Vehicle driving force distribution method and vehicle

Abstract

The embodiment of the invention provides a vehicle driving force distribution method and a vehicle, the vehicle is provided with a plurality of driving motors, the driving motors correspond to hardware constraint parameters, the vehicle is provided with a plurality of driving motors, and the driving motors correspond to the hardware constraint parameters, and the method comprises the steps that under the condition that the vehicle is in a running working condition, vehicle running scene data are acquired; performing scene classification on the vehicle operation scene data, and determining a current scene type; determining safety constraint parameters corresponding to the vehicle operation scene data; determining a driving force distribution coefficient in combination with the current scene type, the security constraint parameter and the hardware constraint parameter; and controlling the driving motor to output torque corresponding to the driving force distribution coefficient according to the driving force distribution coefficient. And a driving force distribution coefficient which meets the safety requirement and adapts to scene characteristics is calculated, and finally, each driving motor is controlled to output torque according to the driving force distribution coefficient, so that the vehicle can automatically adjust a driving strategy in different driving scenes.

Description

Technical Field

[0001] This application relates to the field of driving force distribution technology, and in particular to a vehicle driving force distribution method and a vehicle. Background Technology

[0002] Four-wheel drive vehicles, with their independently controllable torque output capability for each drive wheel, have become an important research direction in the field of autonomous driving. However, existing drive force distribution methods are mostly based on preset fixed operating modes or limited static vehicle parameters, and the operating parameters considered are relatively simple, resulting in a lack of sufficient adaptability when facing complex and ever-changing real-world driving scenarios. Summary of the Invention

[0003] This application provides a vehicle drive force distribution method and a vehicle, aiming to improve the problem that the drive force distribution method lacks sufficient adaptability when facing complex and ever-changing real-world driving scenarios.

[0004] To address the aforementioned problems, in a first aspect of this application, an embodiment discloses a vehicle drive force distribution method. The vehicle has multiple drive motors, and each drive motor corresponds to hardware constraint parameters, including: Acquire vehicle operation scenario data while the vehicle is in operation; The vehicle operation scenario data is classified to determine the current scenario type; Determine the safety constraint parameters corresponding to the vehicle operation scenario data; Based on the current scenario type, the safety constraint parameters, and the hardware constraint parameters, determine the driving force allocation coefficient; Based on the driving force distribution coefficient, the drive motor is controlled to output torque corresponding to the driving force distribution coefficient.

[0005] In this embodiment, by collecting and classifying vehicle operation scenario data in real time, the current driving scenario can be identified. Then, based on the current scenario type, safety constraint parameters matching that scenario type are determined. Next, by combining the safety constraint parameters with the inherent hardware constraint parameters of the drive motor, a drive force distribution coefficient that satisfies both safety requirements and adapts to scenario characteristics can be calculated. Finally, by controlling each drive motor to output torque according to the drive force distribution coefficient, the vehicle can automatically adjust its drive strategy under different driving scenarios.

[0006] Optionally, the vehicle operation scenario data includes road condition data and vehicle status data, and the safety constraint parameters include tire grip torque and vehicle stability threshold. The step of determining the safety constraint parameters corresponding to the vehicle operation scenario data includes: The tire vertical load is determined based on the vehicle status data; The tire grip torque is determined based on the road condition data and the tire vertical load. The vehicle stability threshold is determined based on the vehicle status data.

[0007] Optionally, the vehicle state data includes longitudinal acceleration and lateral acceleration, and the step of determining the tire vertical load based on the vehicle state data includes: Based on a preset vehicle dynamics model, the longitudinal acceleration and lateral acceleration are converted into tire vertical loads.

[0008] Optionally, the road condition data includes the road surface friction coefficient, and the step of determining the tire grip torque based on the road condition data and the tire vertical load includes: The product of the road surface friction coefficient, the tire vertical load, and the preset tire rolling radius is determined as the tire grip torque.

[0009] Optionally, the vehicle status data includes vehicle speed, and the step of determining the vehicle stability threshold based on the vehicle status data includes: When the vehicle speed is less than the first speed threshold, the vehicle stability threshold is determined to be the first stability threshold; If the vehicle speed is not less than the first speed threshold, the vehicle stability threshold is determined to be the second stability threshold; the first stability threshold is greater than the second stability threshold.

[0010] Optionally, the step of determining the driving force allocation coefficient by combining the current scene type, the safety constraint parameters, and the hardware constraint parameters includes: Based on the current scenario type and the vehicle operation scenario data, determine the multi-objective optimization function; The multi-objective optimization function is solved based on the safety constraint parameters and the hardware constraint parameters to obtain the driving force allocation coefficients.

[0011] Optionally, the step of determining the multi-objective optimization function based on the current scenario type and the vehicle operation scenario data includes: Based on preset weight configuration rules, the current scenario type is converted into stability weight, control weight, and energy consumption weight. The stability control coefficient, handling control coefficient, and energy consumption control coefficient are determined based on vehicle operation scenario data. The product of the stability weight and the stability control coefficient is determined as the stability characteristic value; The product of the control weight and the control coefficient is determined as the control characteristic value; The product of the energy consumption weight and the energy consumption control coefficient is determined as the energy consumption characteristic value; A multi-objective optimization function is constructed by summing the stability feature value, the control feature value, and the energy consumption feature value.

[0012] Optionally, the vehicle status data includes actual yaw rate, ideal yaw rate, and wheel speed; the vehicle operating scenario data includes real-time current and real-time voltage corresponding to the drive motor; characterized in that the step of determining the stability control coefficient, handling control coefficient, and energy consumption control coefficient based on the vehicle operating scenario data includes: The difference between the actual yaw rate and the ideal yaw rate is defined as the vehicle yaw rate deviation. The product of the wheel speed and the preset tire rolling radius is determined to be the tire circumferential speed; The difference between the vehicle speed and the tire circumferential speed is determined as the first difference value; The quotient of the first difference and the vehicle speed is determined to be the wheel slip ratio; Based on a preset first weighting coefficient, the stability control coefficient is obtained by weighted summation of the vehicle yaw rate deviation and the wheel slip ratio. Based on the preset second weighting coefficient, the preset vehicle steering response speed and preset torque response speed are weighted and summed to obtain the handling control coefficient; The product of the real-time current and real-time voltage of the drive motor is determined as the real-time power of the drive motor. The energy consumption control coefficient is obtained by summing the real-time power of the multiple drive motors.

