios where navigation and route exploration are not enabled, there are no intelligent driving sensors, future travel information cannot be identified, and historical data identification is irregular, the engine's operating state is controlled based on the initial SOC of the power battery for each road segment, the vehicle's energy consumption for that road segment, the vehicle's actual overall demand, and a speed prediction control strategy based on historical data, so that the engine operates in a high-efficiency operating range.

[0433] 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 not a commuting route, the vehicle speed is predicted within a preset time period while the vehicle is traveling on the preset travel route, and the predicted vehicle speed within the preset time period is obtained; based on the predicted vehicle speed within the preset time period, the component control sequence of the vehicle within the preset time period is predicted; and the corresponding component is controlled according to the first control instruction in the component control sequence; the component includes at least one of the accelerator and the pedal.

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] For example, a rolling time window method is used for short-term vehicle speed recordings, denoted as vehicle speed Vt|p={vt-i:1≤i≤p}. For instance, to ensure short-term prediction accuracy, the p-value can be selected as 40. Combining historical vehicle data, the vehicle speed is divided into intervals, and the probability pmij of the current speed interval changing to another speed interval in the next moment is calculated, constructing the system state transition probability matrix Pm=(pmij)n×n.

[0444] Considering the vehicle's own speed limitations and the aforementioned traffic flow speed limitations, the system state transition probability matrix is ​​modified (Pm). For example, the traffic flow speed is set as the average speed, thus limiting the maximum and minimum speeds of the vehicle during future travel. The vehicle's own speed limitations affect the maximum acceleration or deceleration of the vehicle's response, limiting the speed change range between adjacent time points. Based on the system state transition probability matrix and the current vehicle speed, the speed range with the highest probability in the next time step is predicted. For example, if the current vehicle speed is 20 km / h, the speed range with the highest probability in the next time step is predicted to be between 20 km / h and 30 km / h.

[0445] Using Vt|f=vt|t∏Pm(n), n=1,2,...,f, calculate the future predicted speed at time f, obtain 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 in the future time. Substitute the above data into the vehicle's longitudinal kinematics model to calculate whether the safe driving conditions are met: if the safety conditions are met, output the predicted vehicle speed within the preset time period; if not, activate the safety reminder.

[0446] 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.

[0447] S1507 controls the engine's operating state based on the initial SOC of the power battery in each road segment, the vehicle's energy consumption in that segment, the vehicle's actual overall demand, and a speed prediction control strategy based on perception planning, so that the engine operates in a high-efficiency range.

[0448] In some embodiments, for scenarios where navigation and route exploration are not enabled, intelligent driving sensors are present but cannot identify future travel information and historical data identification is irregular, the engine's operating state is controlled according to the target SOC of each road segment, the actual vehicle demand, and a speed prediction control strategy based on perception planning, so that the engine operates in a high-efficiency range.

[0449] In some embodiments, predicting the vehicle speed within a preset time period to obtain the predicted vehicle speed within the preset time period can be achieved by:

[0450] When the intelligent driving function is activated, the vehicle speed is predicted within a preset time period based on intelligent driving sensor data, thus obtaining the predicted vehicle speed within the preset time period.

[0451] In some embodiments, for example, within a preset time period of 5 to 10 seconds in the future, intelligent driving sensors such as lidar, millimeter-wave radar, and cameras are used to collect information about the current vehicle and its surrounding environment, and to calculate the predicted vehicle speed for the next 5 to 10 seconds.

[0452] In some embodiments, when the driver releases the accelerator and has no driving demand, the motor control device is shut down first before the back electromotive force of the vehicle motor is less than the withstand voltage value of the high voltage device, and the motor coasts with zero feedback, converting the kinetic energy of the whole vehicle into the driving distance.

[0453] 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.

[0454] 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.

[0455] S1508 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and commuting energy management strategies, ensuring that the engine operates within its high-efficiency operating range.

[0456] 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 engine's operating state is controlled based on the vehicle's historical driving data corresponding to the commuter route, so that the engine operates in a high-efficiency range.

[0457] In some embodiments, predictions are made by identifying patterns in historical driving data. For example, if a preset travel route is identified as a commuter route based on historical driving data, then the vehicle's driving conditions are considered commuter conditions, and these conditions are used as the future travel conditions. If identification fails, the prediction fails. For example, the driving data used for storage and prediction mainly includes data related to vehicle energy consumption, such as speed, gradient, and power demand.

[0458] The engine's operating status is controlled based on the target SOC for each road segment, actual vehicle demand, and commuting energy management strategy, ensuring that the engine operates within its high-efficiency range.

[0459] In some embodiments, historical driving data includes a sequence of vehicle speeds as the vehicle travels a commuter route over a historical time period.

[0460] In some embodiments, historical driving data includes vehicle speed sequences when the vehicle travels along commuting routes within a historical time period. Based on these speed sequences, the engine's operating state is controlled according to the target SOC of each road segment, actual vehicle demand, and commuting energy management strategies, so that the engine operates in its efficient operating range.

[0461] S1509, the user manually selects the destination.

[0462] In some embodiments, when a user manually starts navigation, the user manually selects the destination.

[0463] In some embodiments, the preset travel route is determined by responding to the user's input destination if the navigation system's auto-start function is disabled.

[0464] In some embodiments, a preset travel route can be determined in response to the user's input destination, which can be achieved by:

[0465] Based on the vehicle's origin and destination, at least one candidate energy-saving path is determined; the total vehicle energy consumption predicted by at least one candidate energy-saving path is less than the total vehicle energy consumption predicted by other paths. The total vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path.

[0466] In response to the selection operation of 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 of the route includes the total vehicle energy consumption of multiple road segments.

[0467] In some embodiments, at least one candidate energy-saving route is calculated based on the vehicle's origin and destination. These candidate routes are the routes with the lowest energy consumption throughout the entire trip. Users can select one of these candidate energy-saving routes as their preset travel route. The preset travel route refers to the selected candidate energy-saving route, which includes multiple road segments. The total vehicle energy consumption of the route includes the total vehicle energy consumption of each of the multiple road segments.

[0468] S1510, determine whether the remaining driving range is greater than the total driving range.

[0469] In some embodiments, if the remaining mileage is greater than the total mileage of the preset travel route, then step S1511 is executed; if the remaining mileage is less than or equal to the total mileage, then step S1514 is executed.

[0470] S1511 is determined to be a short driving mileage.

[0471] In some embodiments, when the remaining mileage is greater than the total mileage of the preset travel route, it is determined to be a short mileage.

[0472] S1512, User Interface (UI): Recommended energy-saving path.

