Vehicle energy recovery method and device, vehicle and storage medium

Abstract

This application relates to the field of vehicle technology, and in particular to a vehicle energy recovery method, device, vehicle, and storage medium. The method includes: performing layered fusion processing on the current vehicle's status information, road condition information, traffic signal information, and neighboring vehicle operating status information; identifying the current driving scenario of the current vehicle based on the fused information; calculating the target energy recovery intensity of the current driving scenario; converting it into a recovery torque command; and sending it to the motor controller, so that the motor controller can perform energy recovery based on the recovery torque command. This solves the problems of low energy recovery efficiency, poor environmental adaptability, and difficulty in balancing comfort caused by unpredictable road conditions in related technologies, thereby achieving a dual optimization of maximizing energy recovery efficiency and driving comfort in complex traffic scenarios.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle energy recovery method, apparatus, vehicle, and storage medium. Background Technology

[0002] Electric vehicle energy recovery systems convert kinetic energy generated during braking or coasting into electrical energy and store it in the battery, which is a key technology for improving driving range. Related technologies primarily rely on vehicle sensors (such as vehicle speed and brake pedal signals) to implement passive recovery strategies such as braking-triggered or coasting-triggered approaches. These strategies are mature in structure, have a direct response, and are widely used in mass-produced vehicles.

[0003] However, passive energy recovery strategies in related technologies have significant limitations in complex traffic environments. Because they cannot anticipate road conditions ahead (such as slope, curvature, and friction coefficient), traffic signal status (such as remaining traffic light time), and the dynamics of surrounding vehicles (such as sudden braking by the vehicle in front), their recovery behavior is lagging and fixed. While some vehicle models offer multiple levels of recovery intensity adjustment, they still struggle to dynamically adjust according to driving scenarios, resulting in insufficient energy recovery and low utilization efficiency in typical scenarios such as long downhill slopes and intersections, which urgently need to be addressed. Summary of the Invention

[0004] This application provides a vehicle energy recovery method, device, vehicle, and storage medium to solve the problems of low energy recovery efficiency, poor environmental adaptability, and difficulty in balancing comfort caused by unpredictable road conditions in related technologies, thereby achieving a dual optimization of maximizing energy recovery efficiency and driving comfort in complex traffic scenarios.

[0005] The first aspect of this application provides a vehicle energy recovery method, including the following steps: Obtain current vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information; The vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information are subjected to hierarchical fusion processing to obtain fused information; Based on the fused information, the current driving scenario of the current vehicle is identified, and the target energy recovery intensity of the current driving scenario is calculated. The target energy recovery intensity is converted into a recovery torque command, and the recovery torque command is sent to the motor controller so that the motor controller can perform energy recovery based on the recovery torque command.

[0006] By employing the aforementioned technical means, and by acquiring multi-source information including vehicle status, road conditions, traffic signals, and neighboring vehicle status, and performing layered fusion processing to identify scenarios and calculate target recovery intensity, the problem of information dimension loss caused by existing technologies relying solely on a single vehicle signal is solved. This enables proactive perception and dynamic recovery in complex traffic environments, improving the adaptability and efficiency of energy recovery.

[0007] According to one embodiment of this application, the current driving scenario is a traffic light intersection scenario, and calculating the target energy recovery intensity of the current driving scenario includes: The remaining time of the traffic light signal, the distance between the current vehicle and the intersection, the current vehicle speed, and the recovery efficiency coefficient are obtained. The target energy recovery intensity for the current driving scenario is calculated based on the remaining time, the distance between the current vehicle and the intersection, the current vehicle speed, and the energy recovery efficiency coefficient.

[0008] By employing the aforementioned technical means, and specifically targeting traffic light intersections, the remaining time, distance, and current vehicle speed are used to calculate the intensity of coasting recovery. This replaces the traditional delayed strategy that relies on the brake pedal, thereby enabling precise coasting deceleration before a red light and maximizing the recovery of kinetic energy. This avoids unnecessary braking losses and significantly improves energy utilization in intersection scenarios.

[0009] According to one embodiment of this application, the current driving scenario is a downhill scenario, and calculating the target energy recovery intensity of the current driving scenario includes: Obtain current road gradient information, vehicle mass, current vehicle speed, and maximum regenerative braking force of the electric motor; The component of gravity along the slope is calculated based on the current road slope information and the vehicle mass; The target energy recovery intensity for the current driving scenario is obtained based on the component of gravity along the slope, the current vehicle speed, and the maximum recoverable force of the motor braking.

[0010] Using the above-mentioned technical means, for downhill scenarios, the gravity component is calculated by utilizing slope information and the recovery intensity is set accordingly. This enables the recovery braking force to counteract the acceleration due to gravity and maintain vehicle speed stability, thereby maximizing the recovery of potential energy while reducing mechanical brake wear.