[0013] Optionally, the hardware constraint parameters include the rated torque of the drive motor and a preset upper limit for the torque change rate. The step of solving the multi-objective optimization function based on the safety constraint parameters and the hardware constraint parameters to obtain the driving force distribution coefficient includes: The vehicle stability threshold is determined as the boundary of the torque difference between the front and rear axles; The smaller of the tire grip torque and the drive motor rated torque is determined as the torque boundary. By combining the front and rear axle torque difference boundary, the preset torque change rate upper limit, and the torque boundary, a set of candidate driving force distribution coefficients is determined. The multi-objective optimization function is solved based on the set of candidate driving force allocation coefficients to obtain the driving force allocation coefficients.

[0014] In a second aspect, this application discloses a vehicle drive force distribution device, wherein the vehicle has multiple drive motors, and each drive motor corresponds to hardware constraint parameters, including: The data acquisition module is used to acquire vehicle operation scenario data when the vehicle is in operation. The classification module is used to classify the vehicle operation scenario data and determine the current scenario type; The first determining module is used to determine the safety constraint parameters corresponding to the vehicle operation scenario data; The second determining module is used to determine the driving force allocation coefficient by combining the current scene type, the safety constraint parameters, and the hardware constraint parameters; The control module is used to control the drive motor to output torque corresponding to the drive force distribution coefficient according to the drive force distribution coefficient.

[0015] In a third aspect of this application, embodiments of this application disclose a vehicle including a processor and a memory, wherein... The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the vehicle drive force distribution method as described above.

[0016] In a fourth aspect, embodiments of this application also disclose a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle drive force distribution method as described above.

[0017] The embodiments of this application have the following advantages: The vehicle described in this embodiment has multiple drive motors, each with corresponding hardware constraint parameters. When the vehicle is in operation, it acquires vehicle operating scenario data; classifies the data to determine the current scenario type; determines the corresponding safety constraint parameters; and, combining the current scenario type, the safety constraint parameters, and the hardware constraint parameters, determines a drive force distribution coefficient. Based on this coefficient, the drive motors are controlled to output torque corresponding to the drive force distribution coefficient. By real-time acquisition and classification of vehicle operating scenario data, the current driving scenario can be identified. Then, based on the current scenario type, safety constraint parameters matching that type are determined. Finally, by combining the safety constraint parameters with the inherent hardware constraint parameters of the drive motors, a drive force distribution coefficient that satisfies both safety requirements and adapts to scenario characteristics can be calculated. Ultimately, by controlling each drive motor to output torque according to the drive force distribution coefficient, the vehicle can automatically adjust its drive strategy under different driving scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of an embodiment of a vehicle drive force distribution method according to this application; Figure 2 This is a flowchart illustrating the steps of another embodiment of the vehicle drive force distribution method of this application; Figure 3 This is a schematic diagram illustrating an example of vehicle operation scenario data acquisition and control in this application; Figure 4 This is a flowchart illustrating the driving force allocation process of this application; Figure 5 This is a structural block diagram of an embodiment of a vehicle drive force distribution device according to this application; Figure 6 This is a structural block diagram of a vehicle provided in an embodiment of this application; Figure 7 This is a structural block diagram of a storage medium provided in an embodiment of this application. Detailed Implementation

[0019] To make the technical problems, technical solutions, and beneficial effects solved by this application clearer, the following detailed description is provided in conjunction with embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] Example 1 Reference Figure 1 The diagram illustrates a step flowchart of an embodiment of a vehicle drive force distribution method according to this application. This application provides a vehicle drive force distribution method, wherein the vehicle has multiple drive motors, and each drive motor corresponds to hardware constraint parameters.

[0021] In this embodiment of the invention, the arrangement of multiple drive motors may include, but is not limited to, four drive motors independently driving four wheels, or two drive motors driving the front and rear axles respectively. Hardware constraint parameters include at least the inherent characteristic parameters of each drive motor, such as rated torque, peak torque, preset upper limit of torque change rate, maximum speed, and peak power. These parameters collectively define the boundaries of the drive force distribution.

[0022] The vehicle drive force distribution method may specifically include the following steps: Step 101: Acquire vehicle operation scenario data while the vehicle is in driving condition; It can acquire vehicle operation scenario data sent by various sensors while the vehicle is in motion, in order to adjust the driving force distribution coefficient.

[0023] The operating conditions of a vehicle in motion can include a non-zero speed driving state, an electric state with the transmission in a forward or reverse gear, etc., indicating that the vehicle is running. Those skilled in the art can also determine other operating conditions based on actual circumstances. The embodiments of the present invention do not limit the specific operating conditions.

[0024] Step 102: Classify the vehicle operation scenario data to determine the current scenario type; After obtaining vehicle operation scenario data from multiple sensors, which may include environmental data, road condition data, vehicle status data, and driving task data, the current scenario type corresponding to the vehicle operation scenario data can be matched. When the current vehicle operation scenario data changes, new vehicle operation scenario data needs to be obtained again for judgment.

[0025] Step 103: Determine the safety constraint parameters corresponding to the vehicle operation scenario data; Based on real-time collected vehicle operation scenario data, various safety constraint parameters can be calculated to ensure vehicle driving safety. These safety constraint parameters may include, but are not limited to, tire grip torque, vehicle stability threshold, and drive force distribution conservation constraints. Safety constraint parameters characterize the safety conditions that a vehicle must adhere to to maintain a stable state under current driving conditions.

[0026] Step 104: Determine the driving force allocation coefficient by combining the current scene type, the safety constraint parameters, and the hardware constraint parameters.

[0027] Based on the current scenario type, safety constraint parameters, and hardware constraint parameters, the optimal driving force distribution coefficient that matches the current driving conditions can be quickly determined.

[0028] Step 105: Based on the driving force distribution coefficient, control the drive motor to output torque corresponding to the driving force distribution coefficient.

[0029] Based on the determined drive force distribution coefficient, the output torque of each drive motor corresponding to the drive force distribution coefficient can be controlled. This allows for precise adjustment of the output torque of each drive motor.

[0030] The vehicle described in this embodiment of the invention has multiple drive motors, each with corresponding hardware constraint parameters. When the vehicle is in driving condition, it acquires vehicle operating scenario data; classifies the vehicle operating scenario data to determine the current scenario type; determines the safety constraint parameters corresponding to the vehicle operating scenario data; and determines a driving force distribution coefficient by combining the current scenario type, the safety constraint parameters, and the hardware constraint parameters. Based on the driving force distribution coefficient, the drive motors are controlled to output torque corresponding to the driving force distribution coefficient. By real-time acquisition and classification of vehicle operating scenario data, the current driving scenario can be identified. Then, based on the current scenario type, safety constraint parameters matching that current scenario type are determined. Next, by combining the safety constraint parameters with the inherent hardware constraint parameters of the drive motors, a driving force distribution coefficient that satisfies both safety requirements and adapts to scenario characteristics can be calculated. Finally, by controlling each drive motor to output torque according to the driving force distribution coefficient, the vehicle can automatically adjust its driving strategy under different driving scenarios.