[0473] In some embodiments, the UI may be an instrument panel, a pad, a head-up display (HUD), etc., and recommend energy-saving paths to users through the UI. The energy-saving path is the path with the lowest overall vehicle energy consumption, or at least one path with overall vehicle energy consumption less than a preset energy consumption threshold.

[0474] S1513, User confirms driving route.

[0475] In some embodiments, the user confirms a driving route from the energy-saving routes recommended by the UI, i.e., a preset travel route. For example, the steps for determining a preset travel route are described in the above-mentioned method for intelligent energy management of new energy vehicles, and will not be repeated here.

[0476] S1514 is identified as having a long mileage.

[0477] In some embodiments, when the remaining driving range is less than or equal to the total driving range, it is determined to be a long driving range, and an energy-saving replenishment plan needs to be recommended.

[0478] S1515, UI: Recommended energy-saving path.

[0479] In some embodiments, energy-saving paths are recommended to users via the UI.

[0480] S1516, User confirms driving route.

[0481] In some embodiments, the user confirms a driving route from the energy-saving routes recommended by the UI, i.e., a preset travel route. For example, the steps for determining a preset travel route are described in the above-mentioned method for intelligent energy management of new energy vehicles, and will not be repeated here.

[0482] S1517, UI: Recommend energy-saving and energy replenishment planning.

[0483] In some embodiments, when the remaining driving range is less than or equal to the total driving range, the UI recommends an energy-saving and refueling plan to the user. For example, the steps for determining the energy-saving and refueling plan are described in the steps for determining the preset travel route in the above-mentioned intelligent energy management method for new energy vehicles, and will not be repeated here.

[0484] S1518 determines whether the intelligent driving sensors are available.

[0485] In some embodiments, if the intelligent driving sensor is available, step S1522 is performed; if the intelligent driving sensor is unavailable, step S1519 is performed.

[0486] S1519, determine whether to activate the energy-saving driving guidance system.

[0487] In some embodiments, if the energy-saving driving guidance system is not activated, step S1520 is executed; if the energy-saving driving guidance system is activated, step S1521 is executed. For example, energy-saving driving guidance refers to controlling and guiding the vehicle to travel at the target speed that minimizes the overall vehicle energy consumption along the route. When the user is manually driving, energy-saving driving guidance is mainly presented in the form of a reference speed, which can interact with the user through displays such as instruments or tablets. For example, the target speed can be displayed through instruments to guide the user to control the vehicle to travel at the target speed that minimizes the overall vehicle energy consumption along the route.

[0488] S1520 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and an energy management strategy based on navigation information fusion, ensuring that the engine operates within its high-efficiency operating range.

[0489] In some embodiments, the energy management strategy based on navigation information fusion is described in steps 1, 2, and 3 of the above-mentioned intelligent energy management method for new energy vehicles, which predicts the vehicle energy consumption along the path. This solution will not repeat these steps.

[0490] In some embodiments, based on the vehicle's current speed and traffic light information, it is determined whether the vehicle has the capability to pass through the signalized intersection where the traffic light corresponding to the traffic light information is located; if the vehicle does not have the capability to pass through the signalized intersection where the traffic light corresponding to the traffic light information is located, the vehicle's drivable time is calculated; the engine is controlled to operate efficiently or stop, and the vehicle is controlled to travel at the vehicle's current speed within the drivable time, and the mechanical brakes are deactivated at the end of the drivable time, and a preset energy recovery level is activated.

[0491] 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.

[0492] In some embodiments, the energy management strategy based on navigation information fusion further includes local correction of traffic light information fusion. The steps of this solution are detailed in the above-described intelligent energy management method for new energy vehicles, specifically the step of local correction of traffic light information fusion in the energy management strategy based on navigation information fusion; these steps will not be repeated here.

[0493] In some embodiments, the energy management strategy based on navigation information fusion further includes an automatic navigation method for commuting, i.e., when automatic navigation is activated and the preset travel route is the commuting route. For the steps of this solution, please refer to the steps of the above-mentioned intelligent energy management method for new energy vehicles, specifically the automatic navigation method for commuting, which will not be repeated here.

[0494] S1521 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and energy management strategies based on navigation information fusion and energy-saving driving guidance, ensuring that the engine operates within its high-efficiency operating range.

[0495] In some embodiments, for navigation information fusion, please refer to steps 1, 2, and 3 of the above-described intelligent energy management method for new energy vehicles to predict the vehicle energy consumption along a route; these steps will not be repeated in this solution. Energy-saving driving guidance refers to controlling and guiding the vehicle to travel at the target speed that minimizes the vehicle energy consumption along the route. For example, the target speed is displayed through an instrument panel or similar means to guide the user to control the vehicle to travel at the target speed that minimizes the vehicle energy consumption along the route. For example, for determining the target speed, please refer to the steps of the above-described intelligent energy management method for new energy vehicles to determine the target speed; these steps will not be repeated in this solution.

[0496] S1522, determine whether Navigate on Autopilot (NOA) is enabled.

[0497] In some embodiments, if NOA is enabled, then step S1523 is performed; if NOA is not enabled, then step S1524 is performed.

[0498] S1523 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and the energy-saving speed control strategy, ensuring that the engine operates within its high-efficiency operating range.

[0499] In some embodiments, when NOA is activated, the engine's operating state is controlled based on the target SOC of the power battery for each road segment, actual vehicle demand, and energy-saving speed control strategy, aiming to minimize fuel consumption along a preset travel route. This ensures the engine operates within its efficient operating range. For example, the determination of the energy-saving speed, i.e., the target speed, is described in the steps of the target speed determination method in the aforementioned new energy vehicle energy intelligent management method, and will not be repeated here. Once the target speed is determined, the vehicle is controlled to travel at that speed.

[0500] S1524, determine whether to activate Adaptive Cruise Control (ACC).

[0501] In some embodiments, if ACC is enabled, step S1525 is executed; if ACC is not enabled, step S1532 is executed.

[0502] S1525, determine if there are vehicles ahead.

[0503] In some embodiments, if there is a vehicle ahead, step S1529 is performed; if there is no vehicle ahead, step S1526 is performed.

[0504] S1526, Determine if vehicle speed planning is activated.

[0505] In some embodiments, if vehicle speed planning is activated, the following step S1527 is performed; if vehicle speed planning is not activated, the following step S1528 is performed.

[0506] S1527 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and the energy-saving cruise speed control strategy, ensuring that the engine operates within its high-efficiency range.

[0507] In some embodiments, the energy-saving cruise control speed control strategy controls the vehicle to travel based on a target speed. For example, the steps for determining the target speed in the above-mentioned new energy vehicle energy intelligent management method are not repeated here.

