A method and system for four-wheel differential torque-enhanced steering control of distributed electric drive vehicles

Abstract

This invention proposes a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system, belonging to the field of artificial intelligence risk prediction technology. It includes an upper-level torque decision module, a lower-level torque coordination module, and onboard sensors. Based on the actual driving scenario, a stationary steering assist strategy is selected, and the system enters the operating condition by pressing a function button. Vehicle dynamic information is acquired through onboard sensors, and combined with static vehicle data, it is determined whether the vehicle is in a stationary steering or small-radius steering state. When the selected function button is for stationary steering or small-radius steering, real-time vehicle dynamic information is acquired, and combined with fuzzy inference and neural network algorithms, the ground adhesion coefficient and target yaw rate are intelligently determined, thereby achieving precise control of the steering torque. Alternatively, a dual-controller structure can be adopted to effectively distribute and adjust the torque of each wheel under complex operating conditions, ensuring flexible handling and stable driving of the vehicle in confined spaces.

Description

Technical Field

[0001] This invention relates to the field of distributed drive vehicle assisted steering technology, and in particular to a four-wheel differential torque enhanced steering control method and system for distributed electric drive vehicles. Background Technology

[0002] Steering ability has always been considered a key indicator for evaluating the performance of new energy vehicles. Among these indicators, the minimum turning radius, a crucial parameter for measuring steering performance, directly reflects a vehicle's agility in navigating winding, narrow roads or avoiding impassable obstacles. The smaller a car's turning radius, the better its maneuverability during steering maneuvers. Currently, most new energy passenger vehicles on the market use the traditional Ackermann steering system, with a minimum turning radius ranging from approximately 4 to 8 meters. This characteristic limits the vehicle's steering agility on complex urban roads.

[0003] To address this challenge, technologies such as stationary steering and compass steering have emerged, significantly optimizing vehicle steering maneuverability. Especially for off-road vehicles performing special missions, these technologies enable them to achieve extremely small-radius turns and even rapid U-turns in narrow areas with numerous obstacles, such as streets, bridgeheads, and obstacle-filled spaces. The aforementioned functions primarily rely on complex mechanical steering architectures or independent hydraulic braking systems to achieve wheel locking and steering. In contrast, in-wheel motor-driven vehicles employ a simpler slip steering principle. Through independent differential steering technology, only appropriate external forces need to be applied to the left and right wheels to create an opposite speed difference, thus completing the steering action. This method eliminates the need for additional complex steering mechanisms, reducing costs and significantly improving vehicle agility and adaptability.

[0004] Existing research on distributed drive torque coordination lacks consideration of vehicle adaptability to off-road and paved road surface adhesion conditions. Relying solely on driver control of the vehicle's state to adjust output torque and steering speed makes it difficult to achieve good matching of torque coordination between the four-wheel hub motors. Furthermore, in various narrow road sections, relying solely on traditional steering strategies, hub coordination, and driver operation makes it difficult for the vehicle to maneuver quickly, reducing its passability on special road conditions. Therefore, it is essential to provide a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system that adaptively distributes the torque of the four-wheel hub motors according to the steering strategy corresponding to steering needs, optimizing the dynamic response of torque coordination among each wheel, and ensuring vehicle stability and maneuverability while achieving steering objectives. Summary of the Invention

[0005] In view of this, the present invention proposes a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system that proposes a wheel hub cooperative control scheme adapted to the ground for restricted working conditions with poor outdoor adhesion.

[0006] The technical solution of this invention is implemented as follows: On one hand, this invention provides a distributed electric drive vehicle four-wheel differential torque enhanced steering control method, wherein the vehicle is equipped with an upper-level torque decision module, a lower-level torque coordination module, and on-board sensors, specifically including the following steps:

[0007] The steering status is determined by combining vehicle sensors with static vehicle data: if it is a stationary turn, the slip ratio of each wheel and the road adhesion coefficient are obtained, and step A is executed; if it is a small radius turn, step B is executed.

