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

Abstract

This application discloses a method, system, and related equipment for intelligent energy management of new energy vehicles. Based on the vehicle's starting and ending points, at least one candidate energy-saving path is determined. The predicted energy consumption of the vehicle along at least one candidate energy-saving path is lower than that of other paths. The total energy consumption is 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 route is determined. The preset travel route includes multiple road segments, and the total energy consumption includes the energy consumption of each road segment. With the goal of minimizing fuel consumption along the preset travel route, the engine's operating state is controlled based on the initial state of charge (SOC) of the power battery in each road segment, the energy consumption of the road segment, and the vehicle's actual overall demand, ensuring the engine operates within its high-efficiency range. Using this application can reduce fuel consumption for users and improve the driving experience.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to intelligent energy management methods, systems and related equipment for new energy vehicles. Background Technology

[0002] 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. However, such energy management strategies, relying solely on the vehicle's operating conditions for energy control, often lead to increased fuel consumption and higher energy consumption. Summary of the Invention

[0003] This application provides 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.

[0004] In a first aspect, embodiments of this application provide a method for intelligent energy management of new energy vehicles, the method comprising:

[0005] Based on the vehicle's origin and destination, at least one candidate energy-saving path is determined; among them, 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, and the total vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path.

[0006] 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 multiple road segments.

[0007] 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.

[0008] Secondly, embodiments of this application provide an intelligent energy management system for new energy vehicles, the system comprising:

[0009] The drive unit includes an engine, a drive motor, and a generator. The engine is used to selectively output power to the wheel ends of the vehicle; the drive motor is used to output power to the wheel ends; and the generator is connected to the engine to generate electricity under the drive of the engine.

[0010] The power battery is used to power the drive motor and to charge it according to the current output by the generator or drive motor.

[0011] The control device determines at least one candidate energy-saving path based on the vehicle's starting point and ending point; wherein the vehicle energy consumption predicted by 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.

[0012] 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 multiple road segments.

[0013] 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.

[0014] Thirdly, embodiments of this application provide an intelligent energy management device for new energy vehicles, the device comprising:

[0015] The determination unit is used to determine at least one candidate energy-saving path based on the vehicle's starting point and ending point; wherein, the vehicle energy consumption predicted by 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.

[0016] The determining unit is also used to respond to the selection operation of at least one candidate energy-saving path and determine the preset travel path; wherein, 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 of the path includes the total vehicle energy consumption of multiple road segments.

[0017] The control unit is used to control the engine's operating state based on the initial SOC of the power battery in each road segment, the vehicle's energy consumption in the road segment, and the actual vehicle demand, with the goal of minimizing fuel consumption along the preset travel route, so that the engine operates in the high-efficiency range.

[0018] Fourthly, embodiments of this application 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.

[0019] Fifthly, embodiments of this application provide a vehicle that includes a new energy vehicle energy intelligent management system for performing the system described in the second aspect.

[0020] In a sixth aspect, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0021] In a seventh aspect, embodiments of this application 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 above-described method.

[0022] In this embodiment, a travel route with the lowest overall vehicle energy consumption is selected based on the vehicle's starting point 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

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0024] Figure 1 This is a schematic diagram of the architecture of an intelligent energy management system for new energy vehicles provided in an embodiment of this application;

[0025] Figure 2 This is a schematic diagram of the architecture of another intelligent energy management system for new energy vehicles provided in this application embodiment;

[0026] Figure 3 This is a schematic diagram of the architecture of another intelligent energy management system for new energy vehicles provided in this application embodiment;

[0027] Figure 4 This is a schematic diagram of a method for intelligent energy management of new energy vehicles provided in an embodiment of this application;

[0028] Figure 5 This is a schematic diagram of a candidate energy-saving path determination logic provided in an embodiment of this application;

[0029] Figure 6 This is a schematic diagram of energy consumption prediction provided in an embodiment of this application;

[0030] Figure 7 This is a schematic diagram of the logic of an energy consumption prediction method provided in an embodiment of this application;

[0031] Figure 8 This is a schematic diagram of a power replenishment strategy provided in an embodiment of this application;

[0032] Figure 9 This is a schematic diagram of road segment division provided in an embodiment of this application;

[0033] Figure 10 This is a schematic diagram of a predicted SOC provided in an embodiment of this application;

[0034] Figure 11 This is another schematic diagram of predicted SOC provided in the embodiments of this application;

[0035] Figure 12 This is a schematic diagram of energy management based on historical driving data provided in an embodiment of this application;

[0036] Figure 13 This is a schematic diagram of a partial correction logic for traffic light information fusion provided in an embodiment of this application;

[0037] Figure 14 This is a schematic diagram of the automatic navigation initial time update logic provided in an embodiment of this application;

[0038] Figure 15 This is a schematic diagram of another intelligent energy management method for new energy vehicles provided in this application embodiment;

[0039] Figure 16 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application;

[0040] Figure 17 This is a schematic diagram of an energy-saving path provided in an embodiment of this application;

[0041] Figure 18 This is a schematic diagram of a power replenishment plan provided in an embodiment of this application;

[0042] Figure 19 This is a schematic diagram of the structure of an intelligent energy management device for new energy vehicles provided in an embodiment of this application;

[0043] Figure 20 This is a schematic diagram of the structure of a control device provided in an embodiment of this application. Detailed Implementation

[0044] 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 numbers 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 application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0045] 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 application may have the same meaning or different meanings, the specific meaning of which must be determined by its interpretation in that specific embodiment or further in conjunction with the context of that specific embodiment.

[0046] 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 the following," etc., used in this application 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.

[0047] It should be understood that although the steps in the flowcharts of this application's embodiments 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. These sub-steps or stages 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.

[0048] 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).”

[0049] Figure 1 This is a schematic diagram of the architecture of a new energy vehicle energy intelligent management system provided in an embodiment of this application. (Reference) Figure 1 As shown, the intelligent energy management system for new energy vehicles may include: a drive unit (not shown), which 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. The control unit 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 unit may be implemented in the power domain control module (such as the VCU of the power domain control module), in the cockpit domain module (such as the host of the cockpit domain), or on a cloud server, or by the cooperation of several of the above modules; this application does not limit this.

[0050] 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. 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. 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; wherein the predicted total vehicle energy consumption of at least one candidate energy-saving path is lower than that 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, a preset travel path is determined; wherein 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, the operating state of the engine 10 is controlled 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, so that the engine operates in a high-efficiency operating range.

[0051] Specifically, engine 10 can be an Atkinson cycle engine. A clutch C1 is provided between engine 10 and the wheel end. Control device 50 controls the connection and disconnection between engine 10 and the wheel end by controlling the disengagement and engagement of clutch C1, so that engine 10 can selectively output power to the wheel end. This enables engine 10 direct drive, that is, engine 10 directly drives the wheel end. For example, when control device 50 controls clutch C1 to disengage, engine 10 is disconnected from the wheel end, and engine 10 does not directly output power to the wheel end. When control device 50 controls clutch C1 to engage, engine 10 is connected to the wheel end, and engine 10 directly outputs power to the wheel end, realizing engine 10 direct drive. Compared with traditional pure range-extended hybrid vehicles, this architecture has an engine direct drive path. This avoids the energy conversion losses caused by traditional pure range-extended hybrid vehicles lacking an engine direct drive path, even if the engine is very efficient (both engine speed and torque are efficient), and can only generate electricity through a generator to drive the drive motor. It also avoids the energy conversion losses caused by the power battery frequently operating in a charging and discharging state. This effectively improves the overall vehicle economy.

[0052] The drive motor 20 can be a flat-wire motor. The stator windings of a flat-wire motor use rectangular coils, which increases the stator slot fill factor and reduces the motor size, significantly improving the power density. The drive motor 20 is directly connected to the wheel end via gears. The control device 50 controls the drive motor 20 to output power to the wheel end. Optionally, the drive motor 20 and generator 30 are arranged in parallel. Compared to other arrangements, such as coaxial arrangement of the drive motor 20 and generator 30, the parallel arrangement in this embodiment places fewer demands on motor design, making it easier to arrange a high-power generator and reducing cost.

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

[0054] In some embodiments, when the control device 50 includes a power domain control module, the power domain control module is connected to the drive motor 20 and the generator 30 respectively. The power domain control module supplies power to the drive motor 20 according to the current output by the generator 30. The power battery 40 is connected to the power domain control module. The power battery 40 supplies power to the drive motor 20 through the power domain control module, or charges the drive motor 20 according to the current output by the generator 30 or the drive motor 20. The power domain control module controls the engine 10 to operate 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 wheels of the vehicle. The selective output of power to the wheels of the vehicle 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 efficient operating range, then the engine 10 is controlled to operate in the efficient operating range and drive the vehicle, or the engine 10 is controlled to drive the generator or the drive motor 20 to generate electricity, and the excess electricity is stored in the power battery 40 and output to the wheels of the vehicle; if the target SOC is greater than a certain threshold of the initial SOC ... If the initial SOC is a certain threshold, and the actual vehicle demand is greater than or equal to the engine's operating range, then the engine 10 is controlled to operate in the high-efficiency range and driven by the drive motor 20, or the drive motor 20 and engine 10 jointly drive the vehicle. The engine's high-efficiency 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 engine 10 jointly output power to the vehicle's wheels. If the target SOC is less than the initial SOC threshold, 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 new energy vehicle energy intelligent management system may further include a transmission 70 and a main reducer 80. See also... Figure 2 , Figure 2 This is a schematic diagram of the architecture of another new energy vehicle energy intelligent management system provided in an embodiment of this application, for reference. Figure 2 As shown, the transmission 70 may further include gears Z1, Z2, Z3, and Z4. The central shaft of gear Z1 is connected to one end of the clutch C1. Gear Z1 meshes with gear Z2, gear Z2 meshes with gear Z3, the central shaft of gear Z3 is connected to the drive motor 20, the central shaft of gear Z2 is connected to the central shaft of gear Z4, and gear Z4 meshes with the main reduction gear of the final reducer 80. Of course, the transmission 70 can also adopt other structures; specific details 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. 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. The control device 50 acquires the driving parameters of the hybrid vehicle. Optionally, the driving parameters include at least one of wheel-end torque demand, the state of charge (SOC) of the power battery 40, and the vehicle speed of the hybrid vehicle, wherein the wheel-end torque demand is also the overall vehicle torque demand. The control device 50 controls the engine 10, drive motor 20, and generator 30 according to the driving parameters to control the charging and discharging of the power battery 40, so that the engine 10 operates in the economic zone. For example, the control device 50 can select the operating mode with the lowest equivalent fuel consumption as the current operating mode of the hybrid vehicle by comparing the equivalent fuel consumption of the hybrid vehicle in series mode, parallel mode, and EV mode. It should be noted that the equivalent fuel consumption comparison is based on the engine 10 operating in its economic zone. For example, the engine 10 operates at 25kW in the economic zone. However, considering driving parameters such as wheel 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. Conversely, 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. Furthermore, equivalent fuel consumption refers to the sum of the fuel consumed by the engine 10 itself and the equivalent fuel consumed by the power battery 40. The equivalent fuel consumption can be obtained by converting the electrical charge consumed by the power battery 40 into fuel based on empirical values. When the power battery 40 is charging, the equivalent fuel consumption is negative; when the power battery 40 is discharging, the equivalent fuel consumption is positive.

[0057] 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. In series mode, the power output between engine 10 and the wheel ends is cut off (i.e., clutch C1 is disengaged), and engine 10 drives generator 30 to generate electricity and supply it to drive motor 20. In some cases, engine 10 also charges the power battery 40 with excess energy through generator 30. In parallel mode, the power is coupled between engine 10 and the wheel ends (i.e., clutch C1 is engaged). In some cases, engine 10 also charges the power battery 40 with excess energy through drive motor 20. In EV mode, neither engine 10 nor generator 30 operates, and power battery 40 supplies power to drive motor 20. Furthermore, when the hybrid vehicle operates in series, parallel, or EV mode, the charging and discharging of power battery 40 is controlled to ensure that engine 10 always operates in the economic zone. The equivalent fuel consumption comparison is also based on the engine 10 operating in the economic zone. This ensures that engine 10 operates in the high-efficiency zone throughout the entire operating range, 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 a large-capacity power battery, engine, drive motor and generator.

[0058] In some embodiments of this application, after a user selects a travel route, during the travel process, when the vehicle enters the current road segment, it interacts with other vehicles using speed planning functions within a certain range of the current road segment to exchange travel information. It should be noted that speed planning refers to the function of calculating a target speed, which is determined as follows: Using the minimum overall vehicle energy consumption along the route as the objective function, a speed sequence is generated based on the road traffic flow speed 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, including at least driving style, to obtain a corrected speed sequence. The corrected speed sequence is the target speed, which is the optimal energy-saving speed. By utilizing 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. This information improves the dimensionality and accuracy of the input information. When navigation and route planning are enabled, interaction data from nearby vehicles is collected to correct the navigation information. This interaction data is vehicle-to-vehicle (V2V) data. The most commonly used V2V data is vehicle speed, which can be communicated to surrounding vehicles via short-range wireless communication, enabling platooning and short-range predictive control. Furthermore, communication can also be achieved through "vehicle-cloud-vehicle" communication, i.e., wireless cloud services, without distance limitations, supplementing map navigation with vehicle speed and energy consumption prediction. The system proactively identifies special road conditions ahead, such as traffic light intersections, long uphill sections, and congested driving, and updates vehicle speed and SOC planning in a timely manner to ensure the vehicle operates at high efficiency. When navigation and route planning are not enabled, it collects interaction data from nearby vehicles and combines this data with the vehicle's historical data, as well as surrounding information collected by sensors such as LiDAR, millimeter-wave radar, and cameras, to make short-term predictions of the vehicle's future travel. Based on these short-term predictions, it performs optimization calculations to minimize energy consumption and reduce user fuel consumption.

[0059] During the trip, when the vehicle leaves the current road segment, please refer to [the relevant information]. Figure 3 , Figure 3 This is a schematic diagram of another intelligent energy management system for new energy vehicles provided in this application embodiment. Through the "vehicle-cloud" communication method, historical travel information is uploaded for relevant data statistical analysis, and other vehicles that have recently used the speed planning function can optimize their travel planning through the "vehicle-cloud-vehicle" method.

[0060] In some embodiments of this application, 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 a theoretical model and a data model with variable weights, to predict the vehicle's energy consumption for a user's preset route. Spatiotemporal alignment refers to using the preset travel route (distance or time) as the coordinate axis for the 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.

[0061] 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.

[0062] 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.