[0508] S1528 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and the cruise control speed control strategy, ensuring that the engine operates within its high-efficiency operating range.

[0509] 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 vehicle is controlled to travel based on the current speed.

[0510] S1529, determine whether vehicle speed planning is activated.

[0511] In some embodiments, if vehicle speed planning is activated, the following step S1530 is performed; if vehicle speed planning is not activated, the following step S1531 is performed.

[0512] S1530 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and the energy-saving following speed control strategy, ensuring that the engine operates within its high-efficiency range.

[0513] 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 the vehicle speed planning is activated, the operation of determining the target vehicle speed with the minimum vehicle energy consumption for each road segment is triggered based on the road traffic flow speed and the current vehicle speed.

[0514] Get the current speed of the vehicle in front of you;

[0515] The control speed of the vehicle is determined based on the current speed of the vehicle in front and the vehicle's energy-saving speed; the vehicle is then controlled to travel based on the control speed.

[0516] In some embodiments, if the current speed of the vehicle in front is greater than or equal to the energy-saving speed of the vehicle, then the controlled speed of the vehicle is the energy-saving speed; if the current speed of the vehicle in front is less than the energy-saving speed of the vehicle, then the controlled speed of the vehicle is the current speed of the vehicle in front. For example, the determination of the energy-saving speed, i.e. the target speed, is described in the steps of the above-mentioned determination method of target speed in the intelligent energy management method for new energy vehicles, and will not be repeated in this solution.

[0517] S1531 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and the following vehicle speed control strategy, so that the engine operates in its high-efficiency range.

[0518] 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 the speed planning is not activated, the following speed control strategy is to obtain the current speed of the vehicle ahead and control the vehicle to drive based on the current speed of the vehicle ahead.

[0519] S1532 controls the engine's operating state based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall needs, and an energy management strategy that integrates navigation information and intelligent driving perception, ensuring that the engine operates within its high-efficiency operating range.

[0520] In some embodiments, the energy management strategy based on navigation information is described in steps 1, 2, and 3 of the above-mentioned intelligent energy management method for new energy vehicles, which predicts the vehicle's energy consumption along the path; these steps will not be repeated here. The intelligent driving perception-based energy management strategy utilizes intelligent driving sensors such as lidar, millimeter-wave radar, and cameras to collect information about the current vehicle and its surrounding environment, and calculates the predicted vehicle speed for the next 5 to 10 seconds.

[0521] 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.

[0522] In some embodiments, for example, a preset time period of 5 to 10 seconds in the future, 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.

[0523] In some embodiments, when none of the above strategies are satisfied, the method further includes: adjusting the power-saving SOC based on driver style, the vehicle's current speed, or the vehicle's environmental information; and controlling the engine's operating state based on a comparison between the vehicle's actual SOC and the adjusted power-saving SOC.

[0524] 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.

[0525] S1533, determine whether the conditions for automatic navigation are met.

[0526] In some embodiments, when automatic navigation is enabled, it is determined whether the automatic navigation conditions are met. For example, the automatic navigation conditions include time conditions and address conditions. If the time and address for vehicle use are met, the following step S1534 is executed. If the time or address for vehicle use is not met, the above step S1503 is executed.

[0527] S1534, automatic navigation upon power-on.

[0528] In some embodiments, when the conditions for automatic navigation are met, i.e., both the time of vehicle use and the location of power consumption are met, navigation will automatically start after the vehicle is powered on.

[0529] In some embodiments of this disclosure, based on navigation information, driving condition information, intelligent driving sensor information, energy-saving driving guidance system information, and information about vehicles ahead, a corresponding energy management strategy is determined. The engine's operating state is controlled according to the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, the vehicle's actual overall demand, and the corresponding energy management strategy. By integrating information such as traffic flow, traffic lights, charging stations, and driving style, the engine start-stop timing and operating point are optimized, improving system operating efficiency and reducing user fuel consumption. Intelligent electric vehicle integration further reduces engine start-stop cycles, enhancing the driving experience.

[0530] In some implementations of this disclosure, referring to Figure 1, a drive unit includes an engine 10, a drive motor 20, and a generator 30; a power battery 40 and a control unit 50. The drive unit provides driving force to the vehicle. For example, the engine 10 selectively outputs power to the wheels of the vehicle. The drive motor 20 outputs power to the wheels. The generator 30 is connected to the engine 10 to generate electricity under the drive of the engine 10. The power battery 40 supplies power to the drive motor 20 and is charged according to the current output from the generator 30 or the drive motor 20.

[0531] The control device 50 is configured to: determine at least one candidate energy-saving path based on the vehicle's starting point and ending point; the predicted total vehicle energy consumption of the at least one candidate energy-saving path is less than the predicted total vehicle energy consumption of other paths, and the total vehicle energy consumption is predicted based on road condition information and energy consumption impact information of each path; in response to the selection operation of at least one candidate energy-saving path, determine a preset travel path; the preset travel path refers to the selected candidate energy-saving path; the preset travel path includes multiple road segments, and the total vehicle energy consumption includes the total vehicle energy consumption of multiple road segments; with the goal of minimizing fuel consumption of the preset travel path, control the operating state of the engine 10 based on the initial SOC of the power battery 40 of each road segment, the total vehicle energy consumption of the road segment, and the actual vehicle demand of the vehicle, so that the engine operates in a high-efficiency operating range.

[0532] 1. Based on the vehicle's starting point and destination, determine at least one candidate energy-saving path; the total vehicle energy consumption predicted by at least one candidate energy-saving path is less than the total vehicle energy consumption predicted by other paths. The total vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path.

[0533] For example, candidate energy-saving paths can be determined as follows:

[0534] In some embodiments, at least one candidate energy-saving path is determined based on the vehicle's starting point and ending point: at least one candidate driving 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 road condition information and energy consumption impact 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.

[0535] 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.

[0536] In some embodiments, the first travel dimension indicator includes travel distance, and the second travel dimension indicator includes travel time.

[0537] 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.

[0538] 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.

[0539] 1. Based on the energy consumption prediction algorithm of automotive theory, the total energy consumption of the vehicle along the preset travel route is predicted according to the road traffic flow speed and the static parameters of the vehicle. The total energy consumption of the vehicle along the route is the theoretical energy consumption required.

[0540] In some embodiments, energy consumption impact information includes vehicle status information, which includes at least the vehicle's static parameters, and road condition information includes at least the road traffic flow speed. The total vehicle energy consumption along a route is predicted based on the road condition information and energy consumption impact information for each route. This can be achieved by: predicting the total vehicle energy consumption of a preset travel route based on the road traffic flow speed and the vehicle's static parameters using an energy consumption prediction algorithm according to automotive theory. The total vehicle energy consumption along a route is the theoretical energy consumption requirement.