[0008] Step A: Based on the road surface adhesion coefficient, determine the desired yaw rate that meets the road surface conditions. c desier The obtained expected yaw rate c desier Perform tracking control and obtain the desired yaw moment. M d The desired yaw moment during steering is determined by the upper-level torque decision module. M d The output; based on the adhesion coefficient and slip ratio. m - l The curve model calculates the optimal slip ratio. l opt The lower-level torque coordination module receives the desired yaw torque. M d The output request is processed, and the output request for the desired yaw moment is allocated according to the torque distribution strategy. The results of the allocation are then processed. T d Output to each motor;

[0009] Step B: Collect the accelerator pedal opening signal and steering wheel angle signal output by the driver through onboard sensors. Based on the accelerator pedal opening and steering wheel angle, determine the longitudinal force to maintain the vehicle's longitudinal movement and the front wheel steering angle. Calculate the target yaw rate and target center of gravity lateral deviation angle that conform to the real-time vehicle state. Further obtain the supplementary yaw moment based on the vehicle's steering state, establish constraints based on the desired state objective function to distribute the supplementary moment, and tune the final torque value of each wheel. T i Based on the final torque value of each wheel T i The motor responds to the requested torque output and completes a small-radius steering maneuver.

[0010] Based on the above technical solutions, the preferred method is to determine the steering state by combining vehicle-mounted sensors with static vehicle data. Specifically, this includes installing an inertial navigation system and an MCU computing unit on the vehicle. The inertial navigation system, MCU computing unit, and vehicle-mounted sensors acquire and calculate vehicle state parameters: the inertial navigation system obtains the vehicle's lateral and longitudinal accelerations and yaw rate, and the vehicle-mounted sensors obtain the wheel speeds.

[0011] The MCU computing unit combines the vehicle's longitudinal and lateral accelerations, wheel speeds, and static parameters of the vehicle body to estimate the real-time slip ratio of each wheel. The slip ratio of each wheel is defined as follows: ;in, l 1. l 2. l 3. l 4 represents the slip ratios of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. V x , V y These are the longitudinal velocity and the lateral velocity, respectively, obtained by integrating the longitudinal acceleration and the lateral acceleration of the vehicle. B The wheelbase of the vehicle; a , b These are the distances from the center of mass to the front and rear axles, respectively. c The yaw rate at the center of mass; oh 1. oh 2. oh 3. oh 4 represents the wheel speed; r The tire's rolling radius;

[0012] Then, based on the slip ratio of each wheel of the vehicle, the vehicle controller establishes inference rules for the slip ratio of each wheel according to fuzzy experience, and estimates the current road adhesion coefficient. m i , ,in To improve the utilization rate of each wheel's adhesion, The attachment confidence level for each wheel.

[0013] Preferably, the decision is based on the desired yaw rate that conforms to the road surface conditions. c desier The specific content is as follows:

[0014] Based on the current road adhesion coefficient, the relationship between longitudinal and lateral forces is defined using an adhesion ellipse fitting method. Combined with the wheel's rotational dynamics equations, the longitudinal force of the tire is constrained. The yaw acceleration at the center of mass, based on the adhesion ellipse and dynamic constraints, is defined as follows:

[0015] ,in , These represent the longitudinal and lateral forces on the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. i =1, 2, 3, 4; This represents the yaw moment of inertia of the entire vehicle. Let be the yaw acceleration at the center of mass;

[0016] The accelerator pedal opening value captured by the sensor and output by the driver is defined as:

[0017] ;in This is the weighting coefficient for the accelerometer pedal opening, with a maximum value of 1; This is the accelerator pedal opening signal; This represents the maximum limit value of the accelerator pedal opening signal. This refers to the free travel of the accelerator pedal opening.

[0018] Based on the yaw acceleration at the center of mass and the accelerator pedal opening, the desired yaw rate conforming to road conditions. c desier Defined as: ,in This represents the settling time per unit step response under different road surface adhesion limits. Let yaw acceleration be the angular acceleration at the center of mass. For the desired yaw rate c desier The derivative of .

[0019] Preferably, the desired yaw moment is obtained. M d The specific content is: based on the obtained expected yaw rate that conforms to the road surface conditions. c desier Based on the BPPID adaptive PID control algorithm based on BP neural network, a yaw rate controller with adaptive tuning parameters is established, and the learning rule is defined as follows:

[0020] ; ;in represent t Adaptive tuning parameters at time t, represent t Adaptive tuning parameters at time -1 It's the learning rate. y It is a set value. This is the current predicted output value. It is the feature quantity of the current input;

[0021] The incremental PID algorithm is defined as follows:

[0022] ;

[0023] ;

[0024] For the current moment k The control output, For the previous moment k -1 control output, This represents the increment between the error at the current time and the error at the previous time. For the current moment k The error, and for k -1 time and k The error at time -2, , and These are the proportional gain parameter, integral gain parameter, and derivative gain parameter; based on the above controller tracking control results, the desired target yaw moment is obtained. M d And send it to the upper-level torque decision module.