[0011] According to one embodiment of this application, the current driving scenario is a following vehicle scenario, and calculating the target energy recovery intensity of the current driving scenario includes: The speed of the vehicle in front of the current vehicle, the relative distance between the current vehicle and the vehicle in front, and the acceleration of the vehicle in front are obtained. The target energy recovery intensity of the current driving scenario is calculated based on the speed of the vehicle in front, the relative distance, and the acceleration of the vehicle in front, using a preset safe distance algorithm.

[0012] By employing the aforementioned technical means, and targeting the following vehicle scenario, the energy recovery intensity is dynamically adjusted by incorporating the speed of the vehicle in front, the relative distance, and the acceleration, combined with a safe distance algorithm. This achieves smooth energy recovery while maintaining a safe following distance, thus balancing driving safety and energy utilization efficiency.

[0013] According to one embodiment of this application, the current driving scenario is a comprehensive driving scenario, and calculating the target energy recovery intensity of the current driving scenario includes: Identify whether multiple sub-scenes are triggered simultaneously; If multiple sub-scenes are triggered simultaneously, the scene priority weight corresponding to each sub-scene is obtained respectively; Based on the energy recovery intensity of each sub-scenario and the corresponding scenario priority weight, the target energy recovery intensity of the current driving scenario is calculated using a weighted summation method.

[0014] By employing the aforementioned technical means, for comprehensive scenarios triggered simultaneously by multiple scenarios, priority weights are introduced and weighted summation is performed to calculate the comprehensive recovery intensity, thereby achieving global optimal control under multi-objective constraints and ensuring the logical consistency and stability of the system under various superimposed working conditions.

[0015] According to one embodiment of this application, the step of performing layered fusion processing on the vehicle status information, the road condition information, the traffic signal information, and the neighboring vehicle operating status information to obtain fused information includes: The vehicle status information, road condition information, traffic signal information, and neighboring vehicle operation status information are preprocessed to obtain preprocessed information. The data preprocessing includes at least one of data cleaning, timestamp alignment, and coordinate unification. Feature extraction is performed on the preprocessed information to obtain a first feature vector corresponding to the vehicle status information, a second feature vector corresponding to the road condition information, a third feature vector corresponding to the traffic signal information, and a fourth feature vector corresponding to the neighboring vehicle operating status information; The first feature vector, the second feature vector, the third feature vector, and the fourth feature vector are fused to obtain the fused information.

[0016] By employing the aforementioned technical methods, and through layered fusion processing such as data cleaning, time alignment, and feature extraction of multi-source information, the problem of low fusion accuracy caused by noise, time asynchrony, and coordinate inconsistency in the original sensor data is solved. This provides high-quality, strongly correlated feature input for scene recognition, directly improving the accuracy and robustness of subsequent energy recovery intensity calculation.

[0017] According to the vehicle energy recovery method provided in this application, the vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information of the current vehicle are layered and fused. Based on the fused information, the current driving scenario of the current vehicle is identified, and the target energy recovery intensity of the current driving scenario is calculated, converted into a recovery torque command, and sent to the motor controller, so that the motor controller can perform energy recovery based on the recovery torque command. This solves the problems of low energy recovery efficiency, poor environmental adaptability, and difficulty in balancing comfort caused by unpredictable road conditions in related technologies, thereby achieving a dual optimization of maximizing energy recovery efficiency and driving comfort in complex traffic scenarios.

[0018] A second aspect of this application provides a vehicle energy recovery device, comprising: The acquisition module is used to acquire the current vehicle's status information, road condition information, traffic signal information, and the operating status information of neighboring vehicles; The fusion module is used to perform hierarchical fusion processing on the vehicle status information, the road condition information, the traffic signal information, and the neighboring vehicle operation status information to obtain fused information; The energy recovery module is used to identify the current driving scenario of the current vehicle based on the fused information, calculate the target energy recovery intensity of the current driving scenario, convert the target energy recovery intensity into a recovery torque command, and send the recovery torque command to the motor controller so that the motor controller can perform energy recovery based on the recovery torque command.

[0019] According to one embodiment of this application, the current driving scenario is a traffic light intersection scenario, and the energy recovery module is used for: The remaining time of the traffic light signal, the distance between the current vehicle and the intersection, the current vehicle speed, and the recovery efficiency coefficient are obtained. The target energy recovery intensity for the current driving scenario is calculated based on the remaining time, the distance between the current vehicle and the intersection, the current vehicle speed, and the energy recovery efficiency coefficient.

[0020] According to one embodiment of this application, the current driving scenario is a downhill scenario, and the energy recovery module is used for: Obtain current road gradient information, vehicle mass, current vehicle speed, and maximum regenerative braking force of the electric motor; The component of gravity along the slope is calculated based on the current road slope information and the vehicle mass; The target energy recovery intensity for the current driving scenario is obtained based on the component of gravity along the slope, the current vehicle speed, and the maximum recoverable force of the motor braking.