[0063] Based on the above description, please refer to Figure 4 , Figure 4 This is a schematic diagram of a method for intelligent energy management of new energy vehicles provided in an embodiment of this application, such as... Figure 4 The energy intelligent management method for new energy vehicles shown includes, but is not limited to, steps S401-S403, wherein:

[0064] S401. Based on the vehicle's starting point and ending point, determine at least one candidate energy-saving path; wherein, the vehicle energy consumption predicted by 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.

[0065] The candidate energy-saving path can be determined as follows:

[0066] In one implementation, 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; based on the total energy consumption of the vehicle on each candidate driving path, at least one candidate energy-saving path is determined from the at least one candidate driving path; wherein, 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.

[0067] In this embodiment, based on user behavior information and road condition information and energy consumption impact information for each candidate driving path, such as the energy consumption impact information including the user's driving style as aggressive driving, and the road condition information including the slope of the candidate driving path and the vehicle speed, the total energy consumption of the vehicle on each candidate driving path is predicted, and based on the total energy consumption of the vehicle on each candidate driving path, at least one candidate energy-saving path is determined from at least one candidate driving path.

[0068] In one implementation, 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.

[0069] In this embodiment, please refer to Figure 5 , Figure 5This is a schematic diagram of a candidate energy-saving path determination logic provided in an embodiment of this application. 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 selection model retains and displays the n routes according to a comprehensive weight score. For example, three routes are retained: 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 minimum energy consumption.

[0070] The different candidate driving routes are mainly selected based on the vehicle's current location and the user's navigation destination, taking into account the influence of travel factors from the starting point to the destination. These factors include estimated travel distance, estimated energy consumption, and estimated road traffic conditions. Big data analysis is conducted based on user travel experience and actual traffic flow impact. 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, the path distance is selected as the first travel dimension indicator based on the principle of shortest travel distance. Based on the road connectivity in the road network, the travel routes are arranged and combined according to the first travel dimension indicator to determine m drivable paths. At the same time, to meet the second travel dimension indicator, such as the shortest time, the route with the shortest time in the current combination is selected. At least one candidate driving route is determined from the m drivable paths.

[0071] In one implementation, the first travel dimension indicator includes the travel distance, and the second travel dimension indicator includes the travel time.

[0072] In one implementation, based on the second travel dimension index of m drivable paths, at least one candidate drivable path is determined from the m drivable paths. This 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 path as at least one candidate drivable path.

[0073] In this embodiment, based on the travel time constraint, the m candidate travel routes with the shortest travel time in the current combination are selected. The selection principle is based on 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.

[0074] In one implementation, at least one candidate driving route is determined based on the vehicle's origin and destination. This 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. The comprehensive travel indicator of the at least one candidate driving route is less than the comprehensive travel indicators of the other drivable routes within the at least one drivable route.

[0075] In this embodiment, 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...Ω are assigned. n For example, in short-distance travel, priority is given to shorter travel times, so time has a larger weight, while distance and energy consumption have a smaller weight. The comprehensive score of the alternative routes is obtained through weighted calculation, and a certain number of candidate travel paths are retained according to the scores.

[0076] Among them, 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.

[0077] Please see below. Figure 6 , Figure 6 This is a schematic diagram of energy consumption prediction provided in an embodiment of this application. Figure 6 In the image, 'a' represents the speed information shown on the map. The actual map data is not speed, but rather the distance and estimated travel time for each segment. The average speed for that segment is calculated, so it is discrete. Figure 6 The diagram in b is based on energy consumption prediction using map-revealed data. Since speed is discrete and energy consumption is directly related to speed, energy consumption is also discrete. Figure 6 The diagram in C shows the result of speed planning based on speed information revealed by the map. Speed ​​planning aims to control the vehicle to travel at a planned speed, so a continuous speed can reduce energy consumption. Therefore, the planning is discrete. Figure 6 The diagram below shows the results of energy consumption prediction based on planned speed. For the specific steps of energy consumption prediction based on map-revealed data, please refer to steps 1, 2, or 3 below; for the specific steps of energy consumption prediction based on planned speed, please refer to the corresponding steps in step 4 below.

[0078] 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.

[0079] In one implementation, the energy consumption impact information includes vehicle status information, which at least includes the vehicle's static parameters, and road condition information, which at least includes road traffic flow speed. The total 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, predicting the total vehicle energy consumption of a preset travel route based on road traffic flow speed and vehicle static parameters, where the total vehicle energy consumption along the route is the theoretical required energy consumption.

[0080] In one implementation, the vehicle's static parameters include at least: wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

[0081] In one implementation, the theoretical energy consumption requirement 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.

[0082] In this embodiment, please refer to Figure 7 , Figure 7 This is a schematic diagram of an energy consumption prediction method provided in an embodiment of this application. The method predicts the energy consumption of a travel route based on a fusion of vehicle theory and data-driven approaches, using predicted operating condition information. Vehicle theory primarily calculates the main range of energy consumption prediction to ensure that the data-driven approach does not deviate excessively. Road condition information may also include slope information. Vehicle static parameters include wind resistance, rolling resistance, acceleration resistance, and gradient resistance. These static parameters may also include inherent vehicle parameters affecting energy consumption, such as speed, curb weight, and frontal area.

[0083] Specifically, by acquiring vehicle parameters and considering load changes, if the vehicle is equipped with an Inertial Measurement Unit (IMU), acceleration is directly acquired; if the vehicle does not have an IMU, acceleration is estimated based on vehicle speed. Combined with throttle torque and vehicle acceleration, energy consumption prediction is theoretically calculated, i.e., theoretical energy consumption prediction for automobiles. Through offline training, 12 energy consumption prediction models are formed based on a specific vehicle model, driving style, and driving conditions. A model is matched based on driving conditions and driving style. Energy consumption is predicted using the matched model. If the predicted energy consumption is greater than the upper limit and corresponds to actual energy consumption data, that energy consumption prediction model is confirmed. The model is then trained in the cloud based on actual energy consumption data, and the model parameters are updated. If the predicted energy consumption is not greater than the upper limit, energy consumption prediction is performed through theoretical calculations and data-driven prediction. The theoretical calculations provide theoretical energy consumption prediction for automobiles, while the data-driven prediction uses the target energy consumption prediction model. Energy consumption prediction is performed based on energy consumption prediction theory and data-driven energy consumption prediction. If the actual energy consumption is within a certain range, the weights are adjusted. If the actual energy consumption is outside a certain range, it is determined whether the model is correctly matched. If not, it is re-matched among 12 energy consumption prediction models. If it is correct, the model is uploaded to the cloud server to retrain and the parameters are updated to the vehicle.

[0084] 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.

[0085] 2. Input the road type, driving style and vehicle model 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 the road type of the preset travel route and / or the user's driving style information.

[0086] In one implementation, the energy consumption impact information includes vehicle status information, which at least includes the user's driving style and vehicle model information, and road condition information, which at least includes the road type. The total vehicle energy consumption for a given 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 then outputting the predicted total vehicle energy consumption for the preset travel route from the target energy consumption prediction model. The total vehicle energy consumption for the route serves as a reference demand energy consumption. The target energy consumption prediction model is determined from multiple preset energy consumption prediction models based on the road type and / or the user's driving style information for the preset travel route.

[0087] In this embodiment, vehicle model information refers to vehicle parameters, and the target energy consumption prediction model is the data-driven part. Besides considering driving style and conditions, the data-driven part also considers vehicle air conditioning usage, battery thermal management system and power consumption of low-voltage accessories such as lights, instruments, fans, water pumps, multimedia audio-visual systems, seat heating, and seat ventilation; weather conditions ahead, such as temperature, humidity, and wind speed; terrain conditions ahead, such as overpasses, slopes, air resistance, and track resistance; distribution of refueling and charging locations; and charging conditions at the destination. The data-driven approach first uses machine learning algorithms to derive the energy consumption prediction model through offline training. Then, it performs online learning by performing data closure based on real-time data. When the model error continuously exceeds 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.

[0088] In one implementation, road types include: ordinary roads, expressways, highways, and congested roads.

[0089] In one implementation, the user's driving style is categorized as aggressive, normal, and mild based on the rate of change of accelerator pedal opening and the rate of change of acceleration.

[0090] In this embodiment, please refer to Figure 7Due to significant differences in energy consumption among different vehicles and drivers, a two-dimensional clustering analysis was performed on all driving behavior data for a specific vehicle model, categorized by driving style and driving conditions. Driving conditions were divided into ordinary roads, expressways, highways, and congested roads. Driving styles were categorized as aggressive, normal, and mild based on the rate of change of accelerator pedal opening and acceleration. This two-dimensional cross-segmentation resulted in 12 groups of driving data for this vehicle model. Based on these 12 groups, algorithms such as random forests were used to train energy consumption prediction models offline, resulting in 12 different parameter models representing energy consumption prediction models under different classification groups. The obtained models were compressed and deployed in the vehicle-side controller, along with a driving style recognition algorithm that dynamically identifies the driver's driving style and driving conditions, calling the corresponding model to predict energy consumption for the travel route. Furthermore, after actual driving behavior occurs, the predicted energy consumption is compared with the actual energy consumption. Driving behavior data with errors exceeding a certain threshold is uploaded to the cloud, triggering cloud-based prediction model training and updating the corresponding energy consumption prediction model, thus achieving closed-loop data learning.

[0091] 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.

[0092] 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.

[0093] In one implementation, energy consumption impact information includes vehicle status information, which at least includes vehicle static parameters, vehicle model information, and user behavior information, which at least includes the user's driving style. 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 road traffic flow speed and vehicle static parameters, the total vehicle energy consumption of the preset travel route is predicted, and 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: 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, and 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 the road type of the preset travel route and / or the user's driving style information.

[0094] In one implementation, the total energy consumption of the vehicle along a preset travel route is predicted based on the vehicle's theoretical energy consumption and reference energy consumption along the preset travel route. This can be achieved 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 and second weights to predict the vehicle's total energy consumption along the route.

[0095] In this embodiment, the vehicle's total energy consumption along the route is predicted by weighting the determined theoretical energy consumption demand with a first weight and the reference energy consumption demand with a second weight. 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.

[0096] In one implementation, the first weight of the theoretical energy demand and the second weight of the reference energy demand are added together to 1. With the constraint that the actual vehicle energy consumption on the road segment is within a preset range, the first weight of the theoretical energy demand and the second weight of the reference energy demand are updated 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.

[0097] In one implementation, if the error between the predicted total energy consumption of a vehicle on road segment n and the actual total energy consumption of a vehicle on road segment n is greater than a certain threshold, then the actual total energy consumption of the vehicle on road segment n and the model identifier of the target energy consumption prediction model are sent to the server, so that the server can optimize the energy consumption prediction model corresponding to the model identifier based on the actual total energy consumption of the vehicle on road segment n.

[0098] In this embodiment, 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 as an offline model; otherwise, the parameters of the energy consumption prediction model corresponding to the model identifier continue to be used.

[0099] In one implementation, the division of road segments 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.

[0100] In this embodiment, the preset travel route can be divided into multiple road segments based on road condition information, including road type and congestion level. Based on road type, it can be divided into urban road segments, rural road segments, etc., and based on congestion level, it can be divided into expressway segments or congested road segments, etc.

[0101] In one implementation, 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.

[0102] In one implementation, the vehicle's total energy consumption on the nth road segment is predicted using the following method:

[0103] 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.

[0104] In this embodiment, the first weight of the theoretical energy demand of the nth road segment and the 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.

[0105] In one implementation, after the vehicle passes through the nth road segment, the actual vehicle energy consumption of the vehicle in the nth road segment is obtained; if the actual vehicle energy consumption of the road segment is within the threshold range, the first weight and the second weight remain unchanged, and the threshold range is determined based on the predicted vehicle energy consumption of the vehicle in the nth road segment.

[0106] In this embodiment, 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.

[0107] In one implementation, the theoretical energy consumption and reference energy consumption of the vehicle on the nth road segment are obtained; based on the first initial weight of the theoretical energy consumption and the first initial weight of the reference energy consumption, the theoretical energy consumption and reference energy consumption on the nth road segment are weighted to obtain the first reference road segment vehicle energy consumption; after the vehicle passes through the nth road segment, the actual road segment vehicle energy consumption on the nth 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.

[0108] In this embodiment, the theoretical and reference energy consumption requirements of a vehicle on road segment n are weighted and calculated to obtain the total vehicle energy consumption for the 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 for the first reference road segment. If the actual total vehicle energy consumption is greater than the total vehicle energy consumption for the first reference road segment, the target energy consumption prediction model is optimized to improve accuracy.

[0109] In one implementation, the theoretical energy consumption and reference energy consumption of the vehicle on the nth road segment are obtained; based on the second initial weight of the theoretical energy consumption and the second initial weight of the reference energy consumption, the theoretical energy consumption and reference energy consumption on the nth road segment are weighted to obtain the second reference road segment vehicle energy consumption; after the vehicle passes through the nth road segment, the actual road segment vehicle energy consumption on 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.

[0110] In this embodiment, the theoretical and reference energy consumption requirements of a vehicle on road segment n are weighted and calculated to obtain the total vehicle energy consumption for the second reference road segment. After the vehicle completes its journey on the road segment, the actual total vehicle energy consumption for the road segment and the total vehicle energy consumption for the second reference road segment are compared. If the actual total vehicle energy consumption for the road segment is less than the total vehicle energy consumption for the second reference road segment, the target energy consumption prediction model is optimized to improve accuracy.

[0111] In one implementation, obtaining the first weight of the theoretical energy consumption demanded by the vehicle on the nth road segment and the second weight of the reference energy consumption demanded can be achieved by: obtaining the theoretical energy consumption demanded and the reference energy consumption demanded by the vehicle on the nth road segment in a preset travel route; performing a weighted calculation on the theoretical energy consumption demanded and the reference energy consumption demanded based on the first initial weight of the theoretical energy consumption demanded and the first initial weight of the reference energy consumption demanded to obtain the first reference road segment total vehicle energy consumption demanded for the nth road segment; and performing a weighted calculation on the theoretical energy consumption demanded and the reference energy consumption demanded based on the second initial weight of the theoretical energy consumption demanded and the second initial weight of the reference energy consumption demanded to obtain the first reference road segment total vehicle energy consumption demanded for the nth road segment. The initial weights are used to perform a weighted calculation on the theoretical energy consumption and reference energy consumption of the nth road segment to obtain the second reference road segment's total vehicle energy consumption for the nth road segment. After the vehicle has traveled the nth road segment, the actual road segment's total vehicle energy consumption is obtained. If the actual road segment's total vehicle energy consumption is greater than the second reference road segment's total vehicle energy consumption but less than the first reference road segment's total vehicle energy consumption, then the first and second weights are updated, and the updated first weight is used as the current first weight for the theoretical energy consumption, and the updated second weight is used as the current second weight for the reference energy consumption.