[0025] Preferably, when the actual slip ratio is close to the expected optimal slip ratio l opt Due to the existence of errors, a PID control algorithm is used to achieve the desired slip ratio. l opt As a reference input, the output wheel drive torque adjustment amount is used. △T The specific content is as follows:

[0026] Based on the adhesion coefficient and slip ratio under standard ground conditions m - l The curve model, combined with the obtained estimated values ​​of the adhesion coefficients of each wheel, calculates the optimal wheel slip ratio by applying similar weights to the optimal slip ratio on the standard ground. l opt Standard ground similarity is defined as: , , This represents the current adhesion coefficient of the ground. The average adhesion coefficient is calculated for six different standard ground surfaces; the optimal slip ratio is then calculated. l opt Defined as: Among them, the molecules , , , , , The optimal slip ratio under different standard ground similarity conditions; , , , , , For different standard ground similarities; based on the obtained wheel slip ratio estimation results, a PID controller is established, using the optimal slip ratio as the reference input, to obtain the vehicle driving torque adjustment amount. △T And send it to the upper-level torque decision module.

[0027] Preferably, the output request for the desired yaw moment is allocated according to a torque distribution strategy, and the result of the allocation is... T d The specific content output to each motor is as follows: The lower-level torque coordination module receives the target yaw torque request from the upper-level torque decision module, and the upper-level torque decision module outputs the desired yaw torque. M d That is, the desired target yaw moment of the BPPID output based on the average distribution rule for the adaptive PID control algorithm based on BP neural network. M d The allocation will be carried out according to the following rules: Distribute torque to each wheel Output is sent to each motor and executed; the lower-level torque coordination module receives and determines the amount of compensation wheel drive torque adjustment. △T The system receives requests and simultaneously uses real-time vehicle status parameters returned by onboard sensors and the vehicle controller to determine the real-time vehicle steering status according to steering phase judgment rules. Based on the motor's torque command execution, the feedback of the real-time vehicle steering status, and the set torque compensation strategy, the wheel drive torque is adjusted. △T Intervention is provided to compensate and maintain the stability of the vehicle's state during the stationary turning process.

[0028] Preferably, in step B, the process of acquiring the accelerator pedal opening signal and steering wheel angle signal output by the driver through onboard sensors, and determining the longitudinal force to maintain the vehicle's longitudinal movement and the front wheel steering angle based on the accelerator pedal opening and steering wheel angle, specifically involves using onboard sensors to acquire the accelerator pedal opening signal. K 0 and steering wheel angle i The maximum total driving torque is determined based on the vehicle motor's external characteristics and the limitations imposed by high and low speeds on the motor's output torque. ,in n The motor speed is used; based on the linear relationship between the total driving torque and the accelerator pedal, the longitudinal torque that meets the driver's expectations is determined. : The ratio coefficient between the steering wheel angle and the front wheel angle is used to determine the steering wheel angle. i Front wheel steering angle .

[0029] Preferably, the calculation of the target yaw rate and target center of gravity lateral deviation angle that conforms to the real-time vehicle state in step B specifically involves: based on the real-time acquisition of vehicle steering state parameters by the vehicle controller, the MCU calculation unit, based on the real-time vehicle state information acquired by the vehicle controller and combined with the vehicle's static parameters, calculates the target yaw rate that conforms to the real-time vehicle state. Lateral deviation angle of the target's center of gravity They are defined as follows: , Among them, stability factors K Defined as , L Wheelbase a This refers to the front wheelbase. b Rear wheelbase The average steering angle of the front wheels. For the rear axle wheel lateral stiffness, This refers to the lateral stiffness of the front axle wheels.