[0021] According to one embodiment of this application, the current driving scenario is a following vehicle scenario, and the energy recovery module is used for: The speed of the vehicle in front of the current vehicle, the relative distance between the current vehicle and the vehicle in front, and the acceleration of the vehicle in front are obtained. The target energy recovery intensity of the current driving scenario is calculated based on the speed of the vehicle in front, the relative distance, and the acceleration of the vehicle in front, using a preset safe distance algorithm.

[0022] According to one embodiment of this application, the current driving scenario is a comprehensive driving scenario, and the energy recovery module is used for: Identify whether multiple sub-scenes are triggered simultaneously; If multiple sub-scenes are triggered simultaneously, the scene priority weight corresponding to each sub-scene is obtained respectively; Based on the energy recovery intensity of each sub-scenario and the corresponding scenario priority weight, the target energy recovery intensity of the current driving scenario is calculated using a weighted summation method.

[0023] According to one embodiment of this application, the fusion module is used for: The vehicle status information, road condition information, traffic signal information, and neighboring vehicle operation status information are preprocessed to obtain preprocessed information. The data preprocessing includes at least one of data cleaning, timestamp alignment, and coordinate unification. Feature extraction is performed on the preprocessed information to obtain a first feature vector corresponding to the vehicle status information, a second feature vector corresponding to the road condition information, a third feature vector corresponding to the traffic signal information, and a fourth feature vector corresponding to the neighboring vehicle operating status information; The first feature vector, the second feature vector, the third feature vector, and the fourth feature vector are fused to obtain the fused information.

[0024] According to the vehicle energy recovery device provided in this application embodiment, the vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information of the current vehicle are fused in a layered manner. Based on the fused information, the current driving scenario of the current vehicle is identified, and the target energy recovery intensity of the current driving scenario is calculated, converted into a recovery torque command, and sent to the motor controller, so that the motor controller can perform energy recovery based on the recovery torque command. This solves the problems of low energy recovery efficiency, poor environmental adaptability, and difficulty in balancing comfort caused by unpredictable road conditions in related technologies, thereby achieving a dual optimization of maximizing energy recovery efficiency and driving comfort in complex traffic scenarios.

[0025] A third aspect of this application provides a vehicle, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle energy recovery method as described in the above embodiments.

[0026] A fourth aspect of this application provides a computer-readable storage medium storing computer instructions for causing the computer to perform the vehicle energy recovery method as described in the above embodiments.

[0027] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0028] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the structure of an in-vehicle system according to an embodiment of this application; Figure 2 This is a flowchart of a vehicle energy recovery method according to an embodiment of this application; Figure 3 This is a schematic diagram of an energy recovery method according to an embodiment of this application; Figure 4 This is a block diagram of a vehicle energy recovery device according to an embodiment of this application; Figure 5 This is a structural schematic diagram of the vehicle provided in an embodiment of this application. Detailed Implementation

[0029] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0030] As those skilled in the art will understand, with the popularization of new energy vehicles, the driving range and energy efficiency of electric vehicles have become core concerns for users. Energy Recycle Systems (ERS), as a key technology for improving driving range, convert the kinetic energy of a vehicle during braking or coasting into electrical energy stored in the battery, significantly reducing energy consumption and increasing driving range.

[0031] In related technologies, existing energy recovery strategies for electric vehicles mostly rely on the vehicle's own sensors (such as vehicle speed and brake pedal signals) to achieve "passive" energy recovery through methods such as brake-triggered recovery and coasting-triggered recovery. As electric vehicles demand increasingly longer driving ranges, "passive" energy recovery can no longer meet the needs of refined energy recovery control in dynamic application scenarios.

[0032] Existing "passive" energy recovery strategies, such as braking-triggered and coasting-triggered strategies, have the following drawbacks: (1) Poor environmental adaptability: It is impossible to perceive the road conditions ahead (such as slope, curvature, road surface friction coefficient), traffic signals (such as traffic light duration) and the status of surrounding vehicles (such as sudden braking of the vehicle in front) in advance. The energy recovery strategy is lagging behind and cannot adjust the recovery intensity according to the driving scenario. (2) Low energy recovery efficiency: Relying on driver braking to trigger recovery, or using fixed intensity recovery (such as low / medium / high three levels), it is difficult to match the dynamic energy recovery needs under complex road conditions, especially in scenarios such as long downhill slopes and traffic light intersections, where kinetic energy is not fully recovered. (3) Conflict between comfort and recycling efficiency: In pursuit of recycling efficiency, strong recycling may lead to an excessive "dragging feeling" in the vehicle; in order to ensure comfort, weak recycling sacrifices energy utilization. It is difficult to achieve a balance between the two with a fixed recycling intensity.