[0541] In some embodiments, the static parameters of the vehicle include at least: wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

[0542] 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.

[0543] 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. 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.

[0544] In some embodiments, energy consumption impact information includes vehicle status information, which includes at least the user's driving style and vehicle model information, and road condition information, which includes at least the road type. The total vehicle energy consumption along a route is predicted based on the road condition information and energy consumption impact information for each route. This can be achieved by: inputting the road type, driving style, and vehicle model information into a target energy consumption prediction model, and outputting the predicted total vehicle energy consumption for the preset travel route from the target energy consumption prediction model. The total vehicle energy consumption along the route is a reference demand energy consumption. The target energy consumption prediction model is determined from multiple preset energy consumption prediction models based on at least one of the road type or the user's driving style information for the preset travel route.

[0545] In some embodiments, road types include: ordinary roads, expressways, highways, and congested roads.

[0546] 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.

[0547] 3. Based on the theoretical and reference energy consumption requirements of the vehicle on the preset travel route, the total energy consumption of the vehicle on the preset travel route is predicted.

[0548] The theoretical energy consumption requirement is calculated using an energy consumption prediction algorithm based on automotive theory. The reference energy consumption requirement is then obtained by outputting the target energy consumption prediction model. The theoretical energy consumption requirement and the reference energy consumption requirement are weighted and added together to predict the total vehicle energy consumption for the preset travel route.

[0549] In some embodiments, vehicle status information includes at least vehicle static parameters and vehicle model information; user behavior information includes at least the user's driving style; and road condition information includes at least road traffic flow speed and road type. The theoretical energy consumption requirement is obtained through the following steps: predicting the total vehicle energy consumption of a preset travel route based on the road traffic flow speed and vehicle static parameters using an energy consumption prediction algorithm based on automotive theory; the total vehicle energy consumption of the route is the theoretical energy consumption requirement. The reference energy consumption requirement is obtained through the following steps: inputting road type, driving style, and vehicle model information into a target energy consumption prediction model; and outputting the predicted total vehicle energy consumption of the preset travel route from the target energy consumption prediction model; the total vehicle energy consumption of the route is the reference energy consumption requirement. 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.

[0550] In some embodiments, the total energy consumption of a vehicle along a preset travel route can be predicted based on the vehicle's theoretical energy consumption and reference energy consumption along the preset travel route by: obtaining a first weight of the vehicle's theoretical energy consumption and a second weight of the reference energy consumption; and performing a weighted calculation on the vehicle's theoretical energy consumption and reference energy consumption based on the first weight and the second weight to predict the vehicle's total energy consumption along the route.

[0551] 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.

[0552] In some embodiments, if the error between the predicted vehicle energy consumption on the nth road segment and the actual vehicle energy consumption on the nth road segment is greater than a certain threshold, 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 so that the server can optimize the energy consumption prediction model corresponding to the model identifier based on the actual vehicle energy consumption on the nth road segment.

[0553] In some embodiments, the division of road segments is related to the traffic information of a preset travel route; each road segment is divided according to at least one of the road type and congestion level of the preset travel route.

[0554] In some embodiments, road types include at least: ordinary roads, expressways, highways, and congested roads; the user's driving style is divided into at least three categories: aggressive, normal, and mild, based on the rate of change of accelerator pedal opening and the rate of change of acceleration.

[0555] In some embodiments, the vehicle's total energy consumption on road segment n is predicted in the following manner:

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 4. Based on the energy consumption prediction algorithm of automotive theory, predict the total energy consumption of the preset travel route according to the road traffic flow speed, vehicle static parameters and the target speed that minimizes the total energy consumption of the vehicle along the route.

[0562] In some embodiments, energy consumption impact information includes vehicle status information, which 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. The overall vehicle energy consumption along the route is predicted based on the road condition information and energy consumption impact information for each route. This can be achieved by: using an energy consumption prediction algorithm based on automotive theory, and predicting the overall vehicle energy consumption of a preset travel route based on the road traffic flow speed, the vehicle's static parameters, and the target speed that minimizes the overall vehicle energy consumption along the route.

[0563] In some embodiments, according to the energy consumption prediction algorithm of automotive theory, the total energy consumption of a preset travel route is predicted based on driving style, road traffic flow speed, vehicle static parameters, and the target vehicle speed that minimizes the total energy consumption of the route.

[0564] In some embodiments, 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, average vehicle speed, gradient, traffic light information, and weather information; energy consumption impact information includes vehicle status information, or energy consumption impact information includes at least one of user driving style information or traffic light information and vehicle status information; the actual vehicle demand for each road segment includes: the total vehicle power required for the vehicle to travel on each road segment.

[0565] 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 energy consumption of a preset travel route based on road traffic flow speed, vehicle static parameters, and the target speed that minimizes the total energy consumption of the route; or, when the intelligent driving function is activated and navigation-assisted driving function is activated, an energy consumption prediction algorithm based on automotive theory is triggered to predict the total energy consumption of a preset travel route based on road traffic flow speed, vehicle static parameters, and the target speed that minimizes the total energy consumption of the route; or, when the intelligent driving function is activated, navigation-assisted driving function is deactivated, adaptive cruise control function is activated, there are no vehicles ahead, and energy-saving driving guidance function is activated, an energy consumption prediction algorithm based on automotive theory is triggered to predict the total energy consumption of a preset travel route based on road traffic flow speed, vehicle static parameters, and the target speed that minimizes the total energy consumption of the route.

[0566] 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 energy consumption of a preset travel route based on road traffic flow speed, vehicle static parameters, and the target vehicle speed that minimizes the total energy consumption of the route.

[0567] 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.

[0568] For example, the target vehicle speed is determined in the following way: with the goal of minimizing the overall vehicle energy consumption along the route, a speed sequence is generated based on the road traffic flow speed of 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.

[0569] In some embodiments, the speed sequence is modified based on constraints, including at least driving style, to obtain a modified speed sequence.

[0570] 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.

[0571] 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.

[0572] 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 information includes 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 is used to generate a speed sequence based on the initial speed solution, with the objective function of minimizing the overall vehicle energy consumption along the route.

[0573] 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.

[0574] 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.

[0575] 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 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; using the intersection speed model as the objective function to minimize the overall vehicle energy consumption along the path and ensure that the passage time through the traffic light intersection is less than the preset expected passage time, and generating a locally corrected smooth speed sequence.

[0576] 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.

[0577] Please refer to the steps in S401 above for the steps of this solution, and this solution will not repeat them here.

[0578] 2. In response to the selection operation of 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 of the route includes the total vehicle energy consumption of multiple road segments.