[0030] Preferably, in step B, the supplementary yaw moment based on the vehicle's steering state is obtained, and constraints based on the desired state objective function are established to distribute the supplementary moment and tune the final torque values ​​of each wheel. T i Specifically, a dual-controller system, consisting of a fuzzy controller and a sliding mode controller, is used to track and tune the target state parameters. For the fuzzy controller, the fuzzy inference rules and corresponding membership functions are tuned based on experimental experience to improve the control output. For the sliding mode controller, a joint control system is designed with yaw rate and lateral deviation angle of the center of gravity as control targets. The error between the target and actual values ​​of these two controllers, and the first derivative of this error, are defined as intermediate variables. , , among which and and These are the actual yaw angular velocity and its first derivative, respectively. and These represent the actual lateral deviation angle of the center of gravity and its first derivative, respectively. The point above the variable represents the first derivative. and These are the error between the actual yaw rate and the target yaw rate, and the first derivative of the error, respectively. and These represent the error between the actual and target center of gravity lateral deviation angles, and the first derivative of the error, respectively. Considering the robustness requirements of the control, a constant velocity approach rate is selected to design the sliding surface. s , ,in k This is a weighting coefficient, with a value range of (0, 1], which is adjusted according to the actual condition of the vehicle. The relative weighting coefficient between the yaw rate error and its reciprocal is set to a value greater than 0. Finally, a Lyapunov function is defined to verify the stability of the joint control system of yaw rate and center of gravity lateral deviation angle. Based on the controller tracking control results described above, the vehicle steering yaw compensation torque required to complete the vehicle steering is obtained. and , To supplement the steering yaw torque output by the fuzzy controller, The steering yaw compensation torque output by the diaphragm controller; based on the obtained vehicle steering yaw compensation torque required for the vehicle to complete steering. and Combined with longitudinal torque that meets the driver's expectations T l Establish a torque distribution strategy, the details of which are as follows: , The steering yaw compensation torque is provided for the outputs of the fuzzy controller and the sliding diaphragm controller, respectively. j =1, 2.

[0031] On the other hand, the present invention also provides a distributed electric drive vehicle four-wheel differential torque enhanced steering control system, including an upper-level torque decision module, a lower-level torque coordination module and on-board sensors configured on the vehicle.

[0032] The vehicle-mounted sensor is used to acquire vehicle dynamic information after the auxiliary function button is pressed, and to determine whether the vehicle is in a stationary turning or small-radius turning state by combining the vehicle static data.

[0033] The upper-level torque decision module, upon confirming that the vehicle is in a stationary turning state, calculates the slip ratio of each wheel and establishes inference rules corresponding to the slip ratio of each wheel based on fuzzy experience derived from experiments to estimate the current road surface adhesion coefficient. m i The peak yaw rate is calculated based on the theory of attached ellipse, and combined with the accelerator pedal opening signal output by the driver, a decision is made on the desired yaw rate that conforms to the road conditions. c desier The BPPID adaptive PID control algorithm based on a BP neural network is used. The proportional, integral, and derivative parameters of the PID control are tuned using the neural network information transmission mode and learning rules, and the desired yaw rate is obtained. c desier Perform tracking control and obtain the desired yaw moment. M d The desired yaw moment during steering is determined by the upper-level torque decision module. M d The output; based on the adhesion coefficient and slip ratio. m - lThe curve model calculates the optimal slip ratio. l opt When the actual slip ratio is different from the expected optimal slip ratio l opt When errors exist, a PID control algorithm is used to achieve the desired slip ratio. l opt As a reference input, the output wheel drive torque adjustment amount is used. △T When the vehicle is confirmed to be in a small-radius turning state, the accelerator pedal opening signal and steering wheel angle signal output by the driver are collected through on-board sensors. Based on the accelerator pedal opening and steering wheel angle, the longitudinal force to maintain the vehicle's longitudinal movement and the front wheel angle are determined. The real-time front wheel angle and longitudinal speed during vehicle overload are obtained. Combined with the vehicle's static parameters, the target yaw rate and target center of gravity lateral deviation angle that conform to the real-time state of the vehicle are calculated. Based on the calculated values ​​of the target yaw rate and target center of gravity lateral deviation angle, a dual controller consisting of a fuzzy controller constructed based on fuzzy inference rules established based on experimental experience and a sliding diaphragm controller based on a sliding diaphragm control algorithm is used to perform weight allocation matching tracking to obtain the supplementary yaw moment based on the vehicle's turning state. According to the supplementary yaw moment, constraints based on the objective function of the desired state are established to allocate the supplementary moment and tune the final value of the torque of each wheel. T i ;