[0033] Therefore, there is an urgent need for a technical solution that can proactively optimize energy recovery strategies by incorporating environmental information in order to solve the above problems.

[0034] To address the shortcomings of existing electric vehicle energy recovery strategies, such as poor environmental adaptability, low recovery efficiency, and difficulty in balancing comfort and recovery efficiency, this invention provides a dynamic optimization method for electric vehicle energy recovery based on vehicle-road cooperation. By integrating vehicle-road cooperation information with the vehicle's own status, it breaks through the limitations of traditional "passive recovery," achieving active and dynamic optimization of the energy recovery strategy, improving the energy recovery efficiency of electric vehicles, extending driving range, and enhancing driving comfort.

[0035] The vehicle energy recovery method, apparatus, vehicle, and storage medium of this application are described below with reference to the accompanying drawings.

[0036] Before introducing the vehicle energy recovery method of this application, let me first introduce the vehicle system of this application.

[0037] like Figure 1 As shown, the modules and their functions in this vehicle system are as follows: Onboard Unit (OBU): Installed in electric vehicles, it is used to communicate with roadside equipment, surrounding vehicles and cloud platforms to obtain external environmental information; Roadside Unit (RSU): Deployed on both sides of the road (such as at intersections, ramp start points, and tunnel entrances / exits) to collect and broadcast road condition information (slope, curvature, road surface friction coefficient), traffic signal information (traffic light duration, countdown) and regional traffic flow data; Surrounding vehicle communication module: Obtains the driving status (real-time speed, acceleration, relative distance, brake light status) of vehicles in front / adjacent via V2V (vehicle-to-vehicle communication); Vehicle Status Acquisition Module: Integrated into electric vehicles, it collects the vehicle's own status parameters, including vehicle battery SOC (State of Charge), current speed, acceleration, battery temperature, and brake pad temperature; Energy recovery control module: As the core module of this system, it receives the above environmental information and vehicle status parameters, calculates the optimal energy recovery intensity and intervention timing through optimization algorithms, and outputs control commands to the motor controller; Motor controller: Adjusts the energy recovery torque of the motor according to control commands to achieve dynamic adjustment of the energy recovery intensity.

[0038] The vehicle system of this application deeply integrates V2X (V2I+V2V+V2N) environmental information with the vehicle's own status, performs driving scenario recognition, and designs differentiated recovery strategies for different scenarios (traditional scenarios such as traffic lights, slopes, and following other vehicles). It quantifies the recovery intensity through mathematical models to balance energy recovery efficiency and comfort.

[0039] The vehicle energy recovery method proposed in this application is described in detail below.

[0040] Specifically, Figure 2 This is a schematic flowchart of a vehicle energy recovery method provided in an embodiment of this application.

[0041] like Figure 2 As shown, the vehicle energy recovery method includes the following steps: In step S201, the vehicle status information, road condition information, traffic signal information, and neighboring vehicle operation status information of the current vehicle are obtained.

[0042] Specifically, in this embodiment, the vehicle terminal can receive road condition information (such as road slope ±5%, radius of curvature 100 meters, road surface friction coefficient 0.8, etc.) broadcast by roadside equipment via V2X (V2I / V2V / V2N), traffic signal information (such as the remaining red light time of the traffic light ahead is 20 seconds, distance from the intersection is 300 meters), and V2V data of surrounding vehicles (such as the vehicle speed 30km / h and acceleration -2m / s² of the vehicle 100 meters ahead); at the same time, it can obtain its own vehicle information (such as SOC=60%, current speed 60km / h, battery temperature 25℃) through the vehicle status acquisition module.

[0043] In step S202, the vehicle status information, road condition information, traffic signal information, and neighboring vehicle operation status information are subjected to hierarchical fusion processing to obtain the fused information.

[0044] Furthermore, in some embodiments, the vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information are subjected to hierarchical fusion processing to obtain fused information. This includes: performing data preprocessing on the vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information to obtain preprocessed information, wherein the data preprocessing includes at least one of data cleaning, timestamp alignment, and coordinate unification; extracting features from the preprocessed information to obtain a first feature vector corresponding to the vehicle status information, a second feature vector corresponding to the road condition information, a third feature vector corresponding to the traffic signal information, and a fourth feature vector corresponding to the neighboring vehicle operating status information; and fusing the first feature vector, second feature vector, third feature vector, and fourth feature vector to obtain fused information.