[0031] Example 2 Reference Figure 2 The diagram illustrates a flowchart of another embodiment of the vehicle drive force distribution method of this application, wherein the vehicle has multiple drive motors and each drive motor corresponds to hardware constraint parameters.

[0032] In this embodiment of the invention, the arrangement of multiple drive motors can be a four-wheel independent drive, a dual-motor drive, or a hybrid drive. The hardware constraint parameters include at least the rated torque of each drive motor and a preset upper limit for the torque change rate. The rated torque of each drive motor characterizes the maximum torque value that the drive motor can continuously and stably output, and the preset upper limit for the torque change rate characterizes the maximum allowable rate of change of the drive motor's output torque. These hardware constraint parameters ensure that the drive force distribution strategy remains stable and reliable during adjustment.

[0033] The vehicle drive force distribution method may specifically include the following steps: Step 201: Acquire vehicle operation scenario data while the vehicle is in driving condition; In situations such as non-zero speed driving or valid driving command input, or when the vehicle is in motion and requires the output torque of each drive motor, vehicle operation scene signals sent by various sensors can be acquired.

[0034] Specifically, when acquiring vehicle operation scenario data through various methods, one or more combinations of the following methods can be used, including but not limited to: Environmental data can be acquired through multiple sensors. For example, a rain sensor can be used to detect the intensity and amount of rainfall on the windshield, and a camera can be used to detect visibility (the camera can be set to 30fps). The camera can be used to obtain the type and size of vehicles in adjacent lanes and their speed difference with the vehicle's own speed to indirectly estimate the crosswind intensity. Road condition data can be acquired through multiple sensors. For example, a road surface friction coefficient sensor can be used to detect the road surface friction coefficient, and a camera can be used to monitor road type (such as asphalt, cement, or gravel), road cover (such as water or snow), road smoothness, road obstacles, and road traffic participants (such as pedestrians or vehicles). Additionally, millimeter-wave radar and lidar can be used to supplement the detection of road smoothness, road obstacles, and road participants, thus compensating for the limitations of the camera. Millimeter-wave radar can be set to 10Hz, and lidar can be set to 20Hz. Vehicle status data can be acquired through multiple sensors. For example, vehicle speed, wheel speed, longitudinal acceleration, lateral acceleration, and actual yaw rate can be calculated using an inertial measurement unit (IMU) and wheel speed sensors. A 100Hz IMU can be used. Tire steering angle can be calculated using a steering wheel sensor. Driving task data can be acquired through the vehicle bus. For example, a planning and control module can provide task data related to other vehicles (such as following, overtaking, obstacle avoidance, etc.), and a map navigation module can provide road-related task data for the entire driving journey (such as cruising, merging, or splitting). The specific limitations of this embodiment are determined based on the detection principles or device types of various sensors. (Refer to...) Figure 3 The diagram illustrates an example of vehicle operation scenario data acquisition and control according to this application. Vehicle operation scenario data can be acquired through sensors such as LiDAR, millimeter-wave radar, cameras, road friction coefficient sensors, and inertial measurement units. Subsequently, the autonomous driving controller, as the core brain, processes this vehicle operation scenario data and combines it with the map navigation module for positioning, path planning, etc. Finally, by controlling the vehicle controller, and through the coordinated execution of the left front wheel motor controller, right front wheel motor controller, left rear wheel motor controller, and right rear wheel motor controller, the four-wheel drive force distribution coefficient can be optimized in real time, thereby completing the entire autonomous driving task.

[0035] Step 202: Classify the vehicle operation scenario data to determine the current scenario type; When a change is detected in the vehicle operation scenario data, the system can classify the scenario in real time based on the data to determine the current scenario type. The current scenario type characterizes the overall driving environment state of the vehicle. For example, the vehicle operation scenario data can be divided into 15 categories, such as "high-speed cruising - dry asphalt road surface," "urban following - wet and slippery road surface," and "rural obstacle avoidance - gravel road surface." A machine learning model can be pre-trained using multi-dimensional vehicle operation scenario data. When the vehicle operation scenario data changes, the model can directly output the corresponding current scenario type based on the input vehicle operation scenario data. The machine learning model can employ a fusion model based on CNN and LSTM, where the CNN module extracts spatial features and the LSTM module captures temporal features. Other suitable model architectures, such as transformer models and deep neural networks, can also be used. Those skilled in the art can also use other models according to the actual situation; this embodiment of the invention does not limit the specific model. The specific model pre-training method can be determined by those skilled in the art based on their needs; this embodiment of the invention does not specifically limit this method.

[0036] Specifically, the vehicle operation scenario data includes road condition data and vehicle status data, and the safety constraint parameters include tire grip torque and vehicle stability threshold.

[0037] The vehicle operation scenario data can include road condition data and vehicle status data. Road condition data is used to characterize the physical characteristics of the interface between the vehicle and the road surface, and vehicle status data is used to characterize the real-time motion state of the vehicle. Safety constraint parameters can include tire grip torque and vehicle stability threshold. Tire grip torque is used to characterize the maximum driving torque that can be transmitted between the tire and the road surface, and vehicle stability threshold is used to characterize the torque distribution limit for maintaining the yaw stability of the vehicle.

[0038] Step 203: Determine the tire vertical load based on the vehicle status data; When determining tire grip torque, you can first determine the tire vertical load related to tire grip torque, which can be determined based on vehicle condition data.

[0039] In an optional embodiment of this application, the vehicle state data includes longitudinal acceleration and lateral acceleration, and the step of determining the tire vertical load based on the vehicle state data includes: Sub-step S11: Based on a preset vehicle dynamics model, the longitudinal acceleration and lateral acceleration are converted into tire vertical loads.

[0040] Specifically, longitudinal and lateral acceleration can be detected by inertial measurement units. At the same time, combined with known parameters such as vehicle mass, center of gravity position, wheelbase and track width, a vehicle dynamics model can be established. The vertical load of the four tires can be calculated by using the longitudinal and lateral accelerations acquired in real time.