[0034] The lower-level torque coordination module receives the desired yaw torque when the vehicle is in a stationary turning state. M d The output request is processed, and the output request for the desired yaw moment is allocated according to the torque distribution strategy. The results of the allocation are then processed. T d The output is sent to each motor, and the determined compensation wheel drive torque adjustment amount is received. △T Upon receiving the request, the vehicle's real-time steering status is determined based on the vehicle's real-time status parameters returned by onboard sensors and the vehicle controller, according to the steering phase judgment rules. Based on the motor's torque command execution, the feedback on the vehicle's real-time steering status, and the set torque compensation strategy, the wheel drive torque is adjusted. △T Intervention is performed to compensate and maintain the stability of the vehicle's state during the stationary turning process; when the vehicle is in a small-radius turning state, it is based on the final value of the torque of each wheel. T i The motor responds to the requested torque output and completes a small-radius steering maneuver.

[0035] The present invention provides a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system, which has the following advantages compared with the prior art:

[0036] (1) This invention proposes a distributed electric drive vehicle four-wheel differential torque enhanced steering control method. During stationary steering, this method acquires vehicle dynamic information in real time and combines fuzzy inference and neural network algorithms to intelligently determine the ground adhesion coefficient and target yaw rate, thereby achieving precise control of steering torque. In addition, during small-radius steering control, a dual-controller structure is adopted, which can effectively distribute and adjust the torque of each wheel under complex working conditions, ensuring flexible handling and stable driving of the vehicle in confined spaces. Through this innovative control strategy, this invention significantly improves the vehicle's power and passability under various ground conditions, and enhances driving safety and comfort.

[0037] (2) For stationary turning, the vehicle sensors acquire vehicle dynamic information such as longitudinal and lateral speeds, longitudinal and lateral accelerations and wheel speeds, and estimate the current ground adhesion coefficient through fuzzy rules; secondly, the desired yaw rate is determined by combining the accelerator pedal opening with the adhesion ellipse theory, and the optimal slip ratio is used to control the target output driving torque adjustment amount; then, the target yaw rate is tracked by the adaptive PID control algorithm based on BP neural network; finally, based on the real-time parameters of the turning stage, the yaw torque is distributed and the torque adjustment amount is intervened according to the torque distribution strategy and torque supplementation strategy, the wheel speed decreases and gradually stabilizes so that the yaw rate converges to the desired value, thereby improving the consistency of four-wheel turning and maintaining the vehicle working condition stability when turning in place;

[0038] (3) For small-radius steering, the main method is to obtain driver operation signals such as accelerator pedal opening signal and steering wheel angle signal from on-board sensors, and decide the longitudinal force to maintain vehicle movement and the front wheel angle based on the driver's intention; secondly, combine the real-time vehicle status information obtained by the vehicle controller, such as front wheel angle, longitudinal speed and vehicle static parameters, to calculate the target yaw rate and the desired center of gravity lateral deviation angle reflecting the driver's operation; then, use a dual controller based on fuzzy controller and sliding mode controller to track and control the target yaw rate and the desired center of gravity lateral deviation angle and output the vehicle steering supplementary yaw moment respectively; finally, complete the tuning of the torque of each wheel according to the target torque function of each wheel driving force and the constraint conditions to achieve a significant reduction in steering radius. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1This is a schematic diagram of the control strategy of a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention;

[0041] Figure 2 This is a schematic diagram of the membership function of the adhesion confidence of each wheel in a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system of the present invention;

[0042] Figure 3 This is a schematic diagram of the membership function of the slip ratio of each wheel in the distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system of the present invention;

[0043] Figure 4 This is a schematic diagram of a high-attachment ground target yaw rate tracking method and system for a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention.

[0044] Figure 5 This is a schematic diagram illustrating the high ground adhesion wheel speed control effect of a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention.

[0045] Figure 6 This is a schematic diagram of a low-ground-attachment target yaw rate tracking method and system for a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention.

[0046] Figure 7 This is a schematic diagram illustrating the low-friction-ground wheel speed control effect of a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention.

[0047] Figure 8 This is an architecture diagram of the steering control device of the distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system of the present invention;

[0048] Figure 9 This is a fuzzy control system diagram of a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention;

[0049] Figure 10 This is a diagram of a slicker control system for a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention.