[0045] Specifically, after information collection is completed, information fusion is performed. The information fusion adopts a "layered fusion architecture," which is divided into data layer fusion and feature layer fusion. Data layer fusion: Deduplication of raw V2I ​​and V2V data (such as retaining the latest data for the same traffic light time period when it is broadcast by multiple RSUs), and time synchronization (based on GNSS time alignment, with an error of ≤1ms). Feature layer fusion: Extracting key features, such as "road slope sequence within 500 meters ahead" (discrete slope values ​​broadcast by RSU interpolated into a continuous function, with a sampling interval of 10 meters) and "traffic light arrival time prediction" (calculated by combining vehicle speed and distance, formula: ,in For communication delay compensation, a value of 0.1 seconds is used. v For vehicle speed, D (e.g., distance).

[0046] In step S203, the current driving scenario of the vehicle is identified based on the fused information, and the target energy recovery intensity of the current driving scenario is calculated. The target energy recovery intensity is converted into a recovery torque command, and the recovery torque command is sent to the motor controller so that the motor controller can perform energy recovery based on the recovery torque command.

[0047] Specifically, in this embodiment, the energy recovery control module identifies the current driving scenario based on fused information and prioritizes scenarios according to their impact on energy recovery. After scenario identification, fuzzy logic is used to convert the priorities into calculable weight coefficients. (0~1), the input includes three dimensions: "crisis urgency", "energy recovery potential" and "safety risk", and the output is a comprehensive weight. : High-priority scenarios include: traffic light intersections (requiring slowdown and stopping), long downhill slopes (for gravitational potential energy recovery), and sudden braking by the vehicle in front (for safe deceleration). If the red light countdown is ≤5 seconds and the distance to the intersection is ≤100 meters, the urgency level is assessed as 1.0, the recovery potential as 0.9, and the safety risk as 0.8. Weighting is then applied accordingly. =0.6 (calculated using a fuzzy rule base); Medium priority scenarios: following other vehicles (maintaining a safe distance), driving on curves (avoiding skidding), etc.; for example, when following another vehicle, the relative distance is 30 meters, and the acceleration of the vehicle in front... =-1.5m / s², urgency level = 0.5, recovery potential = 0.6, safety risk = 0.7, calculate weights. =0.3; Low-priority scenario: Straight road, constant speed (balancing recovery and comfort), no vehicle ahead, SOC=80%, urgency level assessment=0.1, recovery potential=0.3, safety risk=0.2, weight calculation. =0.1.

[0048] Furthermore, for different scenarios, the energy recovery control module uses different optimization algorithms to calculate the recovery intensity (0~100%, corresponding to the motor recovery torque from 0 to the maximum torque).

[0049] Furthermore, in some embodiments, the current driving scenario is a traffic light intersection scenario. Calculating the target energy recovery intensity of the current driving scenario includes: obtaining the remaining time of the traffic light signal, the current distance between the vehicle and the intersection, the current vehicle speed, and the recovery efficiency coefficient; and calculating the target energy recovery intensity of the current driving scenario based on the remaining time, the current distance between the vehicle and the intersection, the current vehicle speed, and the recovery efficiency coefficient.

[0050] Specifically, if the preceding driving scenario is a traffic light intersection scenario (high priority), the goal of energy recovery is to coast to the intersection and stop before the red light ends, while maximizing energy recovery.

[0051] If the light ahead is red and the remaining time is The vehicle is currently at a distance of [distance] from the intersection. The current speed is The formula for calculating the "recovery intensity required for coasting deceleration" is as follows: ,in, The target speed when reaching the intersection (0 when the light is red, and the speed when the light is green). The recycling efficiency coefficient (dynamically adjusted based on battery SOC; the lower the SOC) The higher (the higher).

[0052] For example: 20 seconds remaining on the red light, 300 meters from the intersection, current speed 60 km / h (16.7 m / s), target speed 0. =0.85, calculated as follows =54%, meaning the initial recovery intensity is 54%. As the vehicle speed and distance from the traffic light decrease, the recovery intensity gradually decreases. By recovering kinetic energy and slowing down to a stop at the intersection, the dynamic adjustment of the recovery intensity can improve energy recovery efficiency while reducing the "drag" feeling and improving comfort.

[0053] Furthermore, in some embodiments, the current driving scenario is a downhill scenario. Calculating the target energy recovery intensity of the current driving scenario includes: obtaining current road slope information, vehicle mass, current vehicle speed, and maximum recoverable force of motor braking; calculating the component of gravity along the slope based on the current road slope information and vehicle mass; and obtaining the target energy recovery intensity of the current driving scenario based on the component of gravity along the slope, current vehicle speed, and maximum recoverable force of motor braking.

[0054] Specifically, if the preceding driving scenario is a long downhill section (high priority), then the vehicle speed is maintained by recovering the braking force to balance the gravitational force (avoiding frequent braking) while maximizing energy recovery.

[0055] First, receive the slope information broadcast by the roadside equipment. (Assuming a slope of -8%), combined with vehicle weight Gravitational acceleration Calculate the component of gravity along the ramp. The recycling intensity is set as follows: Recovery intensity and vehicle speed The linear relationship allows for dynamic adjustment of energy recovery intensity to control vehicle speed, maintain stable speed, reduce the frequency of braking, and maximize the recovery of kinetic and gravitational potential energy during downhill driving.