[0041] This application embodiment uses a preset vehicle dynamics model to convert real-time collected longitudinal and lateral accelerations into vertical tire loads for each tire. By determining the vertical tire loads for each tire in real time, the driving force distribution of each tire can be optimized based on the actual vertical tire load distribution, ensuring driving stability while fully utilizing the tire's adhesion.

[0042] Step 204: Determine the tire grip torque based on the road condition data and the tire vertical load; When determining tire grip torque, you can first determine the road condition data related to tire grip torque, and then determine the tire grip torque based on the road condition data and the tire vertical load.

[0043] In an optional embodiment of this application, the road condition data includes the road surface friction coefficient, and the step of determining the tire grip torque based on the road condition data and the tire vertical load includes: Sub-step S21: Determine the product of the road surface friction coefficient, the tire vertical load, and the preset tire rolling radius as the tire grip torque.

[0044] In order to obtain the tire grip torque, the real-time road friction coefficient, the tire vertical load, and the preset tire rolling radius can be multiplied to calculate the grip torque of each tire.

[0045] This embodiment calculates the tire grip torque by multiplying the real-time road friction coefficient, the calculated tire vertical load, and the preset tire rolling radius. The tire grip torque can determine the maximum adhesion capacity of each tire under the current working conditions, which effectively prevents tire slippage and vehicle instability, makes full use of the road adhesion conditions, and ensures driving safety.

[0046] Step 205: Determine the vehicle stability threshold based on the vehicle status data; To obtain safety constraint parameters, the vehicle stability thresholds associated with the safety constraint parameters can be determined first, and then the safety constraint boundaries can be determined based on the vehicle stability thresholds.

[0047] In this embodiment, the vertical load of the tires is first determined by vehicle state data and then converted into a vertical load. Secondly, based on this vertical load, real-time road condition data (such as the road surface friction coefficient), and a preset tire rolling radius, the tire grip torque of each tire is calculated, thereby determining the adhesion capability boundary of each tire. Simultaneously, a vehicle stability threshold is determined based on the vehicle state data. Based on the tire grip torque and the vehicle stability threshold, a complete safety constraint system from a single tire to the entire vehicle is established, ensuring that the real-time adhesion capability of each tire is fully utilized while maintaining the vehicle's overall stability range, thus achieving an optimal balance between safety and power.

[0048] In an optional embodiment of this application, the vehicle state data includes vehicle speed, and the step of determining the vehicle stability threshold based on the vehicle state data includes: Sub-step S31: If the vehicle speed is less than the first speed threshold, determine the vehicle stability threshold as the first stability threshold. In practical applications, vehicle stability thresholds can be divided into different thresholds for different vehicle speeds, establishing a correlation between vehicle speed and stability thresholds. At low speeds, the vehicle can withstand a larger torque difference between the front and rear axles without becoming unstable, thus a larger first stability threshold can be used. Therefore, when the real-time vehicle speed is detected to be lower than the preset first speed threshold, the vehicle stability threshold is set to a relatively large first stability threshold.

[0049] Sub-step S32: If the vehicle speed is not less than the first speed threshold, determine the vehicle stability threshold as the second stability threshold. The first stability threshold is less than the second stability threshold.

[0050] At high speeds, vehicles are prone to instability due to large torque differences between the front and rear axles, thus requiring a smaller second stability threshold. When the real-time vehicle speed is detected to be no lower than a preset first speed threshold, the vehicle stability threshold is set to a relatively small second stability threshold. The first stability threshold is greater than the second stability threshold.

[0051] In this embodiment, when the vehicle speed is less than a first speed threshold, a larger first stability threshold is determined; when the vehicle speed is not less than the first speed threshold, a smaller second stability threshold is determined. By adjusting the vehicle stability threshold according to the vehicle speed, the driving potential under low-speed conditions is fully released while ensuring high-speed driving safety, enabling the vehicle to achieve optimal overall performance across different speed ranges. This speed-based tiered threshold control resolves the contradiction between the inability of traditional single thresholds to simultaneously address the power performance at low speeds and the stability at high speeds.

[0052] Step 206: Determine a multi-objective optimization function based on the current scenario type and the vehicle operation scenario data; Based on the current scenario type and vehicle operation scenario data, when the vehicle needs to achieve optimization among stability, handling and energy consumption, a multi-objective optimization function is constructed.

[0053] In an optional embodiment of this application, the step of determining the multi-objective optimization function based on the current scene type and the vehicle operation scene data includes: Sub-step S41: Based on preset weight configuration rules, the current scenario type is converted into stability weight, control weight, and energy consumption weight. Based on vehicle dynamics principles, the relative importance of various performance objectives differs depending on the vehicle's operating conditions. Building upon this understanding, a mapping relationship can be established between scenario type and stability weight, handling weight, and energy consumption weight—that is, a preset weight configuration rule. For example, the weights for the "high-speed cruising - dry road" scenario can be set as stability weight 0.3, handling weight 0.3, and energy consumption weight 0.4 to reflect the emphasis on energy consumption in this scenario; similarly, the weights for the "rain, snow, slippery - obstacle avoidance" scenario can be set as stability weight 0.5, handling weight 0.4, and energy consumption weight 0.1 to highlight the consideration of stability in this scenario. Those skilled in the art can also use other coefficients according to actual conditions; this embodiment of the invention does not limit the specific coefficient settings. Specific coefficient settings can be determined by those skilled in the art based on their needs; this embodiment of the invention does not specifically limit them.

[0054] Sub-step S42: Determine the stability control coefficient, handling control coefficient, and energy consumption control coefficient based on vehicle operation scenario data; After acquiring vehicle operating scenario data, stability control coefficients, handling control coefficients, and energy consumption control coefficients can be determined based on this data, enabling a multi-dimensional assessment of the vehicle's operating status. Specifically, the stability control coefficient characterizes the vehicle's ability to maintain its driving posture, the handling control coefficient characterizes the vehicle's agility in responding to commands, and the energy consumption control coefficient characterizes the energy utilization efficiency of the powertrain system.

[0055] In an optional embodiment of this application, the vehicle state data includes actual yaw rate, ideal yaw rate, and wheel speed; the vehicle operating scenario data includes real-time current and real-time voltage corresponding to the drive motor; and the step of determining the stability control coefficient, handling control coefficient, and energy consumption control coefficient based on the vehicle operating scenario data includes: Sub-step S51: Determine the difference between the actual yaw rate and the ideal yaw rate as the vehicle yaw rate deviation; After acquiring the actual yaw rate from the inertial measurement unit (IMU), the theoretical ideal yaw rate is simultaneously calculated based on the vehicle dynamics model. This calculation is based on steering angle and vehicle speed parameters. The difference between the two is taken as the vehicle yaw rate deviation. In specific scenarios, the vehicle yaw rate deviation needs to be controlled within a certain range to ensure vehicle safety. For example, in scenarios such as wet / slippery roads or high-speed curves, the vehicle yaw rate deviation should be controlled within 5%. The vehicle yaw rate deviation characterizes the degree of deviation between the actual yaw motion and the ideal yaw motion.