[0112] In this embodiment, please refer to Figure 7The preset distance L is 5km, the first weight is ω1, the second weight is ω2, and the theoretical energy consumption requirement is E. 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.

[0113] 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.

[0114] In one implementation, the 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. The 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, 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.

[0115] In one implementation, based on the energy consumption prediction algorithm of automotive theory, the total energy consumption of a preset travel route is predicted according to driving style, road traffic flow speed, vehicle static parameters, and the target speed that minimizes the total energy consumption of the route.

[0116] In this embodiment, the 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; the road condition information includes at least the road traffic flow speed; and the user behavior information includes at least the user's driving style. After 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 of 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.

[0117] In one implementation, the 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; the energy consumption impact information includes vehicle status information, or the energy consumption impact information includes at least one of the following: 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.

[0118] In one implementation, 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. Alternatively, when the intelligent driving function is activated and the 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. Alternatively, 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, 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.

[0119] In one implementation, 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 the 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.

[0120] In one implementation, the energy-saving driving guidance function refers to a function used to control and guide the vehicle to travel at the target speed that minimizes the overall vehicle energy consumption along the route.

[0121] 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.

[0122] In one implementation, the speed sequence is modified based on constraints to obtain a modified speed sequence, the constraints including at least driving style.

[0123] In one implementation, the limiting conditions may 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.

[0124] In this embodiment, 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. Traffic light information includes: traffic light countdown timer, distance to the traffic light, etc. The maximum permissible speed in the area is the road segment speed limit. Acceleration and deceleration limits can be determined based on traffic flow speed. 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.

[0125] In one implementation, 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, and sand road types, as well as 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.

[0126] In this embodiment, 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.

[0127] In one implementation, 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 used as the initial speed solution and input into the vehicle model. The vehicle model generates a speed sequence based on the initial speed solution with the objective function of minimizing the total vehicle energy consumption along the route.

[0128] In this embodiment, a vehicle model based on a state-space matrix is ​​constructed according to vehicle dynamics. Since the model has nonlinear terms, the area near the working point is linearized to obtain a multi-segment working model. The selection of the working point is determined by the working torque range. The wheel-end torque requirement 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.

[0129] 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.

[0130] In one implementation, a smooth speed sequence is determined based on the road traffic flow speed, current vehicle speed, and constraint information of a preset travel route. This smooth speed sequence is then 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 based on 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 then inputting the smooth speed sequence as the initial speed solution into the vehicle model.

[0131] In this embodiment, the average speed is obtained based on the road traffic flow speed, current vehicle speed, and constraint information of the 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.

[0132] The driving style used is the same as that in the energy consumption prediction model, categorized as aggressive, normal, and mild. Aggressive driving increases traffic flow speed on all road segments; normal driving maintains existing traffic flow speeds on all road segments; and mild driving reduces traffic flow speeds on all road segments. The local correction for traffic light positions is as follows: at a certain distance from the traffic light, acceleration is applied at A1, A2, and A3, respectively, or deceleration at B1, B2, and B3, depending on whether the driving style is aggressive, normal, or mild. Where A1 > A2 > A3, and |B1| > |B2| > |B3|.

[0133] 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.

[0134] 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.

[0135] In one implementation, the speed of road segments in different driving scenarios is corrected 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 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.

[0136] In this embodiment, 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 vehicle following model uses the goal of minimizing the overall vehicle energy consumption on the road segment and the relative change value of acceleration being less than a preset acceleration threshold as the objective function to obtain the speed sequence.

[0137] In one implementation, the speed of road segments in different driving scenarios is corrected 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.

[0138] In this embodiment, 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. This generates a locally corrected smooth speed sequence, which takes into account vehicles ahead, pedestrians, and other possible obstacles to optimize vehicle driving efficiency and reduce energy consumption.

[0139] 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.

[0140] In this embodiment, please refer to Figure 5By collecting historical road data samples from users, a data-driven road feature sample database is constructed. This database records historical road information. By comparing the current road type with historical data features, if the current road matches the historical data features, the corresponding historical route is directly extracted and output. The historical data features include road types such as urban highways and provincial roads, the road segment, and GPS coordinates. The database is used to analyze the matching of current candidate routes to determine if there are overlapping road segments. If so, the historical route is retained as a candidate route, and the time information, distance information, and other information of the historical route are obtained.

[0141] If the path does not exist, the time information and distance information of the path are calculated and stored in the road feature sample database.

[0142] Furthermore, information on various road segments, traffic information, and vehicle information is extracted from different routes. The information on each road segment includes road speed limits, distance, and gradient. The traffic information includes traffic flow and vehicle speed information. These 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.

[0143] S402, in response to the selection operation of at least one candidate energy-saving route, determine the preset travel route; 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 multiple road segments.

[0144] In one optional implementation, if the navigation system's auto-start function is enabled and the current system time is within a preset vehicle usage time period, the navigation system is automatically turned on, and the preset travel route is determined based on the vehicle's current location information.

[0145] In this embodiment, when the navigation system's auto-start function is enabled, and the user (i.e., the car owner) sets the time period for using the vehicle, 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.

[0146] In one implementation, if the navigation system's auto-start function is disabled, it responds to the user's input destination and determines the preset travel route.

[0147] When determining the preset travel route, it is also necessary to consider the vehicle's remaining driving range and the driving range to the destination. If the vehicle's remaining driving range is less than the driving range to the destination, a refueling strategy is determined during the journey along the preset travel route. That is, when the driving range to the destination is greater than the vehicle's remaining driving range L based on predicted energy consumption, a refueling strategy is determined during the journey along the preset travel route.

[0148] In one implementation, determining the energy replenishment 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 when in a fatigued 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.

[0149] In this embodiment, please refer to Figure 8 , Figure 8 This is a schematic diagram of a charging strategy logic provided in an embodiment of this application. When the mileage to the destination is greater than the vehicle's remaining combined fuel and electric mileage L based on predicted energy consumption, the route with the minimum 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...N in the search route are selected. n Candidate refueling addresses M1, M2, M3...M in the search route m The remaining combined fuel and electric mileage L is updated based on energy consumption prediction. This includes M1, M2, M3...M m N1, N2, N3...N n Based on the distance to the driver's rest location, the system plans refueling and charging by judging the driver's fatigue mileage, the mileage of charging address distribution, and the remaining combined fuel and electric mileage. The recommended charging and refueling plan is then displayed 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, which is the driver's longest driving mileage Lmax. The remaining combined fuel and electric mileage is updated by an energy consumption prediction method.

[0150] In one implementation, 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; 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.

[0151] In this embodiment, please refer to Figure 8The first preset distance threshold is defined as the first threshold value, and the first charging address is defined as N1. When the remaining driving range L is greater than Lmax, it is further determined that N1 - Lmax is less than the first threshold value. If it is less, charging is performed at the nearest charging address N1. When the remaining driving range is less than the first threshold value, the mileage driven by driver fatigue can be ignored, and charging is performed at the nearest charging address N1.

[0152] In one implementation, based on the fatigue driving mileage and the vehicle's remaining mileage, the system recommends 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.

[0153] In this embodiment, please refer to Figure 8 When the remaining driving range L is greater than Lmax, and N1 - Lmax is greater than or equal to the first threshold, then charging will proceed to the next charging address corresponding to N1, i.e., the second charging address. If the remaining range is greater than or equal to the first threshold, then fatigue driving mileage cannot be ignored, and the vehicle will proceed to the next charging address corresponding to N1 to ensure driving safety.

[0154] In one implementation, 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 less than a second preset distance threshold, then controlling the vehicle to drive to a third charging address for charging; wherein, 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, and 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 mileage.

[0155] In this embodiment, please refer to Figure 8 The second preset distance threshold, also known as the second threshold, determines the charging location. If the remaining electric / fuel mileage Lremaining is less than Lmax, then Lremaining - Lmax is less than the second threshold. If it is less, the vehicle is charged at the nearest charging address before Lremaining. If it is less than the second threshold, charging is prioritized to ensure driving safety.

[0156] In one implementation, based on fatigue driving mileage and the vehicle's remaining mileage, the system recommends 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; wherein, 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, and 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.

[0157] In this embodiment, please refer to Figure 8 If the remaining driving range Lremaining is less than Lmax, and Lremaining - Lmax is greater than or equal to the second threshold, then refuel at the nearest refueling address before Lremaining. If Lremaining is greater than or equal to the second threshold, then refueling will proceed to ensure the shortest possible travel time.

[0158] 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.

[0159] In this embodiment, 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.

[0160] In one implementation, 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 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; planning the target SOC for each road segment based on the initial SOC of the power battery 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, thereby ensuring the engine operates within its efficient operating range.

[0161] In one implementation, the target SOC for each road segment is planned based on the initial SOC of the power battery and the vehicle energy consumption of the 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 the road segment; determining multiple SOC change paths based on the predicted SOC change, wherein each SOC change path includes a set of SOCs; identifying the SOC change path that minimizes fuel consumption during the planned travel route from among the multiple SOC change paths as the target SOC change path; and defining the SOCs included in the target SOC change path as the target SOC for each road segment.

[0162] In this embodiment, 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.

[0163] In one implementation, the target SOC at the end of the first segment of the preset travel route is determined based on the vehicle's initial SOC on the preset travel route and the predicted SOC change 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 predicted SOC change of the non-first segment and the target SOC at the end of the segment preceding the non-first segment.

[0164] In this embodiment, the target SOC at the end of the first road segment is determined based on the vehicle's initial SOC along the preset travel route and the predicted SOC change for the first road segment. The predicted SOC change refers to the change in battery charge after the vehicle has traveled the first road segment. By combining the initial SOC and the predicted SOC change, the target SOC at the end of the first road segment can be calculated. The target SOC at the end of a non-first road segment is determined based on the predicted SOC change for that road 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 road segment. Since the vehicle's SOC state is a continuously changing process, the impact of the previous journey needs to be considered when considering the target SOC for the current road segment. By combining the predicted SOC change and the target SOC of the previous segment, the target SOC at the end of a non-first road segment can be determined.

[0165] In one implementation, 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 the non-first segments of the preset travel path is determined based on the first predicted SOC change of the non-first segments and the upper limit of the target SOC of the segment preceding the non-first segment; the lower limit of the target SOC for the non-first segments is determined based on the second predicted SOC change of the non-first segments and the lower limit of the target SOC of the segment preceding the non-first segment.

[0166] In one implementation, 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 for 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 along 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.

[0167] In this embodiment, if the target road segment is the first segment of the preset travel route, its first predicted SOC range is determined based on the vehicle's SOC at the start of the route and the predicted SOC change of that segment, ensuring that the current state of the power battery at the start of the trip and the predicted energy consumption of that segment are considered. If the target road 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 road segment, ensuring that the impact of the previous segment is considered when calculating the target SOC. If the target road segment is the last segment of the preset travel route, its second predicted SOC range will be the battery's final SOC when the vehicle reaches the end of the route, ensuring that the expected state of charge of the power battery at the end of the trip is considered when determining the second predicted SOC range. If the target road 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 road segment, as well as the predicted SOC change of the segment following the target road segment, ensuring that the expected impact of the subsequent segment is considered when calculating the target SOC.

[0168] In one implementation, 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.

[0169] In one implementation, 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.

[0170] In this embodiment, for example, the target road segment is a highway, and the vehicle's total energy consumption is 10 kWh per kilometer. On highways, NVH power limits are lower because the road surface is relatively flat, and engine noise and vibration are relatively low, assumed to be 5 kW. The maximum charge / discharge power of the vehicle's battery is assumed to be 50 kW. Therefore, the charge / discharge power range on highways is determined to be 5 kW to 50 kW. Highways are generally flat and have smooth traffic, so the predicted energy consumption on highways is relatively low, for example, the total vehicle energy consumption along the route is 8 kWh per kilometer. The predicted SOC change is calculated based on the charge / discharge power range and the total vehicle energy consumption along the route. Assuming the system predicts that for every kilometer traveled on a highway, the battery's SOC change is -0.1 (meaning the battery SOC decreases by 0.1 for every kilometer traveled), the predicted SOC change for the target road segment is determined to be -0.1, i.e., the battery SOC decreases by 0.1 for every kilometer traveled.

[0171] In one implementation, the endpoint SOC is determined based on the initial SOC of the vehicle's power battery at the starting point of the preset travel route.

[0172] In this embodiment, the final SOC is determined based on the initial SOC of the vehicle's power battery at the starting point of the preset travel route. That is, the final SOC is obtained by subtracting the predicted change in SOC from the initial battery SOC.

[0173] In one implementation, if the starting SOC is greater than or equal to a first preset threshold, the ending SOC is a second preset threshold; if the starting SOC is less than the first preset threshold, the ending SOC is the first preset threshold; wherein, the second preset threshold is greater than the first preset threshold.

[0174] In this embodiment, 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%.

[0175] In one implementation, the vehicle's preset travel route is divided into at least one road segment.

[0176] Determine the target state of charge (SOC) of the vehicle when it is traveling on each road segment.

[0177] The vehicle's engine and motor are controlled based on the actual and target SOC of the vehicle's power battery.

[0178] One road segment can correspond to one target SOC. 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. Therefore, a road segment can be divided into multiple sections, each 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.

[0179] In one implementation, the SOC of the target road segment in the preset travel route is determined based on the first predicted SOC range and the second predicted SOC range of the target road segment;

[0180] When the target road segment is the first road segment of the preset travel route, the first predicted SOC range of the target road segment is determined based on the change in the vehicle's initial SOC on the preset travel route and the predicted SOC of the target road segment.

[0181] If the target road segment is not the first road segment of the preset travel route, the first predicted SOC range of the target road segment is determined based on the upper and lower limits of the target SOC of the previous road segment and the change in the predicted SOC of the target road segment.

[0182] 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.

[0183] 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.

[0184] In one implementation, 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.

[0185] In one implementation, the target road segment in the preset travel route includes at least one sub-road segment, and each sub-road segment corresponds to sub-traffic information. The target road segment is determined based on the sub-traffic information of the at least one included sub-road segment.

[0186] The target road segment can be any segment of a preset travel route. For example, each segment of a preset travel route includes at least one sub-segment, and each sub-segment corresponds to sub-traffic condition information. The sub-traffic condition information describes the traffic conditions of the corresponding sub-segment. In this embodiment, each road segment is determined based on the sub-traffic condition information of at least one of its sub-segments.