[0056] Furthermore, in some embodiments, the current driving scenario is a following vehicle scenario. Calculating the target energy recovery intensity of the current driving scenario includes: obtaining the speed of the vehicle in front of the current vehicle, the relative distance between the current vehicle and the vehicle in front, and the acceleration of the vehicle in front; and calculating the target energy recovery intensity of the current driving scenario based on a preset safe distance algorithm according to the speed of the vehicle in front, the relative distance, and the acceleration of the vehicle in front.

[0057] Specifically, if the driving scenario ahead is following another vehicle (medium priority), then the braking force is recovered to maintain a safe distance from the vehicle ahead, maximizing energy recovery during the following process.

[0058] Get the speed of the vehicle in front via V2V Relative distance acceleration The energy recovery intensity is calculated using an optimized model algorithm. ,coefficient Determined based on the actual vehicle model, using a safety distance model (such as...) , =1.5s is the safe reaction time. For maximum deceleration), dynamically adjust recovery intensity: current vehicle deceleration ( < and < When the vehicle is accelerating or the distance to the vehicle is increasing, increase the recovery intensity; when the vehicle ahead is accelerating or the distance to the vehicle is increasing, decrease the recovery intensity to avoid frequent acceleration and deceleration.

[0059] Furthermore, in some embodiments, the current driving scenario is a comprehensive driving scenario. Calculating the target energy recovery intensity of the current driving scenario includes: identifying whether multiple sub-scenarios are triggered simultaneously; if multiple sub-scenarios are triggered simultaneously, obtaining the scenario priority weight corresponding to each sub-scenarios; and calculating the target energy recovery intensity of the current driving scenario by weighted summation based on the energy recovery intensity of each sub-scenarios and the corresponding scenario priority weight.

[0060] Specifically, if the preceding driving scenario involves complex scenario conflict handling, i.e., when multiple scenarios are triggered simultaneously (such as both a red light and a downhill slope ahead), the priorities are integrated using a weighted coefficient method: high-priority scenarios have a weight of 0.6, medium-priority scenarios 0.3, and low-priority scenarios 0.1. The overall recovery intensity is then calculated using a weighted summation method. .

[0061] Furthermore, the motor controller receives the regenerative torque command (accuracy ±5Nm), and adjusts the motor excitation current via the IGBT power module to achieve regenerative braking. In addition, a feedback closed loop is executed, sampling the actual deceleration every 10ms. If the actual deceleration deviates from the target deceleration by more than 0.2 m / s², the recovery intensity is dynamically corrected (recovery intensity correction value). ,in (This is a proportionality coefficient, determined based on the actual vehicle model).

[0062] To ensure the algorithm's successful implementation, this application also requires calibration of key parameters through bench tests and vehicle tests: Bench testing: On a powertrain test bench (such as AVL PUMA), simulate the recovery efficiency under different SOC (10%~90%) and temperatures (-30℃~55℃). Create a mapping table Mapping table (e.g., when SOC=20%) =0.85, SOC=90% =0.5); Full vehicle testing: Simulated scenarios (traffic lights, ramps, wet surfaces) were built in a closed test track, and multiple sets of data were collected. The scenario weight coefficients were optimized using the least squares method (e.g., =0.6 was adjusted to 0.55 to improve comfort.

[0063] To facilitate a more intuitive understanding of the vehicle energy recovery method of this application by those skilled in the art, the following is combined with... Figure 3 Please provide a detailed explanation.

[0064] like Figure 3 As shown, the steps of this energy recovery optimization algorithm include: First, start the vehicle by powering it on.

[0065] S1 performs multi-source information acquisition and fusion processing; S2 performs scene recognition and priority classification; S3, calculate the optimal recycling intensity using an optimization algorithm; S4, perform energy recovery based on the optimal recovery intensity and provide feedback; Finally, the vehicle was stopped and the power was turned off.

[0066] Therefore, this application identifies vehicle driving scenarios through V2X information and vehicle status information, and adjusts the "active" energy recovery strategy according to the scenario, thus adapting to complex traffic environments. In addition, by recognizing scenarios and dynamically adjusting the energy recovery intensity, it improves energy recovery efficiency and range. It can also adjust the energy recovery intensity in real time according to the vehicle's own acceleration, avoiding a strong "dragging feeling" caused by sudden intervention of large energy recovery intensity (such as rapidly releasing the accelerator under high recovery intensity), thereby improving driving comfort.