[0050] Figure 11 This is a schematic diagram of the error membership function between the actual yaw rate and the target yaw rate of a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention;

[0051] Figure 12 This is a schematic diagram of the error membership function between the actual center of gravity lateral deviation angle and the target center of gravity lateral deviation angle of a distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system according to the present invention.

[0052] Figure 13 This is a schematic diagram of the membership function of the vehicle steering yaw compensation torque of the distributed electric drive vehicle four-wheel differential torque enhanced steering control method and system of the present invention;

[0053] Figure 14 This invention relates to a high-attachment ground core displacement method and system for four-wheel differential torque-enhanced steering control of distributed electric drive vehicles.

[0054] Figure 15 This invention relates to a low-attachment-ground core displacement method and system for a distributed electric drive vehicle with four-wheel differential torque enhancement steering control. Detailed Implementation

[0055] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0056] Current research on torque coordination in distributed electric drives lacks adaptability to field and paved surface adhesion conditions, relying on the driver's judgment of the vehicle's state to adjust output torque and steering speed, resulting in poor torque coordination performance of the four-wheel hub motors. Therefore, if... Figure 1 As shown, on one hand, the present invention provides a four-wheel differential torque-enhanced steering control method for distributed electric drive vehicles. The vehicle is equipped with an upper-level torque decision module, a lower-level torque coordination module, and on-board sensors, and specifically includes the following steps:

[0057] S1: Select the stationary steering assist strategy according to the actual driving scenario, and press the function button to enter the working condition;

[0058] S2: By acquiring vehicle dynamic information through onboard sensors and combining it with static vehicle data, it determines whether the vehicle is in a stationary turning or small-radius turning state. When the selected function button is stationary turning, it calculates the slip ratio of each wheel and establishes inference rules for the slip ratio of each wheel based on fuzzy experience derived from experiments to estimate the current road adhesion coefficient. m i Then, proceed to step A; when the selected function key is in the small radius steering state, proceed to step B.

[0059] In this step, vehicle dynamic information is obtained through on-board sensors, and the vehicle static data is combined to determine whether the vehicle is in a stationary turning or small-radius turning state. Specifically, an inertial navigation system, an MCU computing unit and a vehicle controller are also installed on the vehicle. The inertial navigation system, the MCU computing unit and the on-board sensors obtain and calculate the vehicle state parameters: the inertial navigation system obtains the vehicle's lateral and longitudinal acceleration and yaw rate, and the on-board sensors obtain the wheel speed.

[0060] The MCU computing unit combines the vehicle's longitudinal and lateral accelerations, wheel speeds, and static parameters of the vehicle body to estimate the real-time slip ratio of each wheel. The slip ratio of each wheel is defined as follows:

[0061]

[0062] in, l 1. l 2. l 3. l 4 represents the slip ratios of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. V x , V y These are the longitudinal velocity and the lateral velocity, respectively, obtained by integrating the longitudinal acceleration and the lateral acceleration of the vehicle. B The wheelbase of the vehicle; a , b These are the distances from the center of mass to the front and rear axles, respectively. c The yaw rate at the center of mass; oh 1. oh 2. oh 3. oh 4 represents the wheel speed; r The tire's rolling radius;

[0063] Then, based on the slip ratio of each wheel of the vehicle, the vehicle controller establishes inference rules for the slip ratio of each wheel according to fuzzy experience, and estimates the current road adhesion coefficient. m i , ,in To improve the utilization rate of each wheel's adhesion, For the attachment confidence level of each wheel, i =1, 2, 3, 4; where inference rules for the adhesion confidence and slip ratio of each wheel are established based on fuzzy experience, referring to the attachment... Figure 2 and attached Figure 3 As shown.

[0064] Step A can be specifically divided into:

[0065] A100: Based on the obtained road adhesion coefficient m iThe peak yaw rate is calculated based on the theory of attached ellipse, and combined with the accelerator pedal opening signal output by the driver, a decision is made on the desired yaw rate that conforms to the road conditions. c desier .

[0066] In step A100, the desired yaw rate that meets the road surface conditions is determined. c desier The specific content is as follows:

[0067] Based on the current road adhesion coefficient, the relationship between longitudinal and lateral forces is defined using an adhesion ellipse fitting method. Combined with the wheel's rotational dynamics equations, the longitudinal force of the tire is constrained. The yaw acceleration at the center of mass, based on the adhesion ellipse and dynamic constraints, is defined as follows:

[0068] Among them , These represent the longitudinal and lateral forces on the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. i =1, 2, 3, 4; This represents the yaw moment of inertia of the entire vehicle. Let be the yaw acceleration at the center of mass.