[0056] Sub-step S52: Determine the product of the wheel speed and the preset tire rolling radius as the tire circumferential speed; The wheel speed signal from the wheel speed sensor is acquired, and the wheel speed is multiplied by the preset tire rolling radius to calculate the tire circumferential speed. Wheel speed represents the rotational angular velocity of the wheel, tire rolling radius represents the working radius of the tire under load, and tire circumferential speed represents the linear velocity of the tire contact point with the ground.

[0057] Sub-step S53: Determine the difference between the vehicle speed and the tire circumferential speed as the first difference value; After obtaining the actual vehicle speed, the first difference can be obtained by subtracting the vehicle speed from the tire circumferential speed. This first difference is used to calculate the wheel slip ratio.

[0058] Sub-step S54: Determine the quotient of the first difference and the vehicle speed as the wheel speed slip ratio; After obtaining the first difference, the wheel slip ratio can be calculated by quotienting the first difference and the vehicle speed. Dividing the calculated first difference by the real-time vehicle speed yields the wheel slip ratio. The wheel slip ratio characterizes the degree of relative slip between the tire and the road surface. For example, a wheel slip ratio of 0 indicates pure rolling between the tire and the road surface. A positive wheel slip ratio indicates that the drive wheel speed is higher than the vehicle's forward speed, indicating drive slip. A negative wheel slip ratio indicates that the drive wheel speed is lower than the vehicle's forward speed, indicating braking slip. Therefore, the drive force distribution can be adjusted promptly based on the wheel slip ratio to prevent vehicle instability.

[0059] Sub-step S55: Based on a preset first weighting coefficient, the vehicle yaw rate deviation and the wheel speed slip ratio are weighted and summed to obtain the stability control coefficient; In practical applications, preset first weighting coefficients can be configured. For example, the vehicle yaw rate deviation sub-weighting coefficient for vehicle yaw rate deviation can be set to 0.6, and the wheel slip rate sub-weighting coefficient for wheel slip ratio can be set to 0.4. The vehicle yaw rate deviation is multiplied by the vehicle yaw rate deviation sub-weighting coefficient, and the wheel slip ratio is multiplied by the wheel slip ratio sub-weighting coefficient. The two products are then added together to obtain the stability control coefficient. The smaller the value, the more stable the vehicle.

[0060] Sub-step S56: Based on the preset second weighting coefficient, the preset vehicle steering response speed and the preset torque response speed are weighted and summed to obtain the handling control coefficient. Specifically, a preset second weighting coefficient can be configured. For example, the vehicle steering response speed weighting coefficient for the preset vehicle steering response speed can be set to 0.4 and the torque response speed weighting coefficient for the preset torque response speed can be set to 0.6. The preset vehicle steering response speed and preset torque response speed are multiplied by the corresponding weighting coefficients, and then the two products are summed to obtain the handling control coefficient. The larger the value, the more sensitive the handling.

[0061] Sub-step S57: Determine the product of the real-time current and real-time voltage corresponding to the drive motor as the real-time power corresponding to the drive motor; By acquiring real-time current and voltage data for the drive motors of each drive wheel using sensors—for example, real-time current is obtained through a current sensor built into the motor controller, and real-time voltage is obtained through a voltage sensor—the instantaneous energy consumption state of each drive motor can be determined through real-time calculation of its power. This allows for the determination of a drive force distribution strategy based on actual energy consumption, improving energy efficiency while maintaining vehicle performance.

[0062] In sub-step S58, the real-time power of the multiple drive motors is summed to obtain the energy consumption control coefficient.

[0063] After determining the real-time power of multiple drive motors, namely the four drive wheels (front left, front right, rear left, and rear right), the energy consumption control coefficient can be obtained by summing the real-time power of multiple drive motors. The smaller the value, the lower the energy consumption.

[0064] This application embodiment calculates and weights the vehicle's yaw rate deviation and wheel slip ratio to obtain a stability control coefficient, thereby achieving a quantitative assessment of the vehicle's lateral and longitudinal stability. By weighting and summing a preset vehicle steering response speed and a preset torque response speed to obtain a handling control coefficient, it reflects the vehicle's responsiveness to driving commands. Simultaneously, by calculating the sum of the power of each drive motor in real time to obtain an energy consumption control coefficient, it reflects the instantaneous energy consumption level of the entire vehicle's drive system. This allows for real-time optimization of the drive force distribution strategy while ensuring vehicle stability, safety, and handling agility.

[0065] Sub-step S43: Determine the product of the stability weight and the stability control coefficient as the stability characteristic value; Based on the current scenario type, the corresponding stability weight can be obtained from the preset weight configuration rule library, and then multiplied with the calculated stability control coefficient to obtain the stability feature value.

[0066] Sub-step S44: Determine the product of the control weight and the control coefficient as the control characteristic value; In practical applications, the corresponding control weight can be obtained from the preset weight configuration rule library based on the current scenario type, and multiplied by the calculated control coefficient to obtain the control feature value.

[0067] Sub-step S45: Determine the product of the energy consumption weight and the energy consumption control coefficient as the energy consumption characteristic value; Based on the current scenario type, the corresponding energy consumption weight can be obtained from the preset weight configuration rule library, and multiplied by the calculated energy consumption control coefficient to obtain the energy consumption feature value.

[0068] Sub-step S46: Sum the stability feature value, the control feature value and the energy consumption feature value to construct a multi-objective optimization function.

[0069] The calculated stability eigenvalues, control eigenvalues, and energy consumption eigenvalues ​​are algebraically summed to construct a multi-objective optimization function.

[0070] (1) in These are stability eigenvalues; To manipulate feature values; This represents the energy consumption characteristic value. For stability weights; This is the stability control coefficient; To manipulate weights; For control and maneuvering coefficients; Energy consumption weighting; This is the energy consumption control coefficient.

[0071] By summing the three characteristic values—stability, handling, and energy consumption—a multi-objective optimization function that comprehensively reflects the vehicle's stability, handling, and energy consumption performance can be constructed.