[0579] 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.

[0580] In some embodiments, if the navigation system auto-start function is disabled, a preset travel route is determined in response to the user's input destination.

[0581] For example, when determining a preset travel route, it is also necessary to consider the vehicle's remaining mileage and the mileage to the destination. If the vehicle's remaining mileage is less than the mileage to the destination, then a refueling strategy is determined during the journey along the preset travel route. That is, when the mileage to the destination is greater than the vehicle's remaining mileage L based on predicted energy consumption, a refueling strategy is determined during the journey along the preset travel route.

[0582] In some embodiments, determining a refueling strategy during a preset travel route can be achieved by: obtaining the driver's fatigue driving mileage; 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, recommending that the vehicle drive to a target charging address for charging or a target refueling address for refueling.

[0583] In some embodiments, based on the fatigue driving mileage and the vehicle's remaining mileage, it is recommended that the vehicle drive to a target charging address for charging or a target refueling address for refueling. This 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.

[0584] In some embodiments, based on fatigue driving mileage and the vehicle's remaining mileage, it is recommended that the vehicle drive to a target charging address for charging or a target refueling address for refueling. This 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 route.

[0585] In some embodiments, based on fatigue driving mileage and the vehicle's remaining mileage, it is recommended that the vehicle drive to a target charging address for charging or a target refueling address for refueling. This 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.

[0586] In some embodiments, based on fatigue driving mileage and the vehicle's remaining mileage, it is recommended that the vehicle drive to a target charging address for charging or a target refueling address for refueling. This 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; 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.

[0587] Please refer to the steps in S402 above for the steps of this solution, and this solution will not repeat them here.

[0588] Third, with the goal of minimizing fuel consumption along the preset travel route, the engine 10's operating state is controlled based on the initial SOC of the power battery 40 for each road segment, the vehicle's overall energy consumption for that segment, and the vehicle's actual overall needs, so that the engine operates within its high-efficiency range.

[0589] In some embodiments, with the goal of minimizing fuel consumption along a preset travel route, the engine's operating state is controlled based on the initial SOC of the power battery for each road segment, the vehicle's overall energy consumption for that segment, and the vehicle's actual overall demand. This ensures that the engine operates within its efficient operating range. This can be achieved by: setting the goal of minimizing fuel consumption along a preset travel route and planning the target SOC for each road segment based on the initial SOC of the power battery for that segment and the vehicle's overall energy consumption for that segment; and controlling the engine's operating state based on the initial SOC, target SOC, and actual vehicle demand for each road segment, ensuring that the engine operates within its efficient operating range.

[0590] 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.

[0591] In some embodiments, the target SOC at the end of the first segment of the preset travel path is determined based on the vehicle's initial SOC on the preset travel path and the predicted SOC change of the first segment; the target SOC at the end of a non-first segment of the preset travel path is determined based on the predicted SOC change of the non-first segment and the target SOC at the end of the segment preceding the non-first segment.

[0592] 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.

[0593] 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 dete...

Claims

1. A method for intelligent energy management of new energy vehicles, comprising: Based on the vehicle's starting point and ending point, at least one candidate energy-saving path is determined; wherein, the vehicle energy consumption predicted by the at least one candidate energy-saving path is less than the vehicle energy consumption predicted by other paths, and the vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path. In response to the selection operation of the at least one candidate energy-saving route, a preset travel route is determined; wherein 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 of the route includes the total vehicle energy consumption of the multiple road segments; and With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial SOC of the power battery in each of the multiple road segments, the vehicle's overall energy consumption in the road segment, and the vehicle's actual overall needs, so that the engine operates in a high-efficiency range.

2. The method of claim 1, wherein, Determining the at least one candidate energy-saving path based on the vehicle's starting point and ending point includes: Based on the vehicle's starting point and ending point, at least one candidate driving path is determined; wherein, the starting point of any candidate driving path in the at least one candidate driving path is the vehicle's starting point, and the ending point of any candidate driving path is the vehicle's ending point. Based on the road condition information and energy consumption impact information of each of the at least one candidate driving path, predict the vehicle's total energy consumption on each candidate driving path; and Based on the vehicle's total energy consumption on each candidate driving path, at least one candidate energy-saving path is determined from the at least one candidate driving path; wherein, the vehicle's total energy consumption on any of the at least one candidate energy-saving paths is less than the vehicle's total energy consumption on other candidate driving paths in the at least one candidate driving path besides the at least one candidate energy-saving path.

3. The method according to claim 2, wherein, Determining the at least one candidate driving path based on the vehicle's origin and destination includes: Obtain at least one drivable path from the vehicle's starting point to its destination; Based on the first travel dimension index of each of the at least one drivable path, m drivable paths are determined from the at least one drivable path; where m is a positive integer, and the first travel dimension index of any of the m drivable paths is less than the first travel dimension index of any of the other drivable paths in the at least one drivable path; and Based on the second travel dimension index of the m drivable routes, at least one candidate drivable route is determined from the m drivable routes; wherein, the second travel dimension index of any candidate drivable route is less than the second travel dimension index of the other drivable routes in the m drivable routes excluding the at least one candidate drivable route.

4. The method according to claim 3, wherein, The first travel dimension indicator includes the travel distance, and the second travel dimension indicator includes the travel time.

5. The method according to claim 3 or 4, wherein, The determination of the at least one candidate travel route from the m drivable routes based on the second travel dimension index includes: Determine the target drivable path with the minimum second travel dimension index among the m drivable paths; From the m drivable paths, select drivable paths where the 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 The selected drivable routes are used as the at least one candidate drivable route.

6. The method according to any one of claims 2-5, wherein, Determining the at least one candidate driving path based on the vehicle's origin and destination includes: Obtain at least one drivable path from the vehicle's starting point to its destination; Obtain each travel dimension index for each drivable path in the at least one drivable path, and each weight of each travel dimension index for each drivable path corresponds to the current travel scenario of the vehicle; The travel dimension indicators are weighted according to each weight to obtain the comprehensive travel index for each drivable route; and Based on the comprehensive travel index of each drivable route, at least one candidate drivable route is selected from the at least one drivable route; wherein the comprehensive travel index of the at least one candidate drivable route is less than the comprehensive travel index of the other drivable routes in the at least one drivable route.

7. The method according to any one of claims 2-6, wherein, The step of predicting the vehicle's total energy consumption along each candidate driving path based on road condition information and energy consumption impact information includes: For any candidate driving route, if the total vehicle energy consumption of the candidate driving route exists in the historical database, then the total vehicle energy consumption of the candidate driving route in the historical database shall be used as the total vehicle energy consumption of the vehicle on the candidate driving route; wherein, the historical database stores the total vehicle energy consumption of at least one driving route within a historical time period.