[0187] Specifically, after determining the preset travel route, the sub-segments belonging to the preset travel route and the corresponding sub-traffic information for each sub-segment can be obtained from the map. Then, based on the sub-traffic information of each sub-segment, several adjacent sub-segments can be selected and concatenated to obtain a single road segment. For example, suppose the map outputs a preset travel route including 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 sub-traffic information of each sub-segment, sub-segment 1 is determined as road segment 1, adjacent sub-segments 2 and 3 are concatenated to form road segment 2, adjacent sub-segments 4, 5, and 6 are concatenated to form road segment 3, and adjacent sub-segments 7, 8, 9, and 10 are concatenated to form road segment 4, thus dividing the preset travel route into four road segments. Since each sub-segment has its own road conditions, the preset travel route can be divided into at least one segment based on the road conditions of each sub-segment, so that each segment also has its own road conditions.

[0188] 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, this embodiment merges 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).

[0189] In one implementation, the sub-road condition information includes at least one of the following: road type, road name, road traffic signs, road speed limit, congestion level, distance length, travel time required, average speed, gradient, traffic light information, and weather information. The road type can include ordinary roads, expressways, highways, and congested roads. The congestion level can include high, medium, and low to reflect different degrees of road congestion. The travel time required is the time required for a vehicle to travel from the beginning to the end of the sub-road segment, which can be obtained through big data analysis of historical data from multiple vehicles traveling on the sub-road segment. The average speed is the average speed of vehicles traveling on the sub-road segment; for example, it could be the average speed of vehicles using the aforementioned vehicle control method that have previously traveled on the sub-road segment, or it could be the average speed of multiple vehicles traveling on the sub-road segment. For example, if vehicle 1 travels at an average speed of 10 m / s on the sub-road segment, vehicle 2 travels at an average speed of 11 m / s on the sub-road segment, and vehicle 3 travels at an average speed of 9 m / s on the sub-road segment, then based on the average speeds of vehicle 1, vehicle 2, and vehicle 3, the average speed of the sub-road segment can be determined to be (10 + 11 + 9) / 3 = 10 m / s.

[0190] In one implementation, the sub-road condition information includes road type, and all sub-road segments included in the target road segment have the same road type; and / or the sub-road condition information includes average speed, and the average speed of all sub-road segments included in the target road segment belongs to the same speed range.

[0191] Specifically, the requirement that all sub-segments included in the target road segment have the same road type can be understood as follows: if a road segment includes two sub-segments, then the two sub-segments have the same road type. The requirement that the average speed of all sub-segments included in the target road segment falls within the same speed range can be understood as follows: 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. 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 speed of all sub-segments included in the target road segment falls within the same speed range—can exist individually or simultaneously.

[0192] In one implementation, 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, then combining the pre-divided road segment and the adjacent sub-road segments as a road segment in a preset travel route.

[0193] In this embodiment, a vehicle or server can divide a preset travel route into at least one road segment, and the vehicle obtains the segmentation result. During the segmentation process, determining any one 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.

[0194] For example, a pre-defined travel route includes sub-segment 1, sub-segment 2, sub-segment 3, sub-segment 4, and sub-segment 5. Sub-segments 1, 2, and 3 have the same road type, sub-segments 4 and 5 have the same road type, and sub-segments 3 and 4 have different road types. Based on this method of determining road segments, sub-segments 1, 2, and 3 are first combined into a pre-defined road segment 1, and sub-segments 4 and 5 are combined into another pre-defined road segment 2. Assuming that the average vehicle speed of sub-segments 1, 2, 3, and 4 is 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.

[0195] In one implementation, 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.

[0196] Specifically, 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.

[0197] 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.

[0198] In one implementation, 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.

[0199] Once a user determines a preset travel route based on the map displayed on the terminal screen, the system automatically retrieves road condition data for that route, such as speed limits, gradients, and other signal data. This data is then statistically analyzed, and the preset travel route is divided into several road segments. Furthermore, based on big data analysis, historical vehicle speeds and accelerations are obtained when vehicles pass through these road segments. The vehicle speeds and accelerations retrieved from the big data database are then used to calculate the corresponding average speed, average acceleration, speed standard deviation, and acceleration standard deviation, among other road characteristic parameters, using basic formulas. The calculated road characteristic parameters are compared with pre-stored preset road condition data. Road condition data matching the road characteristic parameters of a given road segment is identified as the corresponding preset road condition, thus dividing the vehicle's preset travel route into several road segments.

[0200] 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 the obtained road condition data for the preset travel route includes speed limits of 60 km / h and 80 km / h, the preset travel route can be divided into road segment a corresponding to the 60 km / h speed limit and road segment b corresponding to the 80 km / h speed limit. Taking road characteristic parameters including average vehicle speed, average acceleration, speed standard deviation, and acceleration standard deviation as an example, firstly, historical driving parameters such as vehicle speed and acceleration are obtained based on big data analysis when vehicles travel through road segment a. Then, the average vehicle speed, average acceleration, speed standard deviation, and acceleration standard deviation of vehicles passing through road segment a are calculated according to the average and standard deviation calculation formulas. These are compared with the pre-stored road condition data corresponding to the preset road conditions. When the calculated road characteristic parameters are within the range of the preset road condition data, the road segment is determined as a road segment. Similarly, the road segment corresponding to road segment b can be obtained.

[0201] In one implementation, 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.

[0202] Specifically, the neural network model can be pre-trained so that after inputting traffic data for the preset travel route, the neural network model can output at least one segment of the preset travel route, forming a segment sequence.

[0203] In one implementation, 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] In one implementation, 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.

[0208] 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.

[0209] In one implementation, 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.

[0210] 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.

[0211] In one implementation, the method for determining the target SOC includes: obtaining the initial SOC of the vehicle's power battery along a preset travel route; and determining the target SOC for each road segment based on the initial SOC and road condition information for each road segment.

[0212] In this embodiment of the application, 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. Please refer to [link to relevant documentation]. Figure 9 , Figure 9 This is a schematic diagram of road segment division provided in an embodiment of this application, such as... Figure 9As shown, the preset travel route, or pre-trip road, includes road segments 1, 2, 3, and 4. The starting point of the preset travel route is point A of road segment 1. This means that when the vehicle reaches point A, the actual SOC of the power battery is the initial SOC of the preset travel route. Road condition information reflects the road conditions of the corresponding road segments. Based on the initial SOC and the road condition information of each road segment, the target SOC for each road 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 road segment in mind, so that when the vehicle completes the journey, 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.

[0213] In one implementation, both sub-road condition information and road condition information include road types; 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, wherein 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.

[0214] Specifically, 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.

[0215] In one implementation, 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.

[0216] Specifically, for a road segment, such as 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 the 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.

[0217] In one implementation, 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.

[0218] In this embodiment, for example, 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%. Combining road condition information, user behavior information, and vehicle status information of the preset travel route, the predicted vehicle energy consumption for the highway section is 480 kWh. The vehicle energy consumption for the urban road section is 240 kWh. The vehicle energy consumption for the rural road section is 180 kWh. Based on the initial SOC of the power battery and the vehicle energy consumption for each road segment, with the goal of minimizing fuel consumption along the preset travel route, the target SOC of the power battery for each road segment is determined. For example, for the highway section: 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 the speed is slower, so SOC can be increased by recovering braking energy, and the target can be set to keep the SOC above 60%; rural roads have lower energy consumption, but the road conditions may be more complex, requiring a certain SOC reserve, for example, the target is to keep the SOC above 55%.

[0219] In one implementation, 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 change in SOC 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 change in SOC of each road segment.

[0220] In this embodiment, 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.

[0221] In one implementation, 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.

[0222] Specifically, assuming k=5, since the endpoint SOC is already determined based on the starting SOC, it can be directly used as the target SOC for the 5th road segment. After determining the target SOC of the 5th road segment, the target SOC of the 4th road segment can be calculated based on its target SOC and the change in SOC. Then, based on the target SOC and the change in SOC of the 4th road segment, the target SOC of the 3rd road segment can be calculated, and so on, for the 3rd, 2nd, and 1st road segments. For example, if the target SOC of the 4th road segment is 40% and the change in SOC of the 4th road segment is 5%, then the target SOC of the 3rd road segment = the target SOC of the 4th road segment - the change in SOC of the 4th road segment = 40% - 5% = 35%.

[0223] In one implementation, 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.

[0224] Specifically, the energy consumption per unit distance can be determined using historical vehicle data. For example, if a vehicle previously traveled on road segment B, the actual energy consumption per unit distance during that trip would be '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.

[0225] In one implementation, 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.

[0226] The pre-defined table stores the correspondence between road type, congestion level, and power consumption per unit distance. Based on this correspondence, the power consumption per unit distance can be retrieved according to the road type and congestion level of a road segment.

[0227] In one implementation, after the vehicle has traveled 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.

[0228] Specifically, after a vehicle travels a preset distance, 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 is obtained; and the power consumption per unit distance in the preset table is updated based on the actual power consumption per unit distance.

[0229] For example, the preset distance length can be 1 kilometer. For every 1 kilometer the vehicle travels, its actual power consumption on that 1-kilometer stretch of road can be obtained, thus yielding the actual power consumption per unit distance. In the preset table, the power consumption per unit distance corresponding to the road type and congestion level of that 1-kilometer stretch is the power consumption per unit distance to be updated. In this embodiment, 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.

[0230] In one implementation, the power consumption per unit distance to be updated in the preset table is updated to the actual power consumption per unit distance.

[0231] 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.

[0232] In one implementation, 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.

[0233] Specifically, the sum of the first weight and the second weight equals 1. The unit distance energy consumption to be updated is multiplied by the first weight to obtain the first product, and the actual unit distance energy consumption is multiplied 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.

[0234] In one implementation, 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.

[0235] Specifically, 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.

[0236] In one implementation, 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.

[0237] In this embodiment, road condition information reflects 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 by both the initial SOC and the road conditions. After determining the target SOC at the end of each road segment, a specific SOC can be selected from the target SOC at the end of each road segment as the target SOC for that road segment.

[0238] In one implementation, multiple SOC change paths can be determined based on the target SOC, wherein each SOC change path includes a set of SOCs; the SOC change path that minimizes energy consumption of vehicles when running on the preset travel route among the multiple SOC change paths 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.

[0239] Specifically, for example, given 5 road segments, randomly selecting one SOC from the target SOC of each segment yields a set of SOCs, comprising 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.

[0240] In one implementation, 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 segment other than the first segment of the preset travel route is determined based on the traffic information of the segment other than the first segment and the target SOC at the end of the segment preceding the segment other than the first segment.

[0241] Specifically, based on the initial SOC and the road condition 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 road condition 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.

[0242] 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.

[0243] In one implementation, the upper and lower limits of the target SOC for the first road segment are determined based on the initial SOC and the road condition information of the first road segment; the upper limit of the target SOC for non-first road segments is determined based on the road condition information of non-first road segments and the upper limit of the target SOC of the road segment preceding the non-first road segment; and the lower limit of the target SOC for non-first road segments is determined based on the road condition information of non-first road segments and the lower limit of the target SOC of the road segment preceding the non-first road segment.

[0244] Specifically, 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 at the end of the vehicle's operation in hybrid mode on 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 at the end of the vehicle's operation in pure electric mode on 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.

[0245] 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.

[0246] Please see Figure 10 , Figure 10 This is a schematic diagram of a predicted SOC provided in an embodiment of this application. Figure 10 Taking the road segment division shown as an example, the first road segment is segment 1, which corresponds to segment AB. Figure 10As shown, the initial SOC of the vehicle at point A is F. Assuming the vehicle uses hybrid mode (i.e., the vehicle uses fuel entirely and the battery is charging) on ​​segment 1 from point A to point B, the first SOC at point B is G, which is the upper limit of the battery SOC for segment 1. Assuming the vehicle uses pure electric mode (i.e., the vehicle uses electricity entirely and the battery is discharging) on ​​segment 1 from point A to point B, the second SOC at point B is I, which is the lower limit of the battery SOC for segment 1. Therefore, the battery SOC range for segment 1 can be determined as [I, G]. Assuming the actual battery SOC F for segment 1 is 70%, the upper limit of battery SOC G at point B is 75%, and the lower limit of battery SOC I is 65%, then the target battery SOC for segment 1 is [65%, 75%].

[0247] 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].

[0248] In one implementation, 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.

[0249] 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.

[0250] In one implementation, the target SOC of a target segment in a preset travel route is determined based on the first target SOC and the second target SOC of the target segment. If 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. If 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. If 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. If 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.

[0251] In this embodiment of the application, 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.

[0252] 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.

[0253] 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. Specifically, the first target SOC at the end of the last road segment of the preset travel route can be the end point 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 point SOC of the preset travel route.

[0254] In one implementation, the aforementioned target SOC is the intersection of the first target SOC and the second target SOC. Specifically, for any segment in the preset travel path, excluding the last segment, the intersection of the first target SOC at the end of that segment and the target SOC at the beginning of the next segment is taken to obtain the target SOC at the end of each segment. The target SOC at the end of the last segment is the endpoint SOC of the preset travel path. Please refer to [link to relevant documentation]. Figure 11 , Figure 11 This is another schematic diagram of predicted SOC provided in the embodiments of this application, such as... Figure 11 The diagram shows the final target SOC, where the starting SOC of the preset travel route is F, and the ending SOC of the preset travel route is U. For example, Figure 10 The first road segment is segment 1 corresponding to segment AB, and the second road segment is segment 2 corresponding to segment BC. Assuming that the first target SOC at the end of segment 1 is [65%, 75%] and the second target SOC at the beginning of segment 2 is [60%, 70%], then after taking the intersection, the range of change at the end of segment 1 is [65%, 70%].

[0255] In one implementation, 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's energy consumption on 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's energy consumption on the target road segment is determined based on the road condition information of the target road segment.

[0256] 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. The NVH limit power is a power threshold value imposed on the engine to ensure its NVH performance meets certain standards.

[0257] In this embodiment, 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 limit 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 limit power of the vehicle's engine, and the maximum charging and discharging power of the power battery.

[0258] The vehicle's total energy consumption during its journey on the road segment is determined based on road condition information. The second target SOC at the start of the journey is determined based on road condition information, destination SOC, total vehicle energy consumption, engine NVH limit power, and maximum charge / discharge power of the power battery.

[0259] 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.

[0260] 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.

[0261] In this embodiment, the charging / discharging power range corresponding to each road segment can be obtained based on the vehicle energy consumption, NVH power limit, and maximum charging / discharging power of the road segment. Based on the initial SOC, the upper limit of the charging / 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 / 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 / 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. Likewise, based on the lower limit of the first target SOC at the end of the first road segment, the lower limit of the charging / 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.

[0262] 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.