[0072] This application's embodiments determine the relative importance of each performance objective under different driving conditions based on stability weights, handling weights, and energy consumption weights obtained from scenario type transformation. Subsequently, stability control coefficients, handling control coefficients, and energy consumption control coefficients calculated based on real-time vehicle operation data determine the vehicle's current actual operating state. By multiplying the corresponding weight coefficients with the control coefficients, stability characteristic values, handling characteristic values, and energy consumption characteristic values ​​are obtained. Finally, a comprehensive evaluation system is established by constructing a multi-objective optimization function through the summation of the three characteristic values. This enables the driving force distribution strategy to adjust handling performance and energy consumption according to actual scenario requirements while ensuring safety and stability, thereby improving the vehicle's overall performance under different driving environments. It achieves balanced optimization of various vehicle performance aspects under different scenarios, ensuring that the driving force distribution scheme meets both safety and handling requirements while also considering energy economy.

[0073] Step 207: Solve the multi-objective optimization function based on the safety constraint parameters and the hardware constraint parameters to obtain the driving force allocation coefficients; Under the premise of satisfying the safety constraint parameters and hardware constraint parameters, a set of candidate driving force allocation coefficients is determined, and the driving force allocation coefficients can be obtained by solving the multi-objective optimization function based on the set of candidate driving force allocation coefficients.

[0074] This application first transforms the driving environment requirements into stability weight, handling weight, and energy consumption weight based on the current scenario type. Simultaneously, it calculates the stability control coefficient, handling control coefficient, and energy consumption control coefficient based on real-time vehicle operation scenario data. The two coefficients are multiplied to obtain their respective eigenvalues ​​and summed to construct a multi-objective optimization function. Next, within the candidate driving force allocation coefficient set determined by safety constraint parameters and hardware constraint parameters, the multi-objective optimization function is solved to finally obtain the driving force allocation coefficient that can achieve the best balance between stability, handling, and energy consumption in the current scenario, thereby improving the overall performance of the vehicle under different driving environments.

[0075] In an optional embodiment of this application, the hardware constraint parameters include the rated torque of the drive motor and a preset upper limit for the torque change rate. The step of solving the multi-objective optimization function based on the safety constraint parameters and the hardware constraint parameters to obtain the driving force distribution coefficient includes: Sub-step S61: Determine the vehicle stability threshold as the boundary of the torque difference between the front and rear axles; In practical applications, the vehicle stability threshold can be determined as the boundary of the torque difference between the front and rear axles, thus providing a clear stability constraint. This ensures that, under any driving force distribution strategy, the torque distribution difference between the front and rear axles will not exceed the vehicle stability threshold that maintains vehicle stability, effectively preventing vehicle instability caused by improper inter-axle torque distribution.

[0076] Sub-step S62: Determine the smaller value between the tire grip torque and the drive motor rated torque as the torque boundary; Specifically, the real-time calculated tire grip torque is compared with the rated torque of the drive motor, and the smaller value is taken as the torque boundary of the drive wheel. On the one hand, this can prevent the drive torque from exceeding the adhesion limit between the tire and the road surface, thus avoiding wheel slippage; on the other hand, it can prevent the drive torque from exceeding the continuous working capacity of the motor, thus avoiding motor overload damage.

[0077] Sub-step S63: Combine the front and rear axle torque difference boundary, the preset upper limit of torque change rate, and the torque boundary to determine the candidate drive force distribution coefficient set; After determining the front and rear axle torque difference boundary, the preset upper limit of torque change rate, and the torque boundary, a set of candidate drive force distribution coefficients that satisfy the basic torque distribution constraints can be generated. Simultaneously, it is ensured that the sum of the drive force distribution coefficients in the candidate set is 1. Furthermore, to avoid motor overload, a preset upper limit of torque change rate needs to be set. For example, the preset upper limit of torque change rate can be 50N. m / ms. Those skilled in the art can determine the set of candidate driving force allocation coefficients based on the actual situation. The embodiments of the present invention do not limit the specific method for determining the set of candidate driving force allocation coefficients.

[0078] Sub-step S64: Solve the multi-objective optimization function based on the candidate driving force allocation coefficient set to obtain the driving force allocation coefficients.

[0079] After determining the set of candidate driving force allocation coefficients, the corresponding multi-objective optimization function value is calculated. The candidate solution that makes the multi-objective optimization function value optimal is selected as the final driving force allocation coefficient. The calculation accuracy of the driving force allocation coefficient is retained to three decimal places to ensure the accuracy of torque allocation.

[0080] For example, the optimal driving force distribution coefficients can be solved using a particle swarm optimization algorithm. The torque distribution coefficients of the four driving wheels (front left, front right, rear left, and rear right) are used as optimization variables to construct the particle position vector. The optimization objective is to maximize a multi-objective optimization function, with safety and hardware constraints as boundary conditions. An initial solution satisfying the constraints is generated by initializing the particle swarm. During the iteration process, the fitness value of each particle is evaluated, and the individual optimal and global optimal positions are updated. The number of particles can be set to 50. When the maximum number of iterations (e.g., 30) is reached or the fitness converges, the convergence accuracy can be set to 10. -4 The globally optimal solution is output as the final driving force allocation coefficient. It should be noted that those skilled in the art can also use other algorithms according to actual circumstances; this embodiment of the invention does not limit the specific algorithm. The specific algorithm can be determined based on the needs of those skilled in the art, and this embodiment of the invention does not specifically limit it.

[0081] This application first defines the vehicle stability threshold as the front-to-rear axle torque difference boundary, ensuring that the drive force distribution does not cause vehicle instability. Second, it uses the smaller value between the tire grip torque and the drive motor rated torque as the torque boundary, avoiding adhesion failure caused by tire slippage and preventing motor overload damage, thereby improving system safety and hardware durability. Then, it combines the front-to-rear axle torque difference boundary, the preset torque change rate upper limit, and the torque boundary to determine a set of candidate drive force distribution coefficients, generating a set of all candidate drive force distribution coefficients that satisfy the constraints, providing a data foundation for optimization. Finally, it solves the multi-objective optimization function based on this set of candidate drive force distribution coefficients, ensuring that the optimal drive force distribution coefficient balancing stability, handling, and energy consumption is found within a safe range, improving the vehicle's overall performance in complex driving environments.

[0082] Step 208: Based on the driving force distribution coefficient, control the drive motor to output torque corresponding to the driving force distribution coefficient.