[0067] The vehicle energy recovery method proposed in this application involves hierarchical fusion processing of the current vehicle's status information, road condition information, traffic signal information, and neighboring vehicle operating status information. Based on the fused information, the current driving scenario of the current vehicle is identified, and the target energy recovery intensity for the current driving scenario is calculated. This is converted into a recovery torque command and sent to the motor controller, which then performs energy recovery based on the recovery torque command. This solves the problems of low energy recovery efficiency, poor environmental adaptability, and difficulty in balancing comfort caused by unpredictable road conditions in related technologies, thereby achieving a dual optimization of maximizing energy recovery efficiency and driving comfort in complex traffic scenarios.

[0068] Next, the vehicle energy recovery device according to the embodiments of this application is described with reference to the accompanying drawings.

[0069] Figure 4 This is a block diagram of a vehicle energy recovery device according to an embodiment of this application.

[0070] like Figure 4 As shown, the vehicle energy recovery device 10 includes: an acquisition module 100, a fusion module 200, and an energy recovery module 300.

[0071] The system includes an acquisition module 100, which acquires the current vehicle's status information, road condition information, traffic signal information, and neighboring vehicle operating status information; a fusion module 200, which performs layered fusion processing on the vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information to obtain fused information; and an energy recovery module 300, which identifies the current driving scenario of the current vehicle based on the fused information, calculates the target energy recovery intensity of the current driving scenario, converts the target energy recovery intensity into a recovery torque command, and sends the recovery torque command to the motor controller so that the motor controller can perform energy recovery based on the recovery torque command.

[0072] Furthermore, in some embodiments, the current driving scenario is a traffic light intersection scenario, and the energy recovery module 300 is used to: obtain the remaining time of the traffic light signal, the current distance between the vehicle and the intersection, the current vehicle speed, and the recovery efficiency coefficient; and calculate the target energy recovery intensity of the current driving scenario based on the remaining time, the current distance between the vehicle and the intersection, the current vehicle speed, and the recovery efficiency coefficient.

[0073] Furthermore, in some embodiments, the current driving scenario is a downhill scenario, and the energy recovery module 300 is used to: acquire current road slope information, vehicle mass, current vehicle speed and maximum recoverable force of motor braking; calculate the component of gravity along the slope based on the current road slope information and vehicle mass; and obtain the target energy recovery intensity of the current driving scenario based on the component of gravity along the slope, current vehicle speed and maximum recoverable force of motor braking.

[0074] Furthermore, in some embodiments, the current driving scenario is a following vehicle scenario, and the energy recovery module 300 is used to: obtain the speed of the vehicle in front of the current vehicle, the relative distance between the current vehicle and the vehicle in front, and the acceleration of the vehicle in front; and calculate the target energy recovery intensity of the current driving scenario based on a preset safe distance algorithm according to the speed of the vehicle in front, the relative distance, and the acceleration of the vehicle in front.

[0075] Furthermore, in some embodiments, the current driving scenario is a comprehensive driving scenario, and the energy recovery module 300 is used to: identify whether multiple sub-scenarios are triggered simultaneously; if multiple sub-scenarios are triggered simultaneously, obtain the scenario priority weight corresponding to each sub-scenarios respectively; and calculate the target energy recovery intensity of the current driving scenario by weighted summation based on the energy recovery intensity of each sub-scenarios and the corresponding scenario priority weight.

[0076] Furthermore, in some embodiments, the fusion module 200 is used to: perform data preprocessing on vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information to obtain preprocessed information, wherein the data preprocessing includes at least one of data cleaning, timestamp alignment, and coordinate unification; extract features from the preprocessed information to obtain a first feature vector corresponding to the vehicle status information, a second feature vector corresponding to the road condition information, a third feature vector corresponding to the traffic signal information, and a fourth feature vector corresponding to the neighboring vehicle operating status information; and fuse the first feature vector, the second feature vector, the third feature vector, and the fourth feature vector to obtain fused information.

[0077] It should be noted that the foregoing explanation of the vehicle energy recovery method embodiment also applies to the vehicle energy recovery device of this embodiment, and will not be repeated here.

[0078] The vehicle energy recovery device proposed in this application performs layered fusion processing on the current vehicle's status information, road condition information, traffic signal information, and neighboring vehicle operating status information. Based on the fused information, it identifies the current driving scenario of the current vehicle, calculates the target energy recovery intensity for the current driving scenario, converts it into a recovery torque command, and sends it to the motor controller. The motor controller then performs energy recovery based on the recovery torque command. This solves the problems of low energy recovery efficiency, poor environmental adaptability, and difficulty in balancing comfort caused by unpredictable road conditions in related technologies, thereby achieving a dual optimization of maximizing energy recovery efficiency and driving comfort in complex traffic scenarios.

[0079] Figure 5 A schematic diagram of the structure of a vehicle provided in an embodiment of this application. The vehicle may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0080] When processor 502 executes the program, it implements the vehicle energy recovery method provided in the above embodiments.