[0263] In this embodiment, the charging and discharging power range corresponding to each road segment can be obtained based on the vehicle energy consumption, NVH power limit, and maximum charging and discharging power of the road segment. Based on the endpoint SOC, the upper limit of the charging and discharging power range of the last road segment, and the road condition information of the last road segment, the lower limit of the second target SOC at the beginning of the last road segment can be calculated. Based on the endpoint SOC, the lower limit of the charging and discharging power range of the last road segment, and the road condition information of the last road segment, the upper limit of the second target SOC at the beginning of the last road segment can be calculated. Furthermore, based on the upper limit of the second target SOC at the beginning of the last road segment, the lower limit of the charging and discharging power range of the second-to-last road segment, and the road condition information of the second-to-last road segment, the upper limit of the second target SOC at the beginning of the second-to-last road segment can be calculated; based on the lower limit of the second target SOC at the beginning of the last road segment, the upper limit of the charging and discharging power range of the second-to-last road segment, and the road condition information of the second-to-last road segment, the lower limit of the second target SOC at the beginning of the second-to-last road segment can be calculated, and so on, the second target SOC at the beginning of each road segment can be calculated.

[0264] In one implementation, 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.

[0265] 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.

[0266] Specifically, 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.

[0267] Optionally, the aforementioned road condition information, driving style information, vehicle status, and user vehicle settings can be input into the target energy consumption prediction model to obtain the model's output of the vehicle's energy consumption for the road segment, thereby improving the accuracy of the prediction results. Vehicle status includes vehicle weight, drag coefficient, rolling resistance coefficient, tire pressure, etc. Vehicle settings can include air conditioning settings.

[0268] In one implementation, 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.

[0269] Specifically, continue to refer to Figure 11 As shown, 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.

[0270] 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.

[0271] In one implementation, determining the target SOC change path that minimizes vehicle energy consumption during a preset travel route from multiple SOC change paths can employ dynamic programming algorithms, Pontryagin's minimum principle (PMP) algorithm, or similar methods. These algorithms use the target SOC of each road segment as the feasible region of the state variables. For numerical calculation, the feasible region needs to be discretized, i.e., the target SOC of each road segment is discretized. Specifically, it can be discretized at equal intervals; if the difference between the maximum and minimum SOC values ​​of a segment is greater than 0.005, it is discretized at intervals of 0.005; if the difference is less than 0.005, the SOC is discretized in three equal intervals. The control variables are the operating mode and engine operating point (torque, speed), where the operating modes include pure electric, series, and parallel. To reduce computational requirements and accelerate the calculation process, the feasible region of the engine operating point can be simplified; in series and parallel modes, the engine operating point uses the control line calculated based on optimal system efficiency. Optimizing the engine operating point requires considering NVH constraints, which are simplified to constraints on engine speed based solely on vehicle speed. Within the feasible region, solving the optimization problem yields the target SOC change path with minimal energy consumption. The SOC included in this target SOC change path can be used as the target SOC for each segment of the preset travel route.

[0272] In one implementation, if the initial SOC is greater than or equal to a first preset threshold, the final SOC is a second preset threshold, which is greater than the first preset threshold; if the initial SOC is less than the first preset threshold, the final SOC is the first preset threshold.

[0273] The second preset threshold can be a pre-calibrated SOC of 25%, and the first preset threshold can be a pre-calibrated minimum allowable SOC of 17%. It should be understood that 25% and 17% are merely examples, and the specific values ​​can be adjusted according to actual circumstances. If the starting SOC of the preset travel route is greater than or equal to 17%, the ending SOC of the preset travel route is determined to be 25%; if the starting SOC of the preset travel route is less than 17%, the ending SOC of the preset travel route is determined to be 17%.

[0274] In one implementation, 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, wherein 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.

[0275] In this embodiment, when the 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 either pure electric mode or non-pure electric mode based on the comparison result.

[0276] Optionally, 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 both 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.

[0277] In one implementation, the steps 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 include:

[0278] 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.

[0279] In this embodiment, 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:

[0280] (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. The hybrid mode includes series mode and parallel mode. In this embodiment, when the vehicle is switched to hybrid mode, parallel mode is prioritized; if the vehicle does not meet the requirements for parallel mode operation, it operates in series mode.

[0281] In one implementation, 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. Specifically, 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.

[0282] In one implementation, 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. This vehicle speed threshold can be obtained through experimental calibration.

[0283] In one implementation, if the preset travel route includes only one road segment, the steps for 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.

[0284] 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 the vehicle can be controlled to operate in pure electric mode.

[0285] In one implementation, 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:

[0286] 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.

[0287] 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.

[0288] In one implementation, 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.

[0289] The category coefficient indicates the road condition category of a road segment. For example, if a road segment is classified as a highway with a medium congestion level, then the category coefficient for that road segment is 1. In other words, a category coefficient of 1 indicates that the road segment is a highway with a medium congestion level. For a road segment A, the equivalent factor corresponding to road segment A can be determined based on the target SOC and the category coefficient of road segment A. It should be noted that this equivalent factor is the equivalent factor in ECMS; please refer to the explanation in ECMS for details, which will not be elaborated 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 period of travel on road segment A is determined based on the equivalent factor corresponding to road segment A and ECMS.

[0290] In one implementation, 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.

[0291] Specifically, the target SOC corresponding to the target road segment can be obtained, where the target road segment is any segment in the 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 is obtained by looking up a table. Specifically, the equivalent factor corresponding to the category coefficient and target SOC of the target road segment can be retrieved by looking up a table. The table stores the correspondence between category coefficients, target SOC, and equivalent factors.

[0292] In one implementation, the instantaneous output power of the power battery during operation on the target road segment is calculated according to the following formula:

[0293]

[0294] 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.

[0295] Specifically, 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.

[0296] In one implementation, 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.

[0297] 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.

[0298] 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.

[0299] In one implementation, considering that some unexpected situations may occur when the vehicle is traveling along the preset travel route, the target SOC is redefined 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 redefined when the vehicle's position deviates from the preset travel route; and the target SOC is redefined if the road conditions of the target road segment change when the vehicle is traveling on the target road segment.

[0300] Specifically, the target road segment can be any segment within the preset travel route. For example, if the vehicle is currently operating on road segment A, and the difference between the actual SOC of the vehicle's power battery and the target SOC of road segment A exceeds a set threshold, then the target SOC of 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, then the target SOC of road segment A and subsequent road segments is redefined. This method can address potential unforeseen circumstances that may occur along the preset travel route, reducing vehicle energy consumption.

[0301] In one implementation, the preset travel route includes a starting point and a destination. If the destination of the preset travel route has charging facilities, the SOC at the destination is reduced when the vehicle reaches the destination.

[0302] In one implementation, the engine's operating state is controlled based on the target SOC of each road segment, the actual vehicle requirements, and the reduced SOC at the destination, so that the engine operates in its high-efficiency range.

[0303] In one implementation, the destination of the preset travel route has charging conditions, specifically including: if there is a charging address at the destination, and there is an idle charging pile at the charging address, then it is determined that the destination has charging conditions.

[0304] In this embodiment, the determination of whether a destination has charging conditions can be based on whether the destination displayed by 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. Alternatively, the determination of charging conditions can 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.

[0305] 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 favorite point, 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 the destination is judged to have a charging station. Furthermore, if the number of fast charging times and the number of slow charging times are >= 3, then the destination is judged to have charging conditions.

[0306] Furthermore, 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 will power off and plug 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 will be reset to zero, and the destination will change to a point where there are no charging conditions.

[0307] In one implementation, the reduced endpoint SOC meets the vehicle's minimum permissible SOC.

[0308] In this embodiment, the minimum permissible SOC of the vehicle is the SOC required for the entire vehicle to operate.

[0309] In one implementation: when the vehicle reaches the end of any road segment, the target SOC of the remaining road segment is updated based on the target SOC of the power battery in that road segment and the predicted energy consumption of the vehicle in the remaining road segment, with the goal of minimizing the fuel consumption of the preset travel route.

[0310] In this embodiment, 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.

[0311] In one implementation: 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.

[0312] In this embodiment, 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 vehicle's current location 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.

[0313] In summary, the new energy vehicle energy intelligent management system control method according to the embodiments of this application 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, reducing fuel consumption for users and improving the driving experience.

[0314] In one implementation, 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.

[0315] In this embodiment, 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, then 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, then 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, then the engine is controlled to stop; this is pure electric mode driving.

[0316] In one implementation, the vehicle is controlled to travel along a preset travel route based on a target speed.

[0317] In one implementation, a prompt message is generated based on the target speed that minimizes the overall vehicle energy consumption. The prompt message is used to remind the driver to control the vehicle's movement based on the target speed that minimizes the overall vehicle energy consumption.

[0318] In one implementation, the prompt information includes at least one of the target vehicle speed and / or pedal control information.

[0319] In this embodiment, when the prompt information is the target vehicle speed, human-machine interaction can be carried out with the driver through instruments, pads, HUDs, etc. When the prompt information is pedal control information, the vehicle speed can be converted into the form of accelerator pedal and brake pedal for human-machine interaction with the driver.

[0320] In one implementation, if the navigation system's auto-start function is turned off, the navigation system is turned off, and the preset travel route is a commuter route, the method further includes controlling the engine's operating state based on the vehicle's historical driving data corresponding to the commuter route, so that the engine operates in a high-efficiency range.

[0321] In this embodiment, 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.

[0322] In one implementation, historical driving data includes a sequence of vehicle speeds as vehicles traveled commuting routes over a historical time period.

[0323] In this embodiment, historical driving data includes vehicle speed sequences when passing through 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 strategy, so that the engine operates in its efficient operating range.

[0324] For further details, please see Figure 12 , Figure 12 This is a schematic diagram of energy management based on historical driving data provided in this application embodiment. During operating condition identification, 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, future operating condition sequences are 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, 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 while ensuring a safe distance. When intelligent driving is not activated, prediction is performed using a method based on historical data statistical transition probability matrix, or prediction is performed based on historical data through a time-series prediction algorithm.

[0325] In one implementation, if the navigation system's auto-start function is disabled, the navigation system is turned 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 command in the component control sequence, the corresponding component is controlled; the component includes at least one of the accelerator and the pedal.

[0326] In one implementation, the components include 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.

[0327] In this embodiment, for example, the preset time period is the next 5 to 10 seconds. Historical data or intelligent driving sensors are used to predict the vehicle speed for the next 5 to 10 seconds. 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. The components include at least one of the accelerator and pedal. According to the first control command in the component control sequence, the corresponding component is controlled and optimized sequentially. Due to the optimization within the preset time period, the user's fuel consumption can be reduced.

[0328] In one implementation, 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. Here, the first preset time period can be the next 5 to 10 seconds.

[0329] In one implementation, the vehicle speed is predicted within a preset time period. This 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.

[0330] In this embodiment, 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, resulting in a predicted vehicle speed for the next 5 to 10 seconds. The preset time period can be the next 5 to 10 seconds.

[0331] In one implementation, historical driving data includes a vehicle speed sequence. Based on the historical driving data, the vehicle speed within a preset time period is predicted to obtain the predicted vehicle speed within the preset time period. This can be achieved by: dividing the vehicle speed sequence to obtain at least one speed interval; obtaining the speed state transition probability from the target speed interval to the next speed interval within the target speed interval, thereby constructing a system state transition probability matrix; wherein, the target speed interval refers to any speed interval within the at least one speed interval, and the system state transition probability matrix includes at least one transition probability; and based on the system state transition probability matrix and the vehicle's current speed, the vehicle speed at each moment within the preset time period is predicted to obtain the predicted vehicle speed within the preset time period.

[0332] In this embodiment, 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 vehicle speed state transition probability of the target vehicle speed interval 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.

[0333] In one implementation, the vehicle speed at each moment within a preset time period is predicted 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. This 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.

[0334] In this embodiment, the vehicle speed sequence is divided to obtain at least one vehicle speed interval. A system state transition probability matrix is ​​constructed, which refers to the probability of the vehicle speed state corresponding to the target vehicle speed interval transitioning to the next vehicle speed interval. Based on vehicle speed limits and traffic flow speed limits, 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. For example, a rolling time window method is used for short-term recording of vehicle speed, denoted as vehicle speed V. t|p ={v t-iThe formula is: 1 ≤ i ≤ p, where p can be chosen as 40 to satisfy short-term prediction accuracy. Based on historical vehicle data, vehicle speed is divided into intervals, and the probability p of the current speed interval changing to another speed interval in the next moment is calculated. mij Construct the system state transition probability matrix P m =(p mij ) n×n Considering the vehicle's own speed limitations and the aforementioned traffic flow speed limitations, the system state transition probability matrix P is modified. m Traffic flow speed is the average speed, which limits the maximum and minimum speed of vehicles during future travel. Vehicle speed limits affect the maximum acceleration or deceleration of the vehicle's response, restricting the speed change between adjacent moments. Based on the system state transition probability matrix and the current vehicle speed, the speed range with the highest probability in the next moment is predicted. For example, if the current speed is 20 km / h, the speed range with the highest probability in the next moment predicted by the transition probability matrix is ​​between 20 km / h and 30 km / h. (V) t|f =v t|t ∏P m (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. The acceleration of the vehicle in front is assumed to have a consistent trend in 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.

[0335] In one implementation, 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 the preset time period; wherein, the short-term predicted operating condition includes the predicted vehicle speed within the preset time period.

[0336] In one implementation, the vehicle is braked when the distance to or relative speed with the vehicle in front is determined to be insufficient for safe driving.

[0337] In this embodiment, when the intelligent driving sensor detects that the distance and relative speed of the vehicle in front do not meet the safe driving conditions, mechanical braking intervention control is activated to ensure the user's travel safety.

[0338] In one implementation, 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.

[0339] In one implementation, 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.

[0340] In one implementation, when the intelligent driving function is activated, the navigation-assisted driving function is deactivated, the adaptive cruise control function is activated, there is a vehicle ahead, and speed planning is activated, 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 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.

[0341] In this embodiment, 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.

[0342] In one implementation, 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.

[0343] In this embodiment, 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.

[0344] In one implementation, based on the target SOC of each road segment, actual vehicle demand, traffic light information, and an energy management strategy fused with navigation information, the engine start-stop is controlled so that the engine operates within 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 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, calculating the vehicle's drivable time; controlling the engine to operate efficiently or stop, and controlling the vehicle to travel at the current speed within the drivable time; and at the end of the drivable time, disengaging the mechanical brakes and activating a preset energy recovery level.

[0345] In one implementation, the vehicle's travel time can be calculated by: calculating the vehicle's coasting distance; determining the vehicle's travel distance based on the coasting distance and the distance between the vehicle and the traffic light; and determining the vehicle's travel time based on the travel distance and the vehicle's current speed.