[0069] The accelerator pedal opening value captured by the sensor and output by the driver is defined as:

[0070] ;in This is the weighting coefficient for the accelerometer pedal opening, with a maximum value of 1; This is the accelerator pedal opening signal; This represents the maximum limit value of the accelerator pedal opening signal. The accelerator pedal opening free travel; the desired yaw rate based on the yaw acceleration at the center of mass and the accelerator pedal opening, conforming to road conditions. c desier Defined as: ,in This represents the settling time per unit step response under different road surface adhesion limits. Let yaw acceleration be the angular acceleration at the center of mass. For the desired yaw rate c desier The derivative of .

[0071] A200: Employs a BPPID adaptive PID control algorithm based on a BP neural network. It utilizes the neural network's information transmission pattern and learning rules to tune the proportional, integral, and derivative parameters of the PID control, obtaining the desired yaw rate. c desier Perform tracking control and obtain the desired yaw moment. Md The desired yaw moment during steering is determined by the upper-level torque decision module. M d The output.

[0072] In step A200, the desired yaw moment is obtained. M d The specific content is: based on the obtained expected yaw rate that conforms to the road surface conditions. c desier Based on the BPPID adaptive PID control algorithm based on BP neural network, a yaw rate controller with adaptive tuning parameters is established, and the learning rule is defined as follows:

[0073] ; ;in represent t Adaptive tuning parameters at time t, represent t Adaptive tuning parameters at time -1 It's the learning rate. y It is a set value. This is the current predicted output value. It is the feature quantity of the current input;

[0074] The incremental PID algorithm is defined as follows:

[0075] ;

[0076] ;

[0077] For the current moment k The control output, For the previous moment k -1 control output, This represents the increment between the error at the current time and the error at the previous time. For the current moment k The error, and for k -1 time and k The error at time -2, , and These are the proportional gain parameter, integral gain parameter, and derivative gain parameter; based on the above controller tracking control results, the desired target yaw moment is obtained. M d And send it to the upper-level torque decision module.

[0078] A300: Based on the adhesion coefficient and slip ratio m - l The curve model calculates the optimal slip ratio. l opt When the actual slip ratio is different from the expected optimal slip ratio l opt Due to the existence of errors, a PID control algorithm is used to achieve the desired slip ratio. l opt As a reference input, the output wheel drive torque adjustment amount is used. △T .

[0079] In step A300, the wheel drive torque adjustment amount △T The specific content is as follows:

[0080] Based on the adhesion coefficient and slip ratio under standard ground conditions m - l The curve model, combined with the obtained estimated values ​​of the adhesion coefficients of each wheel, calculates the optimal wheel slip ratio by applying similar weights to the optimal slip ratio on the standard ground. l opt Standard ground similarity is defined as: , , This represents the current adhesion coefficient of the ground. The average adhesion coefficient is calculated for six different standard ground surfaces; the optimal slip ratio is then calculated. l opt Defined as: Among them, the molecules , , , , , The optimal slip ratio under different standard ground similarity conditions; , , , , , For different standard ground similarities; based on the obtained wheel slip ratio estimation results, a PID controller is established, using the optimal slip ratio as the reference input, to obtain the vehicle driving torque adjustment amount. △ T And send it to the upper-level torque decision module.

[0081] A400: Lower-level torque coordination module receives the desired yaw moment. M d The output request is processed, and the output request for the desired yaw moment is allocated according to the torque distribution strategy. The results of the allocation are then processed. T d Output to each motor.

[0082] The specific content of step A400 is as follows: The lower-level torque coordination module receives the target yaw moment request from the upper-level torque decision module, and the upper-level torque decision module outputs the desired yaw moment. M d That is, the desired target yaw moment of the BPPID output based on the average distribution rule for the adaptive PID control algorithm based on BP neural network. M d The allocation will be carried out according to the following rules: Distribute torque to each wheel Output to each motor and execute.

[0083] A500: The lower-level torque coordination module receives and determines the compensation wheel drive torque adjustment amount △ T The system receives the request and simultaneously uses the vehicle's real-time status parameters returned by the onboard sensors and the vehicle controller to determine the vehicle's real-time steering status based on the steering phase judgment rules.