[0081] Furthermore, the vehicle also includes: Communication interface 503 is used for communication between memory 501 and processor 502.

[0082] The memory 501 is used to store computer programs that can run on the processor 502.

[0083] Memory 501 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0084] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0085] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0086] Processor 502 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0087] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described vehicle energy recovery method.

[0088] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0089] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0090] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0091] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0092] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0093] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0094] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0095] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A vehicle energy recovery method, characterized by, Includes the following steps: Obtain current vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information; The vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information are subjected to hierarchical fusion processing to obtain fused information; Based on the fused information, the current driving scenario of the current vehicle is identified, and the target energy recovery intensity of the current driving scenario is calculated. The target energy recovery intensity is converted into a recovery torque command, and the recovery torque command is sent to the motor controller so that the motor controller can perform energy recovery based on the recovery torque command.

2. The method of claim 1, wherein, The current driving scenario is a traffic light intersection scenario, and the calculation of the target energy recovery intensity of the current driving scenario includes: The remaining time of the traffic light signal, the distance between the current vehicle and the intersection, the current vehicle speed, and the recovery efficiency coefficient are obtained. The target energy recovery intensity for the current driving scenario is calculated based on the remaining time, the distance between the current vehicle and the intersection, the current vehicle speed, and the energy recovery efficiency coefficient.

3. The method of claim 1, wherein, The current driving scenario is a downhill scenario, and the calculation of the target energy recovery intensity for the current driving scenario includes: Obtain current road gradient information, vehicle mass, current vehicle speed, and maximum regenerative braking force of the electric motor; The component of gravity along the slope is calculated based on the current road slope information and the vehicle mass; The target energy recovery intensity for the current driving scenario is obtained based on the component of gravity along the slope, the current vehicle speed, and the maximum recoverable force of the motor braking.

4. The method according to claim 1, characterized in that, The current driving scenario is a following vehicle scenario, and the calculation of the target energy recovery intensity of the current driving scenario includes: The speed of the vehicle in front of the current vehicle, the relative distance between the current vehicle and the vehicle in front, and the acceleration of the vehicle in front are obtained. The target energy recovery intensity of the current driving scenario is calculated based on the speed of the vehicle in front, the relative distance, and the acceleration of the vehicle in front, using a preset safe distance algorithm.

5. The method according to claim 1, characterized in that, The current driving scenario is a comprehensive driving scenario, and the calculation of the target energy recovery intensity of the current driving scenario includes: Identify whether multiple sub-scenes are triggered simultaneously; If multiple sub-scenes are triggered simultaneously, the scene priority weight corresponding to each sub-scene is obtained respectively; Based on the energy recovery intensity of each sub-scenario and the corresponding scenario priority weight, the target energy recovery intensity of the current driving scenario is calculated using a weighted summation method.

6. The method according to claim 1, characterized in that, The process of performing layered fusion processing on the vehicle status information, road condition information, traffic signal information, and neighboring vehicle operating status information to obtain fused information includes: The vehicle status information, road condition information, traffic signal information, and neighboring vehicle operation status information are preprocessed to obtain preprocessed information. The data preprocessing includes at least one of data cleaning, timestamp alignment, and coordinate unification. Feature extraction is performed on the preprocessed information to obtain a first feature vector corresponding to the vehicle status information, a second feature vector corresponding to the road condition information, a third feature vector corresponding to the traffic signal information, and a fourth feature vector corresponding to the neighboring vehicle operating status information; The first feature vector, the second feature vector, the third feature vector, and the fourth feature vector are fused to obtain the fused information.

7. A vehicle energy recovery device, characterized in that, include: The acquisition module is used to acquire the current vehicle's status information, road condition information, traffic signal information, and the operating status information of neighboring vehicles; The fusion module is used to perform hierarchical fusion processing on the vehicle status information, the road condition information, the traffic signal information, and the neighboring vehicle operation status information to obtain fused information; The energy recovery module is used to identify the current driving scenario of the current vehicle based on the fused information, calculate the target energy recovery intensity of the current driving scenario, convert the target energy recovery intensity into a recovery torque command, and send the recovery torque command to the motor controller so that the motor controller can perform energy recovery based on the recovery torque command.

8. The apparatus according to claim 7, characterized in that, The current driving scenario is a traffic light intersection scenario, and the energy recovery module is used for: The remaining time of the traffic light signal, the distance between the current vehicle and the intersection, the current vehicle speed, and the recovery efficiency coefficient are obtained. The target energy recovery intensity for the current driving scenario is calculated based on the remaining time, the distance between the current vehicle and the intersection, the current vehicle speed, and the energy recovery efficiency coefficient.

9. A vehicle, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, the processor executing the computer program to implement the vehicle energy recovery method as described in any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program is executed by a processor to implement the vehicle energy recovery method as described in any one of claims 1-6.

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