[0346] In this embodiment, the energy management strategy based on navigation information fusion also includes local corrections based on traffic light information fusion. Please refer to [link to relevant documentation]. Figure 13 , Figure 13 This is a schematic diagram of a partial correction logic for traffic light information fusion provided in an embodiment of this application. Considering the partial correction of traffic light information fusion, the phase, countdown, vehicle speed, and distance of the upcoming traffic light are obtained from the navigation system. The vehicle's ability to pass through the signalized intersection is determined based on the current speed, distance, and traffic light countdown. The road conditions to the next intersection are assessed; if clear, the distance to the next intersection, current speed, and 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. If the vehicle does not meet the L... 行驶 or t 行驶 Then, it is the continued driving phase.

[0347] Furthermore, the energy management strategy based on navigation information fusion also includes an automatic navigation method for commuting. When automatic navigation is enabled and the preset travel route is the commuting route, please refer to [link to relevant documentation]. Figure 14 , Figure 14This is a schematic diagram of an automatic navigation initial time update logic provided in an embodiment of this application. It consists of optimal commuting time, commuting time period update, commuting route reminder, and commuting destination recommendation. The aim is to automatically start navigation upon power-on and promptly correct commuting time periods and routes, thereby meeting the customized commuting needs of different users and improving commuting navigation efficiency. The automatic navigation method for commuting identifies the destination based on the user's preset commuting cycle, start time, end time, residential address, and home address, further identifying it as a commuting condition. Commuting conditions are divided into start time and end time. When the vehicle starts, the onboard server first determines whether the initial position is met based on GPS, then uses a preset start and end time offset to form a commuting time period. By determining whether the current time falls within the commuting cycle and commuting time period, it identifies whether it is a commuting condition, thereby automatically starting navigation.

[0348] The optimal commute time is determined by recording commute time 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 does not make a change, 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 obtained.

[0349] The commuting time period update process involves the following steps: when the initial GPS positioning is at home or at the office, the user-set initial time is obtained and a commuting time period is formed. If the commuting time period exceeds the deviation threshold, the actual vehicle usage time is recorded, and the commuting time period is updated by the interval offset correction amount when the calibration cycle is met. If the commuting time period does not exceed the deviation threshold, the commuting time period is formed directly. If the calibration cycle is not met, the commuting time period is formed directly.

[0350] The commuting route reminder mainly involves 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.

[0351] The commuting destination recommendation feature allows users to manually navigate to a destination, navigate to an automatic destination, or manually re-enter a 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 feature is ignored and the process ends.

[0352] 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.

[0353] In this embodiment, the State of Charge (SOC) for maintaining vehicle power under different historical operating conditions is dynamically adjusted based on driving style, vehicle speed, altitude, low temperature, and other information 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 vehicle power. The SOC for maintaining vehicle power can be the target SOC.

[0354] In one implementation, the temperature of the power battery is adjusted based on the charging status, the preset travel route, and the user's scheduled pick-up time.

[0355] In this embodiment, the charging status includes the current charge amount and charging rate of the power battery. Different paths lead to different driving modes and battery usage, which in turn have different effects on the heat generated by the battery. If the user expects to get into the vehicle in a short period of time, measures need to be taken to quickly adjust the battery temperature so that it can achieve optimal performance at the time of departure.

[0356] In one implementation, 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.

[0357] In one implementation, engine coolant preheating is controlled when the target temperature of the crew compartment is greater than the current temperature of the crew compartment.

[0358] In this embodiment, before departure, the target passenger compartment temperature is corrected based on the panel passenger compartment temperature, navigation information, outside temperature, charging status, and user boarding time to generate a target passenger compartment temperature deviation value. The deviation correction value is categorized as either cooling or heating type. Furthermore, in heating mode, engine coolant is used to preheat the passenger compartment. 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. Simultaneously, preheating and precooling mitigate 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 passenger compartment comfort.

[0359] In one implementation, the temperature of the power battery is adjusted based on the charging status, the preset travel route, and the user's scheduled pick-up time.

[0360] In this embodiment, the target battery temperature is adjusted before driving based on the charging status, the user's scheduled driving time, and the mileage information. Battery thermal management is activated in advance to improve battery efficiency during driving and save vehicle energy consumption.

[0361] In one implementation, the 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 is controlled to reach the corrected target temperature.

[0362] In one implementation, the target temperature deviation value can be obtained during vehicle operation by:

[0363] During vehicle operation, temperature influencing factors are collected, including at least one of vehicle information and environmental information. Vehicle information includes at least one of window opening information, engine coolant temperature, navigation time, and target temperature deviation value. Environmental information includes at least one of weather information and outside temperature. The target temperature deviation value is determined based on the temperature deviation values ​​corresponding to the temperature influencing factors.

[0364] In one implementation, 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 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; wherein, 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.

[0365] In one implementation, the number of temperature deviation values ​​corresponding to temperature influencing factors is multiple;

[0366] 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; wherein, 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.

[0367] In one implementation, 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; wherein, the second temperature deviation value is greater than the other temperature deviation values ​​among the multiple temperature deviation values ​​except for the second temperature deviation value.

[0368] In this embodiment, 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.

[0369] In one implementation, 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.

[0370] In this embodiment, if there are road sections where the engine start-up time is long in the future, the engine will be started in advance to preheat it, thereby improving the thermal efficiency of the engine during driving.

[0371] In one implementation, the vehicle's traffic jam time is predicted, and when the time interval between traffic jams reaches a fourth preset duration, the engine coolant temperature is increased.

[0372] In one implementation, the engine coolant temperature can be increased by either reducing the engine's water pump speed or reducing the engine's fan speed.

[0373] In this embodiment, just before traffic jams, the target engine coolant temperature is increased, and the engine water pump and fan speeds are reduced to decrease energy consumption.

[0374] Furthermore, the target battery temperature can be adjusted to reduce battery thermal management energy consumption.

[0375] In one implementation, the destination of a preset travel route is predicted. When the distance to the destination is less than the preset distance, the adjustment of the engine coolant temperature based on the target coolant temperature deviation is paused, and the engine coolant temperature is increased until the engine coolant temperature is higher than the preset temperature threshold before the vehicle reaches the destination.

[0376] In this embodiment, when the distance to the destination is less than the preset distance, the destination is about to be reached. Raising the target engine coolant temperature in advance can reduce the speed of the engine water pump and fan, thereby reducing energy consumption.

[0377] In one implementation, the destination of a preset travel route is predicted. When the distance to the destination is less than the 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.

[0378] In this embodiment, 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, thus saving energy consumption caused by maintaining the battery temperature.

[0379] In one implementation, the destination of a preset travel route is predicted. When the distance to the destination is less than the preset distance, the control of the passenger compartment temperature is paused to reach the target passenger compartment temperature, and the target passenger compartment temperature is corrected.

[0380] In this embodiment, 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.

[0381] In this embodiment, 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, reducing energy loss due to high current, thereby saving power consumption of high and low temperature air conditioning and accessories, and reducing fuel consumption.

[0382] Based on the above description, please refer to Figure 15 , Figure 15 This is a schematic diagram of another intelligent energy management method for new energy vehicles provided in this application embodiment, such as... Figure 15 The energy intelligent management method for new energy vehicles shown includes, but is not limited to, steps S1501-S1534, wherein:

[0383] S1501, the user powers on the vehicle.

[0384] S1502, Determine whether automatic navigation is enabled.

[0385] In this embodiment, 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.

[0386] S1503, determine whether the user wants navigation.

[0387] In this embodiment, 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.

[0388] S1504, determine whether it is a commuting condition.

[0389] In this embodiment, 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.

[0390] S1505, determine whether intelligent driving sensors are present.

[0391] In this embodiment, if the vehicle has intelligent driving sensors, step S1507 is executed; if the vehicle does not have intelligent driving sensors, step S1506 is executed. The intelligent driving sensors can be sensors such as lidar, millimeter-wave radar, or cameras.

[0392] 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.

[0393] In this embodiment, 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 range.

[0394] In one implementation, if the navigation system's auto-start function is disabled, the navigation system is turned off, and the preset travel route is not a commuter route, the vehicle speed is predicted within a preset time period while the vehicle is traveling on the preset travel route, thus obtaining 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; and based on the first control command in the component control sequence, the corresponding component is controlled; the component includes at least one of the accelerator and the pedal.

[0395] In one implementation, the components include 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.

[0396] In this embodiment, for example, the preset time period is the next 5 to 10 seconds. Historical data or intelligent driving sensors are used to predict the vehicle speed for the next 5 to 10 seconds. 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. The components include at least one of the accelerator and pedal. According to the first control command in the component control sequence, the corresponding component is controlled and optimized sequentially. Due to the optimization within the preset time period, the user's fuel consumption can be reduced.

[0397] In one implementation, 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. Here, the first preset time period can be the next 5 to 10 seconds.

[0398] In one implementation, the vehicle speed is predicted within a preset time period. This 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.

[0399] In this embodiment, 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, resulting in a predicted vehicle speed for the next 5 to 10 seconds. The preset time period can be the next 5 to 10 seconds.

[0400] In one implementation, historical driving data includes a vehicle speed sequence. Based on the historical driving data, the vehicle speed within a preset time period is predicted to obtain the predicted vehicle speed within the preset time period. This can be achieved by: dividing the vehicle speed sequence to obtain at least one speed interval; obtaining the speed state transition probability from the target speed interval to the next speed interval within the target speed interval, thereby constructing a system state transition probability matrix; wherein, the target speed interval refers to any speed interval within the at least one speed interval, and the system state transition probability matrix includes at least one transition probability; and based on the system state transition probability matrix and the vehicle's current speed, the vehicle speed at each moment within the preset time period is predicted to obtain the predicted vehicle speed within the preset time period.

[0401] In this embodiment, 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 vehicle speed state transition probability of the target vehicle speed interval 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.

[0402] In one implementation, the vehicle speed at each moment within a preset time period is predicted 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. This 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.

[0403] In this embodiment, the vehicle speed sequence is divided to obtain at least one vehicle speed interval. A system state transition probability matrix is ​​constructed, which refers to the probability of the vehicle speed state corresponding to the target vehicle speed interval transitioning to the next vehicle speed interval. Based on vehicle speed limits and traffic flow speed limits, 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. For example, a rolling time window method is used for short-term recording of vehicle speed, denoted as vehicle speed V. t|p ={v t-iThe formula is: 1 ≤ i ≤ p, where p can be chosen as 40 to satisfy short-term prediction accuracy. Based on historical vehicle data, vehicle speed is divided into intervals, and the probability p of the current speed interval changing to another speed interval in the next moment is calculated. mij Construct the system state transition probability matrix P m =(p mij ) n×n Considering the vehicle's own speed limitations and the aforementioned traffic flow speed limitations, the system state transition probability matrix P is modified. m Traffic flow speed is the average speed, which limits the maximum and minimum speed of vehicles during future travel. Vehicle speed limits affect the maximum acceleration or deceleration of the vehicle's response, restricting the speed change between adjacent moments. Based on the system state transition probability matrix and the current vehicle speed, the speed range with the highest probability in the next moment is predicted. For example, if the current speed is 20 km / h, the speed range with the highest probability in the next moment predicted by the transition probability matrix is ​​between 20 km / h and 30 km / h. (V) t|f =v t|t ∏P m (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. The acceleration of the vehicle in front is assumed to have a consistent trend in 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.

[0404] In one implementation, 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 the preset time period; wherein, the short-term predicted operating condition includes the predicted vehicle speed within the preset time period.

[0405] 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 needs, and a speed prediction control strategy based on perception planning, so that the engine operates in a high-efficiency range.

[0406] In this embodiment, 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 requirements, and a speed prediction control strategy based on perception planning, so that the engine operates in a high-efficiency range.

[0407] In one implementation, the vehicle speed is predicted over a preset time period to obtain the predicted vehicle speed over that time period. This can be achieved by:

[0408] 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.

[0409] In this embodiment, for example, within a preset time period of 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, and to calculate the predicted vehicle speed for the next 5 to 10 seconds.

[0410] Furthermore, when the driver releases the accelerator and has no driving demand, before the back electromotive force of the vehicle motor is lower than the withstand voltage value of the high-voltage device, the motor control device is shut down first, and the motor coasts with zero feedback, converting the vehicle's kinetic energy into driving distance.

[0411] In one implementation, the vehicle is braked when the distance to or relative speed with the vehicle in front is determined to be insufficient for safe driving.

[0412] In this embodiment, when the intelligent driving sensor detects that the distance and relative speed of the vehicle in front do not meet the safe driving conditions, mechanical braking intervention control is activated to ensure the user's travel safety.

[0413] 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.

[0414] In one implementation, if the navigation system's auto-start function is turned off, the navigation system is shut down, and the preset travel route is the 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.

[0415] In this embodiment, 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.

[0416] 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.

[0417] In one implementation, historical driving data includes a sequence of vehicle speeds as vehicles traveled commuting routes over a historical time period.

[0418] In this embodiment, 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 strategy, so that the engine operates in its efficient operating range.

[0419] S1509, the user manually selects the destination.

[0420] In this embodiment, when the user manually starts navigation, the user manually selects the destination.

[0421] In one implementation, the preset travel route is determined by responding to the user's input destination and determining the preset travel route if the navigation system's auto-start function is disabled.

[0422] In one implementation, responding to the user's input destination and determining the preset travel route can be done by:

[0423] Based on the vehicle's origin and destination, at least one candidate energy-saving path is determined; among them, 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, and the total vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path.

[0424] 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 multiple road segments.

[0425] In this embodiment, at least one candidate energy-saving path is calculated based on the vehicle's origin and destination. These candidate paths are the paths with the lowest energy consumption throughout the entire trip. The user can select one of the candidate energy-saving paths as the preset travel route. The preset travel route refers to the selected candidate energy-saving path, 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.

[0426] S1510, determine whether the remaining driving range is greater than the total driving range.

[0427] In this embodiment, if the remaining mileage is greater than the total mileage of the preset travel route, the following step S1511 is executed; if the remaining mileage is less than or equal to the total mileage, the following step S1514 is executed.

[0428] S1511 is determined to be a short driving mileage.

[0429] In this embodiment, when the remaining mileage is greater than the total mileage of the preset travel route, it is determined to be a short mileage.

[0430] S1512, User Interface (UI): Recommended energy-saving path.

[0431] In this embodiment, the UI can be an instrument panel, a pad, a head-up display (HUD), etc. The UI recommends energy-saving paths to the user. 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.

[0432] S1513, User confirms driving route.

[0433] In this embodiment, the user confirms the driving route from the energy-saving routes recommended by the UI, i.e., the preset travel route. The steps for determining the preset travel route are detailed in the above-described method for intelligent energy management of new energy vehicles, and will not be repeated here.