8. The method according to any one of claims 1-7, wherein, The step of determining the preset travel route in response to the selection operation of the at least one candidate energy-saving route further includes: If the remaining driving range of the vehicle is less than the driving range to the destination, then a refueling strategy is determined during the journey along the preset travel route.

9. The method according to claim 8, wherein, The determined energy replenishment strategy during the journey along the preset travel route includes: Obtain the driver's fatigue driving mileage; wherein, the fatigue driving mileage refers to the mileage that the driver could drive while in a fatigued driving state; and Based on the fatigue driving mileage and the vehicle's remaining driving range, it is recommended that the vehicle drive to the target charging address for charging or drive to the target refueling address for refueling.

10. The method according to claim 9, wherein, The method of recommending that the vehicle drive to the target charging address or the target refueling address for charging or refueling based on the fatigue driving mileage and the vehicle's remaining driving range includes: If the remaining driving range of the vehicle 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 it is recommended that the vehicle drive to the first charging address for charging; wherein, 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.

11. The method according to claim 9, wherein, The method of recommending that the vehicle drive to the target charging address or the target refueling address for charging or refueling based on the fatigue driving mileage and the vehicle's remaining driving range includes: If the remaining driving range of the vehicle 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 the first preset distance threshold, then it is recommended that the vehicle drive to the second charging address for charging; wherein, 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 refers to the charging address preceding the first charging address in the preset travel route.

12. The method according to any one of claims 9-11, wherein, The method of recommending that the vehicle drive to the target charging address or the target refueling address for charging or refueling based on the fatigue driving mileage and the vehicle's remaining driving range includes: If the remaining driving range of the vehicle is less than the fatigue driving mileage, and the difference between the fatigue driving mileage and the remaining driving range is less than a second preset distance threshold, then it is recommended that the vehicle drive to a third charging address for charging; wherein, the third charging address is located before the end of the remaining driving range, and the distance between the third charging address and the end of the remaining driving range is less than the distance between other charging addresses and the end of the fatigue driving mileage, and the other charging addresses refer to the other charging addresses other than the third charging address among the charging addresses located before the end of the remaining driving range.

13. The method according to any one of claims 9-12, wherein, The method of recommending that the vehicle drive to the target charging address or the target refueling address for charging or refueling based on the fatigue driving mileage and the vehicle's remaining driving range includes: If the remaining driving range of the vehicle is less than the fatigue driving mileage, and the difference between the fatigue driving mileage and the remaining driving range is greater than or equal to a second preset distance threshold, then it is recommended that the vehicle drive to the target refueling address for refueling; wherein, 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 mileage, and the 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.

14. The method according to any one of claims 1-13, wherein, The energy consumption impact information includes vehicle status information, which includes at least the vehicle's static parameters, and the road condition information includes at least the road traffic flow speed. The overall vehicle energy consumption for the route is predicted based on road condition information and energy consumption impact information for each route, including: According to the energy consumption prediction algorithm of automotive theory, the total energy consumption of the preset travel route is predicted based on the road traffic flow speed and the static parameters of the vehicle. The total energy consumption of the route is the theoretical energy consumption requirement.

15. The method according to claim 14, wherein, The static parameters of the vehicle include at least: wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

16. The method according to any one of claims 1-15, wherein, The energy consumption impact information includes vehicle status information; the vehicle status information includes at least the user's driving style and vehicle model information; the road condition information includes at least the road type. The overall vehicle energy consumption for the route is predicted based on road condition information and energy consumption impact information for each route, including: The road type, driving style, and vehicle model information are input into the target energy consumption prediction model, and the target energy consumption prediction model outputs the predicted total vehicle energy consumption of the preset travel route, which is a reference demand energy consumption; wherein, 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.

17. The method according to claim 16, wherein, The road types include at least: ordinary roads, expressways, highways, and congested roads; the user's driving style is divided into at least three categories—aggressive, normal, and mild—based on the rate of change of accelerator pedal opening and the rate of change of acceleration.

18. The method according to any one of claims 1-17, wherein, The overall vehicle energy consumption for the route is predicted based on road condition information and energy consumption impact information for each route, including: Based on the theoretical and reference energy consumption requirements of the vehicle on the preset travel route, the total vehicle energy consumption for the preset travel route is predicted.

19. The method according to claim 18, wherein, The step of predicting the total vehicle energy consumption for the preset travel route based on the vehicle's theoretical and reference energy consumption requirements on the preset travel route includes: Obtain the first weight of the theoretical energy consumption requirement and the second weight of the reference energy consumption requirement of the vehicle; and The theoretical energy consumption and reference energy consumption of the vehicle are weighted according to the first weight and the second weight to predict the total energy consumption of the vehicle along the route.

20. The method of claim 19, further comprising: 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 section 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 process of obtaining the first weight of the theoretical energy demand of the vehicle and the second weight of the reference energy demand includes: Obtain the updated first weight of the theoretical energy consumption requirement of the vehicle and the updated second weight of the reference energy consumption requirement.

21. The method according to any one of claims 18-20, wherein, The vehicle status information includes at least the vehicle's static parameters and vehicle model information; the user behavior information includes at least the user's driving style; and the road condition information includes at least road traffic flow speed and road type. The theoretical energy consumption requirement is obtained through the following steps: According to the energy consumption prediction algorithm of automotive theory, based on the road traffic flow speed and the static parameters of the vehicle, the total energy consumption of the preset travel route is predicted, and the total energy consumption of the route is the theoretical energy demand. The reference energy consumption requirement is obtained through the following steps: The road type, driving style, and vehicle model information are input into the target energy consumption prediction model, and the target energy consumption prediction model outputs the predicted total vehicle energy consumption of the preset travel route, which is a reference demand energy consumption; wherein, 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.

22. The method of claim 21, further comprising: If the predicted total energy consumption of the vehicle on the nth road segment and the actual total energy consumption of the vehicle on the nth road segment are greater than a certain error threshold, then the actual total energy consumption of the vehicle on the nth road segment 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 the nth road segment.

23. The method according to any one of claims 1-22, wherein, The division of each road segment is related to the traffic information of the preset travel route; each road segment is obtained based on at least one of the road type and congestion level of the preset travel route.

24. The method according to any one of claims 1-23, wherein, The energy consumption impact information includes vehicle status information; the vehicle status information includes at least the vehicle's static parameters and the target vehicle speed that minimizes the overall vehicle energy consumption along the route; the road condition information includes at least the road traffic flow speed. The overall vehicle energy consumption for the route is predicted based on road condition information and energy consumption impact information for each route, including: According to the energy consumption prediction algorithm of automotive theory, the total energy consumption of the preset travel route is predicted based on the road traffic flow speed, the static parameters of the vehicle, and the target speed that minimizes the total energy consumption of the vehicle along the route.