[0083] After determining the driving force distribution coefficient, the driving force distribution coefficient is sent to the vehicle controller. After receiving the driving force distribution coefficient, the vehicle controller controls each drive motor to output the corresponding torque to achieve precise distribution of driving force. At the same time, it is necessary to continuously monitor the vehicle operation scenario data and update the data every 10ms. In addition, if the scenario changes, the driving force distribution coefficient is updated again to ensure that the four-wheel drive vehicle has the best driving performance in all scenarios.

[0084] In this embodiment, when the vehicle is in driving condition, vehicle operation scenario data is acquired, the vehicle operation scenario data is classified into scenarios to determine the current scenario type, the tire vertical load is determined based on the vehicle status data, the tire grip torque is determined based on the road condition data and the tire vertical load, the vehicle stability threshold is determined based on the vehicle status data, a multi-objective optimization function is determined based on the current scenario type and the vehicle operation scenario data, the multi-objective optimization function is solved based on the safety constraint parameters and the hardware constraint parameters to obtain the driving force distribution coefficient, and the drive motor is controlled to output torque corresponding to the driving force distribution coefficient based on the driving force distribution coefficient, so that the vehicle can maintain the best balance between stability, handling and energy consumption under various complex driving scenarios.

[0085] Reference Figure 4 The diagram illustrates the driving force allocation process of this application. After the autonomous driving system is activated, it collects data through sensors and continuously updates vehicle operation scenario data. Based on this data, the autonomous driving system classifies the current driving scenario. The autonomous driving controller constructs a multi-objective optimization function and determines stability weights, handling weights, energy consumption weights, and safety constraint parameters using the vehicle operation scenario data. Based on the inherent hardware constraint parameters, the multi-objective optimization function is solved to obtain the driving force allocation coefficients, which are then directly sent to the four motor controllers (left front, right front, left rear, and right rear) for driving force allocation. This cycle continues until the autonomous driving system instructs the system to stop driving force allocation optimization.

[0086] Figure 5 This diagram illustrates a structural block diagram of an embodiment of a vehicle drive force distribution device according to this application. The vehicle has multiple drive motors, each drive motor corresponding to hardware constraint parameters. The vehicle drive force distribution device may specifically include the following modules: The data acquisition module 501 is used to acquire vehicle operation scenario data when the vehicle is in driving condition; The classification module 502 is used to classify the vehicle operation scenario data and determine the current scenario type; The first determining module 503 is used to determine the safety constraint parameters corresponding to the vehicle operation scenario data; The second determining module 504 is used to determine the driving force allocation coefficient by combining the current scene type, the safety constraint parameters and the hardware constraint parameters; The control module 505 is used to control the drive motor to output torque corresponding to the drive force distribution coefficient according to the drive force distribution coefficient.

[0087] In an optional embodiment of this application, the first determining module 503 includes: The first determining submodule is used to determine the tire vertical load based on the vehicle status data; The second determining submodule is used to determine the tire grip torque based on the road condition data and the tire vertical load; The third determining submodule is used to determine the vehicle stability threshold based on the vehicle status data.

[0088] In an optional embodiment of this application, the first determining submodule includes: The first conversion unit is used to convert the longitudinal acceleration and lateral acceleration into tire vertical loads based on a preset vehicle dynamics model.

[0089] In an optional embodiment of this application, the second determining submodule includes: The first determining unit is used to determine the product of the road surface friction coefficient, the tire vertical load, and the preset tire rolling radius as the tire grip torque.

[0090] In an optional embodiment of this application, the third determining submodule includes: The second determining unit is used to determine the vehicle stability threshold as the first stability threshold when the vehicle speed is less than the first speed threshold. The third determining unit is used to determine the vehicle stability threshold as the second stability threshold when the vehicle speed is not less than the first speed threshold.

[0091] In an optional embodiment of this application, the second determining module 504 includes: The third determining submodule is used to determine a multi-objective optimization function based on the current scenario type and the vehicle operation scenario data; The first solution module is used to solve the multi-objective optimization function based on the safety constraint parameters and the hardware constraint parameters to obtain the driving force allocation coefficients.

[0092] In an optional embodiment of this application, the third determining submodule includes: The second conversion unit is used to convert the current scene type into stability weight, control weight and energy consumption weight based on preset weight configuration rules. The fourth determining unit is used to determine the stability control coefficient, handling control coefficient, and energy consumption control coefficient based on vehicle operation scenario data. The fifth determining unit is used to determine that the product of the stability weight and the stability control coefficient is the stability characteristic value; The sixth determining unit is used to determine the product of the control weight and the control coefficient as the control characteristic value; The seventh determining unit is used to determine the product of the energy consumption weight and the energy consumption control coefficient as the energy consumption characteristic value; The first construction unit is used to sum the stability feature value, the control feature value and the energy consumption feature value to construct a multi-objective optimization function.

[0093] In an optional embodiment of this application, the fourth determining unit includes: The twelfth determining unit is used to determine the sixth quotient based on the measured high temperature and the temperature limit value; The thirteenth determining unit is used to determine the product of the sixth quotient and the temperature weighting coefficient as a temperature hazard item.

[0094] In an optional embodiment of this application, the seventh determining submodule includes: The first calculation unit is used to determine the difference between the actual yaw rate and the ideal yaw rate as the vehicle yaw rate deviation. The second calculation unit is used to determine the product of the wheel speed and the preset tire rolling radius as the tire circumferential speed; The third calculation unit is used to determine the difference between the vehicle speed and the tire circumferential speed as the first difference value; The fourth calculation unit is used to determine the quotient of the first difference and the vehicle speed as the wheel speed slip ratio; The fifth calculation unit is used to perform a weighted summation of the vehicle yaw rate deviation and the wheel speed slip ratio based on a preset first weighting coefficient to obtain the stability control coefficient. The sixth calculation unit is used to calculate the weighted sum of the preset vehicle steering response speed and the preset torque response speed based on the preset second weighting coefficient to obtain the handling control coefficient; The seventh calculation unit is used to determine the product of the real-time current and real-time voltage corresponding to the drive motor as the real-time power corresponding to the drive motor; The eighth calculation unit is used to sum the real-time power of the multiple drive motors to obtain the energy consumption control coefficient.

[0095] In an optional embodiment of this application, the first solving module includes: The first solution submodule is used to determine the vehicle stability threshold as the boundary of the torque difference between the front and rear axles; The second solution submodule is used to determine the smaller value of the tire grip torque and the rated torque of the drive motor as the torque boundary. The third solution submodule is used to determine the set of candidate driving force distribution coefficients by combining the front and rear axle torque difference boundary, the preset torque change rate upper limit and the torque boundary; The fourth solution submodule is used to solve the multi-objective optimization function based on the candidate driving force allocation coefficient set to obtain the driving force allocation coefficients.