[0434] S1514 is identified as having a long mileage.

[0435] In this embodiment, if 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.

[0436] S1515, UI: Recommended energy-saving path.

[0437] In this embodiment, energy-saving paths are recommended to users through the UI.

[0438] S1516, User confirms driving route.

[0439] In this embodiment, the user confirms the driving route from the energy-saving routes recommended by the UI, i.e., the preset travel route. The steps for determining the preset travel route are detailed in the above-described method for intelligent energy management of new energy vehicles, and will not be repeated here.

[0440] S1517, UI: Recommend energy-saving and energy replenishment planning.

[0441] In this embodiment, 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. The steps for determining the energy-saving and refueling plan are detailed in the steps for determining the preset travel route in the aforementioned intelligent energy management method for new energy vehicles; these steps will not be repeated here.

[0442] S1518 determines whether the intelligent driving sensors are available.

[0443] In this embodiment, if the intelligent driving sensor is available, step S1522 is executed; if the intelligent driving sensor is unavailable, step S1519 is executed.

[0444] S1519, determine whether to activate the energy-saving driving guidance system.

[0445] In this embodiment, 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. Energy-saving driving guidance refers to controlling and guiding the vehicle to travel at the target speed that minimizes overall vehicle energy consumption along the route. When the user is manually driving, energy-saving driving guidance is mainly presented as 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 overall vehicle energy consumption along the route.

[0446] 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.

[0447] In this embodiment, 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.

[0448] In one implementation, 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 information is located. If the vehicle does not have the capability to pass through the signalized intersection where 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 its current speed within the drivable time. At the end of the drivable time, the mechanical brakes are deactivated, and a preset energy recovery level is activated.

[0449] In one implementation, the vehicle's travel time can be calculated by: calculating the vehicle's coasting distance; determining the vehicle's travel distance based on the coasting distance and the distance between the vehicle and the traffic light; and determining the vehicle's travel time based on the travel distance and the vehicle's current speed.

[0450] In this embodiment, the energy management strategy based on navigation information fusion also includes local corrections for traffic light information fusion. For specific steps of this solution, please refer to the detailed steps of the above-mentioned intelligent energy management method for new energy vehicles, which includes local corrections for traffic light information fusion based on navigation information fusion; these steps will not be repeated here.

[0451] Furthermore, the energy management strategy based on navigation information fusion also 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 specific steps of this solution, please refer to the above-mentioned intelligent energy management method for new energy vehicles, which also includes an automatic navigation method for commuting; these steps will not be repeated here.

[0452] 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.

[0453] In this embodiment, the steps 1, 2, and 3 for predicting the vehicle's energy consumption along a path using navigation information fusion are described in the aforementioned intelligent energy management method for new energy vehicles; these steps will not be repeated here. Energy-saving driving guidance refers to controlling and guiding the vehicle to travel at the target speed that minimizes the vehicle's energy consumption along the path. For example, the target speed is displayed through an instrument panel or similar means to guide the user in controlling the vehicle to travel at the target speed that minimizes the vehicle's energy consumption along the path. The determination of the target speed is detailed in the aforementioned intelligent energy management method for new energy vehicles; these steps will not be repeated here.

[0454] S1522, determine whether Navigate on Autopilot (NOA) is enabled.

[0455] In this embodiment, if NOA is enabled, step S1523 is executed; if NOA is not enabled, step S1524 is executed.

[0456] 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.

[0457] In this embodiment, when NOA is activated, the engine's operating state is controlled based on the target SOC of the power battery for each road segment, the actual vehicle demand, and the energy-saving speed control strategy, aiming to minimize fuel consumption along the preset travel route. This ensures the engine operates within its high-efficiency range. The determination of the energy-saving speed, i.e., the target speed, is detailed in the specific steps of the target speed determination method in the aforementioned intelligent energy management method for new energy vehicles, and will not be repeated here. Once the target speed is determined, the vehicle is controlled to travel at that speed.

[0458] S1524, determine whether to activate Adaptive Cruise Control (ACC).

[0459] In this embodiment, if ACC is enabled, step S1525 is executed; if ACC is not enabled, step S1532 is executed.

[0460] S1525, determine if there are vehicles ahead.

[0461] In this embodiment, if there is a vehicle ahead, step S1529 is executed; if there is no vehicle ahead, step S1526 is executed.

[0462] S1526, Determine if vehicle speed planning is activated.

[0463] In this embodiment, if vehicle speed planning is activated, the following step S1527 is executed; if vehicle speed planning is not activated, the following step S1528 is executed.

[0464] 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.

[0465] In this embodiment, the energy-saving cruise control speed control strategy controls the vehicle to travel based on a target speed. For the determination of the target speed, please refer to the specific steps of the target speed determination method in the above-mentioned new energy vehicle energy intelligent management method, which will not be repeated in this solution.

[0466] 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.

[0467] In one implementation, 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.

[0468] S1529, determine whether vehicle speed planning is activated.

[0469] In this embodiment, if vehicle speed planning is activated, the following step S1530 is executed; if vehicle speed planning is not activated, the following step S1531 is executed.

[0470] 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.

[0471] In one implementation, 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 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.

[0472] Get the current speed of the vehicle in front of you;

[0473] 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.

[0474] In this embodiment, 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 the determination of the energy-saving speed, i.e. the target speed, please refer to the specific steps of the determination method of the target speed in the above-mentioned new energy vehicle energy intelligent management method. This solution will not repeat them.

[0475] 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.

[0476] In one implementation, 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.

[0477] 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.

[0478] In this embodiment, 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.

[0479] In one implementation, 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.

[0480] In this embodiment, for example, a preset time period is 5 to 10 seconds from now. 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.

[0481] Furthermore, when none of the above strategies are satisfied, the method also includes: adjusting the power-saving SOC based on driver style, vehicle speed, or 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.

[0482] In this embodiment, the State of Charge (SOC) for maintaining vehicle power under different historical operating conditions is dynamically adjusted based on driving style, vehicle speed, altitude, low temperature, and other information 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 vehicle power. The SOC for maintaining vehicle power can be the target SOC.

[0483] S1533, determine whether the conditions for automatic navigation are met.

[0484] In this embodiment, when automatic navigation is enabled, it is determined whether the automatic navigation conditions are met. 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.

[0485] S1534, automatic navigation upon power-on.

[0486] In this embodiment, 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.

[0487] In this embodiment, 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.

[0488] For some implementations of this application, please refer to Figure 1 The drive unit (not shown) 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. The engine 10 selectively outputs power to the wheels. The drive motor 20 outputs power to the wheels. The generator 30 is connected to the engine 10 to generate electricity. 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. 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; wherein the predicted total vehicle energy consumption of at least one candidate energy-saving path is lower than that 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, a preset travel path is determined; wherein 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, the operating state of the engine 10 is controlled 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, so that the engine operates in a high-efficiency operating range.

[0489] 1. Based on the vehicle's starting point and destination, determine at least one candidate energy-saving path; wherein, 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, and the total vehicle energy consumption is predicted based on the road condition information and energy consumption impact information of each path.

[0490] The candidate energy-saving path can be determined as follows:

[0491] In one implementation, 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; based on the total energy consumption of the vehicle on each candidate driving path, at least one candidate energy-saving path is determined from the at least one candidate driving path; wherein, 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.

[0492] In one implementation, 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.

[0493] In one implementation, the first travel dimension indicator includes the travel distance, and the second travel dimension indicator includes the travel time.

[0494] In one implementation, based on the second travel dimension index of m drivable paths, at least one candidate drivable path is determined from the m drivable paths. This 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 path as at least one candidate drivable path.

[0495] In one implementation, at least one candidate driving route is determined based on the vehicle's origin and destination. This 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. The comprehensive travel indicator of the at least one candidate driving route is less than the comprehensive travel indicators of the other drivable routes within the at least one drivable route.

[0496] 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.

[0497] In one implementation, the energy consumption impact information includes vehicle status information, which at least includes the vehicle's static parameters, and road condition information, which at least includes road traffic flow speed. The total 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, predicting the total vehicle energy consumption of a preset travel route based on road traffic flow speed and vehicle static parameters, where the total vehicle energy consumption along the route is the theoretical required energy consumption.

[0498] In one implementation, the vehicle's static parameters include at least: wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

[0499] In one implementation, the theoretical energy consumption requirement 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.

[0500] 2. Input the road type, driving style and vehicle model 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 the road type of the preset travel route and / or the user's driving style information.

[0501] In one implementation, the energy consumption impact information includes vehicle status information, which at least includes the user's driving style and vehicle model information, and road condition information, which at least includes the road type. The total vehicle energy consumption for a given 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 then outputting the predicted total vehicle energy consumption for the preset travel route from the target energy consumption prediction model. The total vehicle energy consumption for the route serves as a reference demand energy consumption. The target energy consumption prediction model is determined from multiple preset energy consumption prediction models based on the road type and / or the user's driving style information for the preset travel route.

[0502] In one implementation, road types include: ordinary roads, expressways, highways, and congested roads.

[0503] In one implementation, the user's driving style is categorized as aggressive, normal, and mild based on the rate of change of accelerator pedal opening and the rate of change of acceleration.

[0504] 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.

[0505] 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.

[0506] In one implementation, energy consumption impact information includes vehicle status information, which at least includes vehicle static parameters and vehicle model information; user behavior information, which at least includes the user's driving style; and road condition information, which at least includes 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 road traffic flow speed and vehicle static parameters, the total vehicle energy consumption of the preset travel route is predicted, and 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: 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, and 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 the road type of the preset travel route and / or the user's driving style information.

[0507] In one implementation, the total energy consumption of the vehicle along a preset travel route is predicted based on the vehicle's theoretical energy consumption and reference energy consumption along the preset travel route. This can be achieved 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 and second weights to predict the vehicle's total energy consumption along the route.

[0508] In one implementation, the first weight of the theoretical energy demand and the second weight of the reference energy demand are added together to 1. With the constraint that the actual vehicle energy consumption on the road segment is within a preset range, the first weight of the theoretical energy demand and the second weight of the reference energy demand are updated 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.

[0509] In one implementation, if the error between the predicted total energy consumption of a vehicle on road segment n and the actual total energy consumption of a vehicle on road segment n is greater than a certain threshold, then the actual total energy consumption of the vehicle on road segment n and the model identifier of the target energy consumption prediction model are sent to the server, so that the server can optimize the energy consumption prediction model corresponding to the model identifier based on the actual total energy consumption of the vehicle on road segment n.

[0510] In one implementation, the division of road segments 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.

[0511] In one implementation, 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.

[0512] In one implementation, the vehicle's total energy consumption on the nth road segment is predicted using the following method:

[0513] 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.

[0514] In one implementation, after the vehicle passes through the nth road segment, the actual vehicle energy consumption of the vehicle in the nth road segment is obtained; if the actual vehicle energy consumption of the road segment is within the threshold range, the first weight and the second weight remain unchanged, and the threshold range is determined based on the predicted vehicle energy consumption of the vehicle in the nth road segment.

[0515] In one implementation, the theoretical energy consumption and reference energy consumption of the vehicle on the nth road segment are obtained; based on the first initial weight of the theoretical energy consumption and the first initial weight of the reference energy consumption, the theoretical energy consumption and reference energy consumption on the nth road segment are weighted to obtain the first reference road segment vehicle energy consumption; after the vehicle passes through the nth road segment, the actual road segment vehicle energy consumption on the nth 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.

[0516] In one implementation, the theoretical energy consumption and reference energy consumption of the vehicle on the nth road segment are obtained; based on the second initial weight of the theoretical energy consumption and the second initial weight of the reference energy consumption, the theoretical energy consumption and reference energy consumption on the nth road segment are weighted to obtain the second reference road segment vehicle energy consumption; after the vehicle passes through the nth road segment, the actual road segment vehicle energy consumption on 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.

[0517] In one implementation, obtaining the first weight of the theoretical energy consumption demanded by the vehicle on the nth road segment and the second weight of the reference energy consumption demanded can be achieved by: obtaining the theoretical energy consumption demanded and the reference energy consumption demanded by the vehicle on the nth road segment in a preset travel route; performing a weighted calculation on the theoretical energy consumption demanded and the reference energy consumption demanded based on the first initial weight of the theoretical energy consumption demanded and the first initial weight of the reference energy consumption demanded to obtain the first reference road segment total vehicle energy consumption demanded for the nth road segment; and performing a weighted calculation on the theoretical energy consumption demanded and the reference energy consumption demanded based on the second initial weight of the theoretical energy consumption demanded and the second initial weight of the reference energy consumption demanded to obtain the first reference road segment total vehicle energy consumption demanded for the nth road segment. The initial weights are used to perform a weighted calculation on the theoretical energy consumption and reference energy consumption of the nth road segment to obtain the second reference road segment's total vehicle energy consumption for the nth road segment. After the vehicle has traveled the nth road segment, the actual road segment's total vehicle energy consumption is obtained. If the actual road segment's total vehicle energy consumption is greater than the second reference road segment's total vehicle energy consumption but less than the first reference road segment's total vehicle energy consumption, then the first and second weights are updated, and the updated first weight is used as the current first weight for the theoretical energy consumption, and the updated second weight is used as the current second weight for the reference energy consumption.

[0518] 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.

[0519] In one implementation, the 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. The 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, 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.

[0520] In one implementation, based on the energy consumption prediction algorithm of automotive theory, the total energy consumption of a preset travel route is predicted according to driving style, road traffic flow speed, vehicle static parameters, and the target speed that minimizes the total energy consumption of the route.

[0521] In one implementation, the 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; the energy consumption impact information includes vehicle status information, or the energy consumption impact information includes at least one of the following: 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.

[0522] In one implementation, 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. Alternatively, when the intelligent driving function is activated and the 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. Alternatively, 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, 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.

[0523] In one implementation, 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 the 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.

[0524] In one implementation, the energy-saving driving guidance function refers to a function used to control and guide the vehicle to travel at the target speed that minimizes the overall vehicle energy consumption along the route.

[0525] 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.

[0526] In one implementation, the speed sequence is modified based on constraints to obtain a modified speed sequence, the constraints including at least driving style.

[0527] In one implementation, the limiting conditions may 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.

[0528] In one implementation, 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, and sand road types, as well as 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.

[0529] In one implementation, 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 used as the initial speed solution and input into the vehicle model. The vehicle model generates a speed sequence based on the initial speed solution with the objective function of minimizing the total vehicle energy consumption along the route.

[0530] In one implementation, a smooth speed sequence is determined based on the road traffic flow speed, current vehicle speed, and constraint information of a preset travel route. This smooth speed sequence is then 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 based on 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 then inputting the smooth speed sequence as the initial speed solution into the vehicle model.