25. The method of claim 24, further comprising: The vehicle is controlled to travel on the preset travel route at the target speed that minimizes the overall vehicle energy consumption.

26. The method according to claim 24 or 25, wherein, Based on the target speed for minimizing overall vehicle energy consumption, a prompt message is generated. 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.

27. The method according to any one of claims 24-26, wherein, The target vehicle speed that minimizes overall vehicle energy consumption along the specified path is determined using the following method: With the goal of minimizing the overall vehicle energy consumption along the route, a speed sequence is generated based on the road traffic flow speed of the preset travel route and the vehicle's current speed, where the current speed is the vehicle's speed at the starting point of the preset travel route.

28. The method according to claim 27, further comprising: correcting the velocity sequence based on limiting conditions to obtain a corrected velocity sequence; wherein, The restrictions include at least driving style.

29. The method according to claim 28, wherein, The restrictions also include one or more of the following: travel duration, traffic flow speed information, acceleration restrictions, deceleration restrictions, maximum allowable speed in the area, and traffic light information.

30. The method according to any one of claims 24-29, wherein, The target vehicle speed is determined in the following way: Based on the road traffic flow speed, current vehicle speed, and constraint information of the preset travel route, a smooth speed sequence is determined; wherein, the constraint information includes at least driving style; and the current vehicle speed is the vehicle speed at the starting point of the preset travel route. The smoothed velocity sequence is input as the initial velocity solution into the vehicle model; and Using the vehicle model, with the objective function of minimizing the overall vehicle energy consumption along the path, a speed sequence is generated based on the initial speed solution.

31. The method according to claim 30, wherein, The process of determining a smooth speed sequence based on the road traffic flow speed, current vehicle speed, and constraint information of the preset travel route, and inputting the smooth speed sequence as the initial speed solution into the vehicle model, includes: Based on the road traffic flow speed, current vehicle speed and constraint information of the preset travel route, the average speed is obtained, and the speed change between adjacent road segments is smoothed to obtain a smooth speed sequence. Speed ​​adjustments are made to road segments based on driving style, road traffic flow speed, and traffic light location information for different driving scenarios, thereby locally correcting the smoothed speed sequence; and The initial optimization range of the vehicle model is determined based on the locally corrected smooth velocity sequence, and the smooth velocity sequence is input into the vehicle model as the initial velocity solution.

32. The method according to claim 31, wherein, The speed correction of road segments based on driving style, road traffic flow speed, and traffic light location information for different driving scenarios, in order to locally correct the smooth speed sequence, includes: 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 generates a locally corrected smooth speed sequence with the objective function of minimizing the overall vehicle energy consumption along the path and ensuring that the relative distance to the obstacle is greater than a preset distance threshold.

33. The method according to claim 31 or 32, wherein, The speed correction of road segments based on driving style, road traffic flow speed, and traffic light location information for different driving scenarios, in order to locally correct the smooth speed sequence, includes: When passing through a traffic light intersection, the vehicle's current acceleration, current speed, traffic light information, obstacle speed, and relative distance to the obstacle are input into the intersection speed model. The intersection speed model uses the objective function of minimizing the overall vehicle energy consumption along the path and ensuring that the time spent passing through the traffic light intersection is less than the preset expected time for the intersection to generate a locally corrected smooth speed sequence.

34. The method according to any one of claims 24-33, further satisfying one of the following: When the intelligent driving function is turned on and the vehicle speed planning is activated, the energy consumption prediction algorithm based on automobile theory is triggered to predict the total vehicle energy consumption of the preset travel route based on the road traffic flow speed, the static parameters of the vehicle and the target speed that minimizes the total vehicle energy consumption of the route. When the intelligent driving function is activated and the navigation-assisted driving function is activated, the energy consumption prediction algorithm based on automotive theory is triggered to predict the total energy consumption of the preset travel route based on the road traffic flow speed, the vehicle's static parameters, and the target speed that minimizes the total energy consumption of the vehicle along the route. 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 energy consumption prediction algorithm based on automotive theory is triggered to predict the total energy consumption of the preset travel route based on the road traffic flow speed, the vehicle's static parameters, and the target speed that minimizes the total energy consumption of the vehicle along the route.

35. The method according to any one of claims 24-34, wherein, When the intelligent driving function is turned off and the energy-saving driving guidance function is turned on, the energy consumption prediction algorithm based on automotive theory is triggered to predict the total energy consumption of the preset travel route based on the road traffic flow speed, the vehicle's static parameters, and the target speed that minimizes the total energy consumption of the vehicle along the route.

36. The method according to any one of claims 1-35, wherein, The traffic information includes at least one of the following: road type, road name, road traffic signs, road speed limit, congestion level, distance, travel time, average speed, gradient, traffic light information, and weather information. The energy consumption impact information includes vehicle status information; or, the energy consumption impact information includes at least one of the user's driving style information or traffic light information and vehicle status information. The actual vehicle requirements for each road segment include: the total vehicle power required for the vehicle to travel on each road segment.

37. The method according to any one of claims 1-36, wherein, If the navigation system's auto-start function is disabled, the navigation system is shut down, and the preset travel route is a commuter route, then the method further includes: Based on the historical driving data of the vehicle on the commuting route, the operating state of the engine is controlled so that the engine operates in the high-efficiency operating range. If the navigation system's auto-start function is disabled, the navigation system is turned off, and the preset travel route is not the commuting route, then the method further includes: During the process of the vehicle traveling on the preset travel route, the vehicle speed within a preset time period is predicted to obtain the predicted vehicle speed within the preset time period. Based on the predicted vehicle speed within the preset time period, predict the component control sequence of the vehicle within the preset time period; and The corresponding component is controlled according to the first control command in the component control sequence; the component includes at least one of the accelerator and the pedal.

38. The method according to any one of claims 1-37, satisfying one of the following: 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 method further includes: Control the vehicle to travel based on the current vehicle speed; or 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 the speed planning is not activated, the method further includes: obtaining the current speed of the vehicle ahead of the vehicle; And control the vehicle to travel based on the current speed of the vehicle in front.

39. The method according to any one of claims 27-38, It also includes one of the following: 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 a vehicle ahead, and the vehicle speed planning is activated, the operation of generating a speed sequence based on the road traffic flow speed of the preset travel route and the current vehicle speed is triggered, with the objective function of minimizing the overall vehicle energy consumption along the route. The speed sequence is used as the target vehicle speed; Get the current speed of the vehicle in front of the vehicle; as well as Based on the current speed of the vehicle ahead and the target speed of the vehicle, determine the control speed of the vehicle; control the vehicle to travel based on the control speed. or When the intelligent driving function is activated, the navigation-assisted driving function is deactivated, and the adaptive cruise control function is deactivated, 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 vehicle speed.