[0096] Figure 6 This application shows a structural block diagram of a vehicle according to an embodiment of the present application. This application also provides a vehicle comprising: The processor 601 and the storage medium 602 store a computer program executable by the processor 601, which executes the computer program to implement the vehicle drive force distribution method as described in any of the embodiments of this application.

[0097] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0098] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0099] Reference Figure 7 This diagram illustrates a structural block diagram of a storage medium provided in an embodiment of this application. This application also provides a computer-readable storage medium 701, on which a computer program is stored. When the computer program is run by a processor, it executes the vehicle drive force distribution method as described in any one of the embodiments of this application.

[0100] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0101] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0102] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0103] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0105] In this application, "multiple" refers to two or more.

[0106] The terms “first,” “second,” “third,” “fourth,” etc., in this application (if present) are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0107] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0108] Unless otherwise specified, all steps in this application may be performed sequentially or randomly. For example, if the method includes steps A and B, it means that the method may include steps A and B performed sequentially, or it may include steps B and A performed sequentially. For example, if the method may also include step C, it means that step C may be added to the method in any order. For example, the method may include steps A, B, and C, or it may include steps A, C, and B, or it may include steps C, A, and B, etc.

[0109] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for distributing driving force in a vehicle, characterized in that, The vehicle has multiple drive motors, each drive motor corresponding to hardware constraint parameters, and the method includes: Acquire vehicle operation scenario data while the vehicle is in operation; The vehicle operation scenario data is classified to determine the current scenario type; Determine the safety constraint parameters corresponding to the vehicle operation scenario data; Based on the current scenario type, the safety constraint parameters, and the hardware constraint parameters, determine the driving force allocation coefficient; Based on the driving force distribution coefficient, the drive motor is controlled to output torque corresponding to the driving force distribution coefficient.

2. The method according to claim 1, characterized in that, The vehicle operation scenario data includes road condition data and vehicle status data, and the safety constraint parameters include tire grip torque and vehicle stability threshold. The step of determining the safety constraint parameters corresponding to the vehicle operation scenario data includes: The tire vertical load is determined based on the vehicle status data; The tire grip torque is determined based on the road condition data and the tire vertical load. The vehicle stability threshold is determined based on the vehicle status data.

3. The method according to claim 2, characterized in that, The vehicle state data includes longitudinal acceleration and lateral acceleration, and the step of determining the tire vertical load based on the vehicle state data includes: Based on a preset vehicle dynamics model, the longitudinal acceleration and lateral acceleration are converted into tire vertical loads.

4. The method according to claim 2, characterized in that, The road condition data includes the road surface friction coefficient, and the step of determining the tire grip torque based on the road condition data and the tire vertical load includes: The product of the road surface friction coefficient, the tire vertical load, and the preset tire rolling radius is determined as the tire grip torque.

5. The method according to claim 2, characterized in that, The vehicle status data includes vehicle speed, and the step of determining the vehicle stability threshold based on the vehicle status data includes: When the vehicle speed is less than the first speed threshold, the vehicle stability threshold is determined to be the first stability threshold; If the vehicle speed is not less than the first speed threshold, the vehicle stability threshold is determined to be the second stability threshold; the first stability threshold is greater than the second stability threshold.

6. The method according to claim 5, characterized in that, The step of determining the driving force allocation coefficient by combining the current scenario type, the safety constraint parameters, and the hardware constraint parameters includes: Based on the current scenario type and the vehicle operation scenario data, determine the multi-objective optimization function; The multi-objective optimization function is solved based on the safety constraint parameters and the hardware constraint parameters to obtain the driving force allocation coefficients.

7. The method according to claim 6, characterized in that, The step of determining the multi-objective optimization function based on the current scenario type and the vehicle operation scenario data includes: Based on preset weight configuration rules, the current scenario type is converted into stability weight, control weight, and energy consumption weight. The stability control coefficient, handling control coefficient, and energy consumption control coefficient are determined based on vehicle operation scenario data. The product of the stability weight and the stability control coefficient is determined as the stability characteristic value; The product of the control weight and the control coefficient is determined as the control characteristic value; The product of the energy consumption weight and the energy consumption control coefficient is determined as the energy consumption characteristic value; A multi-objective optimization function is constructed by summing the stability feature value, the control feature value, and the energy consumption feature value.

8. The method according to claim 7, characterized in that, The vehicle status data includes actual yaw rate, ideal yaw rate, and wheel speed; the vehicle operating scenario data includes real-time current and real-time voltage corresponding to the drive motor; the step of determining the stability control coefficient, handling control coefficient, and energy consumption control coefficient based on the vehicle operating scenario data includes: The difference between the actual yaw rate and the ideal yaw rate is defined as the vehicle yaw rate deviation. The product of the wheel speed and the preset tire rolling radius is determined to be the tire circumferential speed; The difference between the vehicle speed and the tire circumferential speed is determined as the first difference value; The quotient of the first difference and the vehicle speed is determined to be the wheel slip ratio; Based on a preset first weighting coefficient, the stability control coefficient is obtained by weighted summation of the vehicle yaw rate deviation and the wheel slip ratio. Based on the preset second weighting coefficient, the preset vehicle steering response speed and preset torque response speed are weighted and summed to obtain the handling control coefficient; The product of the real-time current and real-time voltage of the drive motor is determined as the real-time power of the drive motor. The energy consumption control coefficient is obtained by summing the real-time power of the multiple drive motors.

9. The method according to claim 6, characterized in that, The hardware constraint parameters include the rated torque of the drive motor and a preset upper limit for the torque change rate. The step of solving the multi-objective optimization function based on the safety constraint parameters and the hardware constraint parameters to obtain the driving force distribution coefficient includes: The vehicle stability threshold is determined as the boundary of the torque difference between the front and rear axles; The smaller of the tire grip torque and the drive motor rated torque is determined as the torque boundary. By combining the front and rear axle torque difference boundary, the preset torque change rate upper limit, and the torque boundary, a set of candidate driving force distribution coefficients is determined. The multi-objective optimization function is solved based on the set of candidate driving force allocation coefficients to obtain the driving force allocation coefficients.

10. A vehicle, characterized in that, Including processor and memory, among which The memory is used to store computer programs; The processor is used to execute the program stored in the memory to implement the vehicle driving force distribution method according to any one of claims 1-9.

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