[0531] In one implementation, the speed of road segments in different driving scenarios is corrected 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 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.

[0532] In one implementation, the speed of road segments in different driving scenarios is corrected 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.

[0533] 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.

[0534] For the specific steps of this solution, please refer to the specific steps of S401 above. This solution will not repeat them here.

[0535] 2. Response to the selection operation of at least one candidate energy-saving route, determine the preset travel route; 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 multiple road segments.

[0536] In one optional implementation, if the navigation system's auto-start function is enabled and the current system time is within a preset vehicle usage time period, the navigation system is automatically turned on, and the preset travel route is determined based on the vehicle's current location information.

[0537] In one implementation, if the navigation system's auto-start function is disabled, it responds to the user's input destination and determines the preset travel route.

[0538] When determining the preset travel route, it is also necessary to consider the vehicle's remaining driving range and the driving range to the destination. If the vehicle's remaining driving range is less than the driving range to the destination, a refueling strategy is determined during the journey along the preset travel route. That is, when the driving range to the destination is greater than the vehicle's remaining driving range L based on predicted energy consumption, a refueling strategy is determined during the journey along the preset travel route.

[0539] In one implementation, determining the energy replenishment 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 when in a fatigued 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.

[0540] In one implementation, 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; 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.

[0541] In one implementation, based on the fatigue driving mileage and the vehicle's remaining mileage, the system recommends 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.

[0542] In one implementation, 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 less than a second preset distance threshold, then controlling the vehicle to drive to a third charging address for charging; wherein, 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, and 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 mileage.

[0543] In one implementation, based on fatigue driving mileage and the vehicle's remaining mileage, the system recommends 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; wherein, 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, and 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.

[0544] For the specific steps of this solution, please refer to the specific steps of S402 above. This solution will not repeat them here.

[0545] 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.

[0546] In one implementation, 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 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; planning the target SOC for each road segment based on the initial SOC of the power battery 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, thereby ensuring the engine operates within its efficient operating range.

[0547] In one implementation, the target SOC for each road segment is planned based on the initial SOC of the power battery and the vehicle energy consumption of the 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 the road segment; determining multiple SOC change paths based on the predicted SOC change, wherein each SOC change path includes a set of SOCs; identifying the SOC change path that minimizes fuel consumption during the planned travel route from among the multiple SOC change paths as the target SOC change path; and defining the SOCs included in the target SOC change path as the target SOC for each road segment.

[0548] In one implementation, the target SOC at the end of the first segment of the preset travel route is determined based on the vehicle's initial SOC on the preset travel route and the predicted SOC change 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 predicted SOC change of the non-first segment and the target SOC at the end of the segment preceding the non-first segment.

[0549] In one implementation, 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 the non-first segments of the preset travel path is determined based on the first predicted SOC change of the non-first segments and the upper limit of the target SOC of the segment preceding the non-first segment; the lower limit of the target SOC for the non-first segments is determined based on the second predicted SOC change of the non-first segments and the lower limit of the target SOC of the segment preceding the non-first segment.

[0550] In one implementation, 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 for 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 along 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.

[0551] In one implementation, 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.

[0552] In one implementation, 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.

[0553] In one implementation, the endpoint SOC is determined based on the initial SOC of the vehicle's power battery at the starting point of the preset travel route.

[0554] In one implementation, if the starting SOC is greater than or equal to a first preset threshold, the ending SOC is a second preset threshold; if the starting SOC is less than the first preset threshold, the ending SOC is the first preset threshold; wherein, the second preset threshold is greater than the first preset threshold.

[0555] In one implementation, the vehicle's preset travel route is divided into at least one road segment.

[0556] Determine the target state of charge (SOC) of the vehicle when it is traveling on each road segment.

[0557] The vehicle's engine and motor are controlled based on the actual and target SOC of the vehicle's power battery.

[0558] In one implementation, the SOC of the target road segment in the preset travel route is determined based on the first predicted SOC range and the second predicted SOC range of the target road segment;

[0559] When the target road segment is the first road segment of the preset travel route, the first predicted SOC range of the target road segment is determined based on the change in the vehicle's initial SOC on the preset travel route and the predicted SOC of the target road segment.

[0560] If the target road segment is not the first road segment of the preset travel route, the first predicted SOC range of the target road segment is determined based on the upper and lower limits of the target SOC of the previous road segment and the change in the predicted SOC of the target road segment.

[0561] 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 t...

Claims

1. A method for intelligent energy management of new energy vehicles, characterized in that, include: Based on the vehicle's origin and destination, at least one candidate energy-saving path is determined; wherein, 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 of any candidate energy-saving path is predicted based on the vehicle's theoretical energy demand and reference energy demand on that candidate energy-saving path; the theoretical energy demand is predicted based on road traffic flow speed and the vehicle's static parameters; the reference energy demand is predicted based on road type, user's driving style, and vehicle model information; 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; 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 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 operating range.

2. The method as described in claim 1, characterized in that, The process of determining 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; 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. Based on the road condition information and energy consumption impact information of each candidate driving path, predict the total vehicle energy consumption of the vehicle on each candidate driving path. 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 candidate energy-saving path is less than the vehicle's total energy consumption on other candidate driving paths in the at least one candidate driving path other than the at least one candidate energy-saving path.

3. The method as described in claim 2, characterized in that, Determining at least one candidate driving path based on the vehicle's starting point and ending point includes: Obtain at least one drivable path from the starting point to the destination of the vehicle; Based on the first travel dimension index of each drivable route, m drivable routes are determined from the at least one drivable route; where m is a positive integer, and the first travel dimension index of any drivable route among the m drivable routes is less than the first travel dimension index of other drivable routes in the at least one drivable route besides the m drivable routes. 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 as described in claim 3, characterized in that, The first travel dimension indicator includes travel distance, and the second travel dimension indicator includes travel time.

5. The method as described in claim 3, characterized in that, 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 smallest 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. The selected drivable routes are used as the at least one candidate drivable route.

6. The method as described in claim 2, characterized in that, Determining at least one candidate driving path based on the vehicle's starting point and ending point includes: Obtain at least one drivable path from the starting point to the destination of the vehicle; Obtain travel dimension indicators for each drivable route, and the weight of each travel dimension indicator corresponds to the current travel scenario of the vehicle; The travel dimension indicators are weighted according to each of the weights to obtain the comprehensive travel index for each drivable route; 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 besides the at least one candidate drivable route.

7. The method as described in claim 2, characterized in that, The step of predicting the vehicle's total energy consumption on 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 as described in claim 1, characterized in that, The response to the selection operation of at least one candidate energy-saving path, determining the preset travel path, 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 as described in claim 8, characterized in that, 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 when in a fatigued driving state; 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 the target refueling address for refueling.

10. The method as described in claim 9, characterized in that, The method of recommending that the vehicle drive to a target charging address or a 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 as described in claim 9, characterized in that, The method of recommending that the vehicle drive to a target charging address or a 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 previous charging address of the first charging address in the preset travel route.

12. The method as described in claim 9, characterized in that, The method of recommending that the vehicle drive to a target charging address or a 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 remaining charging addresses other than the third charging address among the charging addresses located before the end of the remaining driving range.

13. The method as described in claim 9, characterized in that, The method of recommending that the vehicle drive to a target charging address or a 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 as described in claim 1, characterized in that, The theoretical energy consumption requirement is predicted based on road traffic flow speed and the static parameters of the vehicle, including: According to the energy consumption prediction algorithm of automotive theory, the theoretical energy consumption requirement is predicted based on the road traffic flow speed and the static parameters of the vehicle.

15. The method as described in claim 14, characterized in that, The static parameters of the vehicle include at least: wind resistance, rolling resistance, acceleration resistance, and gradient resistance.

16. The method as described in claim 1, characterized in that, The reference energy consumption demand is predicted based on road type, user driving style, and vehicle model information, including: The road type, driving style, and vehicle model information are input into the target energy consumption prediction model, and the predicted reference energy consumption is output by 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 type of any candidate energy-saving path and / or the user's driving style information.

17. The method as described in claim 16, characterized in that, The road types include at least: ordinary roads, expressways, and highways; 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 as described in claim 1, characterized in that, The method further includes: Obtain the first weight of the theoretical energy consumption requirement and the second weight of the reference energy consumption requirement of the vehicle; 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.

19. The method as described in claim 18, characterized in that, The method further includes: 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.

20. The method as described in claim 16, characterized in that, Also includes: 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.

21. The method as described in claim 1, characterized in that, 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.

22. The method as described in claim 1, characterized in that, The theoretical energy consumption requirement is predicted based on road traffic flow speed and the static parameters of the vehicle, including: According to the energy consumption prediction algorithm of automotive theory, the theoretical energy consumption requirement is predicted based on the road traffic flow speed, the static parameters of the vehicle, and the target vehicle speed that minimizes the overall vehicle energy consumption along the route.

23. The method as described in claim 22, characterized in that, The vehicle is controlled to travel on the preset travel route at the target speed that minimizes the overall vehicle energy consumption.

24. The method as described in claim 22, characterized in that, 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.

25. The method as described in claim 22, characterized in that, 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.

26. The method as described in claim 25, characterized in that, It also includes, The speed sequence is modified based on constraints, including at least driving style, to obtain a modified speed sequence.

27. The method as described in claim 26, characterized in that, 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.

28. The method as described in claim 22, characterized in that, The target vehicle speed is determined in the following way: A smooth speed sequence is determined based on the road traffic flow speed, current vehicle speed, and constraint information of the preset travel route. 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 used as the initial velocity solution and input into the vehicle model. 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.

29. The method as described in claim 28, characterized in that, 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 ​​corrections are applied to road segments in different driving scenarios based on driving style, road traffic flow speed, and traffic light location information to locally correct the smooth speed sequence. 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.

30. The method as described in claim 29, characterized in that, 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.

31. The method as described in claim 29, characterized in that, 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 goal 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.

32. The method as described in claim 22, characterized in that, 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. or, When the intelligent driving function is activated and the navigation-assisted driving function is activated, the energy consumption prediction algorithm based on automobile theory is triggered to predict the total 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 energy consumption of the vehicle along the route. or, 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.

33. The method as described in claim 22, characterized in that, 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.

34. The method according to claim 1, characterized in that, The actual vehicle requirements for each road segment include: the total vehicle power required for the vehicle to travel on each road segment.

35. The method as described in claim 1, characterized in that, If the navigation system's auto-start function is turned off, the navigation system is turned off, and the preset travel route is a commuting route, then 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. If the navigation system's auto-start function is turned off, the navigation system is turned off, and the preset travel route is not a commuter route, the vehicle's speed within a preset time period is predicted while the vehicle is traveling on the preset travel route, and the predicted speed of the vehicle within the preset time period is obtained. Based on the predicted vehicle speed within a preset time period, predict the component control sequence of the vehicle within the preset time period; 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.

36. The method as described in claim 1, characterized in that, 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: controlling the vehicle to travel based on the current 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 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.

37. The method as described in claim 25, characterized in that, The method further includes: 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; 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 at the control speed; or 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 vehicle speed.

38. The method as described in claim 1, characterized in that, The method further includes: The temperature of the power battery is adjusted based on the charging status, the preset travel route, and the user's scheduled pick-up time; or... Predict the duration of the engine's power output; if the output duration exceeds a third preset duration, start the engine; or... The system predicts the traffic jam time of the vehicle, and when the time interval between the traffic jam and the traffic jam is greater than a fourth preset duration, it increases the engine coolant temperature.

39. The method as described in claim 1, characterized in that, The method further includes: The system predicts the destination of the preset travel route. When the distance to the destination is less than the preset distance, it pauses the adjustment of the engine water temperature based on the target water temperature deviation of the engine and increases the engine water temperature until the engine water temperature is higher than the preset temperature threshold before the vehicle reaches the destination.

40. The method as described in claim 1, characterized in that, The method further includes: The system predicts the destination of the preset travel route. When the distance to the destination is less than the preset distance, it pauses the adjustment of the power battery temperature based on the target temperature deviation of the power battery and continues to adjust the power battery temperature until the temperature of the power battery is within the preset temperature range when the vehicle reaches the destination.

41. The method as described in claim 1, characterized in that, The method further includes: The system predicts the destination of the preset travel route. When the distance to the destination is less than the preset distance, it pauses the control of the vehicle's passenger compartment temperature to reach the target passenger compartment temperature and corrects the target passenger compartment temperature.

42. The method as described in claim 1, characterized in that, 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 demand, the engine's operating state is controlled to ensure it operates within its high-efficiency range. This includes: With the goal of minimizing fuel consumption along the preset travel route, 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. 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 its high-efficiency range.

43. The method as described in claim 42, characterized in that, 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 road 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, where each SOC change path includes a set of SOCs. Among multiple SOC change paths, the SOC change path that minimizes fuel consumption for the vehicle during the planned travel route is identified 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.

44. The method according to claim 43, characterized in that, 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.

45. The method according to claim 44, characterized in that, 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.

46. ​​The method as described in claim 42, characterized in that, Also includes: 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.

47. The method as described in claim 46, characterized in that, Also includes: Based on the target SOC of each road segment, the actual vehicle requirements, and the reduced SOC at the destination, the engine's operating state is controlled so that the engine operates in its high-efficiency range.

48. The method as described in claim 46, characterized in that, The fact that the destination of the preset travel route has charging conditions specifically includes: 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.

49. The method as described in claim 42, characterized in that, 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 its 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 requirement 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 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 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.

50. A control device, characterized in that, The control device 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 any one of claims 1-49.

51. A smart energy management system for new energy vehicles, characterized in that, include: A drive unit, comprising an engine, a drive motor, and a generator, wherein the engine is used to selectively output power to the wheel ends of the vehicle; The drive motor is used to output power to the wheel end; the generator is connected to the engine to generate electricity under the drive of the engine; A power battery, used to supply power to the drive motor and to charge it according to the current output by the generator or the drive motor; A control device is configured to determine at least one candidate energy-saving path based on the vehicle's starting point and ending point; wherein 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 of any candidate energy-saving path is predicted based on the vehicle's theoretical energy demand and reference energy demand on that candidate energy-saving path; the theoretical energy demand is predicted based on road traffic flow speed and the vehicle's static parameters; the reference energy demand is predicted based on road type, user's driving style, and vehicle model information. 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; 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 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 operating range.

52. A vehicle, characterized in that, The vehicle includes the new energy vehicle energy intelligent management system as described in claim 51.

53. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-49.

54. A computer program product, characterized in that, The computer program product includes a computer program adapted to be loaded by a processor and executed as described in any one of claims 1-49.

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