40. The method according to any one of claims 1-39, It also includes one of the following: adjusting the temperature of the power battery based on the charging status, the preset travel route, and the user's scheduled pick-up time; or, The engine is predicted to output power for a duration that is longer than a third preset duration. When the output duration exceeds a third preset duration, the engine is started. or, The system predicts the traffic jam time of the vehicle, and when the traffic jam time interval is greater than a fourth preset duration, it increases the engine coolant temperature.

41. The method according to any one of claims 1-40, further comprising: The destination of the preset travel route is predicted. When the distance between the current position of the vehicle and the destination is less than the preset distance, the adjustment of the engine water temperature based on the target water temperature deviation of the engine is paused, and the engine water temperature is increased until the engine water temperature is higher than the preset temperature threshold before the vehicle reaches the destination.

42. The method according to any one of claims 1-41, further comprising: The system predicts the destination of the preset travel route. When the distance between the vehicle's current position and the destination is less than a preset distance, it pauses the adjustment of the power battery temperature based on the target temperature deviation of the power battery until the temperature of the power battery is within the preset temperature range when the vehicle reaches the destination.

43. The method according to any one of claims 1-42, further comprising: The system predicts the destination of the preset travel route. When the distance between the current position of the vehicle and the destination is less than the preset distance, it pauses the control of the passenger compartment temperature to reach the target passenger compartment temperature and corrects the target passenger compartment temperature.

44. The method according to any one of claims 1-43, wherein, The goal is to minimize fuel consumption along the preset travel route. Based on the initial state of charge (SOC) of the power battery for each road segment, the vehicle's overall energy consumption for that segment, and the vehicle's actual overall needs, the engine's operating state is controlled to ensure it operates within its high-efficiency operating range. This includes: With the goal of minimizing fuel consumption along the preset travel route, the target SOC for each road segment is planned based on the initial SOC of the power battery and the overall vehicle energy consumption for that segment; and Based on the initial SOC, target SOC, and actual vehicle requirements of each road segment, the engine's operating state is controlled so that the engine operates within the high-efficiency operating range.

45. The method according to claim 44, wherein, The step of planning the target SOC for each road segment based on the initial SOC of the power battery and the vehicle energy consumption of each segment includes: Based on the initial SOC of the power battery in each road segment and the vehicle energy consumption in each road segment, the predicted SOC change of the vehicle at the end of each road segment is determined. Based on the predicted SOC change, multiple SOC change paths are determined, wherein each of the multiple SOC change paths includes a set of SOCs. Among the multiple SOC change paths, the SOC change path that minimizes the fuel consumption of the vehicle during the preset travel route is determined as the target SOC change path; and The SOCs included in the target SOC change path are determined as the target SOCs of each road segment.

46. ​​The method according to claim 45, wherein, The target SOC at the end of the first segment of the preset travel route is determined based on the change in the vehicle's initial SOC and the predicted SOC of the first segment of the preset travel route. 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.

47. The method according to claim 46, wherein, The predicted SOC change includes a first predicted SOC change and a second predicted SOC change; the upper limit of the target SOC of the first segment of the preset travel route is determined based on the starting SOC and the first predicted SOC change of the first segment. The lower limit of the target SOC of the first road segment is determined based on the initial SOC and the second predicted SOC change of the first road segment; The upper limit of the target SOC of the non-first segment of the preset travel route is determined based on the first predicted SOC change of the non-first segment and the upper limit of the target SOC of the previous segment of the non-first segment. The lower limit of the target SOC for the non-first road segment is determined based on the second predicted SOC change of the non-first road segment and the lower limit of the target SOC of the previous road segment of the non-first road segment.

48. The method according to any one of claims 44-47, further comprising: 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 will be reduced.

49. The method of claim 48, further comprising: Based on the target SOC of each road segment, the actual vehicle requirements, and the reduced destination SOC upon reaching the destination, the engine's operating state is controlled so that the engine operates within the high-efficiency operating range.

50. The method according to claim 48 or 49, wherein, The fact that the destination of the preset travel route has charging conditions includes: if the destination has a charging address and there is an idle charging pile at the charging address, then the destination is determined to have charging conditions.

51. The method according to any one of claims 44-50, wherein, The step of controlling the engine's operating state based on the initial SOC, target SOC, and actual vehicle requirements for each road segment, so that the engine operates within the high-efficiency operating range, includes: If the target SOC is greater than a certain threshold of the initial SOC, and the actual vehicle demand is less than the demand for the engine to operate in the high-efficiency operating range, then the engine is controlled to operate in the high-efficiency operating range and drive the vehicle, or the engine is controlled to drive the generator or drive motor to generate electricity and store the 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 demand of the engine operating in the high-efficiency operating range, then the engine is controlled to be in the high-efficiency operating range and driven by the drive motor, or the vehicle is driven by the drive motor and the engine together. If the target SOC is less than a certain threshold of the initial SOC, then the engine is controlled to shut down.

52. A control device, comprising a memory, a communication interface, and a processor, wherein, The memory, the communication interface, and the processor are interconnected; the memory stores a computer program, and the processor calls the computer program stored in the memory to implement the method according to any one of claims 1-51.

53. A smart energy management system for new energy vehicles, comprising: The drive device includes: An engine for selectively outputting power to the wheel ends of the vehicle; A drive motor, the drive motor being used to output power to the wheel ends; and A generator connected to the engine to generate electricity under the drive of the engine; A power battery for supplying power to the drive motor and for charging according to the current output by either the generator or the drive motor; Control device, the control device being used for: Based on the vehicle's starting point and ending point, at least one candidate energy-saving path is determined; wherein, the vehicle energy consumption predicted by the at least one candidate energy-saving path is less than the vehicle energy consumption predicted by other paths, and the vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path. In response to the selection operation of the at least one candidate energy-saving route, a preset travel route is determined; wherein, 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 of the route includes the total vehicle energy consumption of the multiple road segments; With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial SOC of the power battery and the vehicle's energy consumption for each of the multiple road segments, as well as the vehicle's actual overall needs, so that the engine operates within its high-efficiency range.

54. A vehicle comprising the new energy vehicle energy intelligent management system according to claim 53.

55. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method according to any one of claims 1-51.

56. A computer program product comprising a computer program adapted to be loaded by a processor and to execute the method according to any one of claims 1-51.

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