[0084] Based on the vehicle status obtained from onboard sensors and the vehicle controller's estimation of the real-time status, the vehicle status is divided into the starting stage, elastic deformation stage, sideslip stage, and steady-state yaw stage according to the steering determination rules, providing a basis for the formulation of subsequent supplementary strategies.

[0085] A600: Based on the motor's execution of torque commands, the real-time feedback of vehicle steering status, and the set torque compensation strategy, the wheel drive torque adjustment amount... △T Intervention is provided to compensate and maintain the stability of the vehicle's state during the stationary turning process.

[0086] Real-time state parameters during vehicle steering are monitored using onboard sensors and the vehicle controller. Based on the established drive supplementation strategy, the intervention timing of wheel drive torque adjustment ΔT is determined to improve consistency during four-wheel steering and maintain stable operating conditions. Based on the established control structure, experimental verification was conducted on high and low ground surfaces. The experimental results are as follows: Figure 4 and Figure 5 As shown. On high-friction surfaces, the vehicle gradually starts and the yaw rate approaches the desired value. Overshoot occurs at 5 to 6 seconds, at which point supplementary control intervenes, reducing wheel speed and gradually stabilizing the yaw rate until it converges to the desired value of 8° / s. On low-friction surfaces, the vehicle's start-up response is more rapid, gradually approaching the desired yaw rate. A slight overshoot occurs at 5 to 6 seconds, and after supplementary control intervenes, the yaw rate gradually converges to the desired value.

[0087] Please see the appendix Figure 6 For constrained working conditions with limited steering space, this invention proposes a flexible steering control scheme with supplementary and enhanced steering, namely small-radius steering.

[0088] Step B can be specifically divided into:

[0089] B100: Collects the accelerator pedal opening signal and steering wheel angle signal output by the driver through on-board sensors, and decides the longitudinal force and front wheel angle to maintain the longitudinal movement of the vehicle based on the accelerator pedal opening and steering wheel angle.

[0090] Specifically, this involves using onboard sensors to collect data on the accelerator pedal opening. K 0 and steering wheel angle i The maximum total driving torque is determined based on the vehicle motor's external characteristics and the limitations imposed by high and low speeds on the motor's output torque. ,in n The motor speed is used; based on the linear relationship between the total driving torque and the accelerator pedal, the longitudinal torque that meets the driver's expectations is determined. : The ratio coefficient between the steering wheel angle and the front wheel angle is used to determine the steering wheel angle. i Front wheel steering angle .

[0091] B200: Obtain the real-time front wheel steering angle and longitudinal speed of the vehicle during driving overload, and combine them with the vehicle's static parameters, such as track width, wheelbase, vehicle mass, tire lateral stiffness and rolling radius, to calculate the target yaw rate and target center of gravity lateral deviation angle that are consistent with the real-time state of the vehicle.

[0092] Specifically, based on the real-time acquisition of vehicle steering state parameters by the vehicle controller, the MCU calculation unit, based on the real-time vehicle state information acquired by the vehicle controller and combined with the vehicle's static parameters, calculates the target yaw rate that matches the vehicle's real-time state. Lateral deviation angle of the target's center of gravity They are defined as follows: , Among them, stability factors K Defined as , L Wheelbase a This refers to the front wheelbase. b Rear wheelbase The average steering angle of the front wheels. For the rear axle wheel lateral stiffness, The front axle wheel lateral stiffness is used. A dual-controller system, consisting of a fuzzy controller and a sliding mode controller, is employed to track and tune the target state parameters. The two control structures are as follows: Figure 7 , 8 As shown.

[0093] B300: Based on the calculated values ​​of the target yaw rate and the target center of gravity lateral deviation angle, a dual-controller system is employed, consisting of a fuzzy controller constructed using fuzzy inference rules based on experimental experience and a sliding diaphragm controller based on a sliding diaphragm control algorithm. Weighted matching tracking is performed to obtain the supplementary yaw moment based on the vehicle's steering state. For the fuzzy controller, the fuzzy inference rules and corresponding membership functions are tuned based on experimental experience, as follows: Figure 9 , Figure 10 and Figure 11 As shown.

[0094] Specifically, a dual-controller system, consisting of a fuzzy controller and a sliding mode controller, is used to track and tune the target state parameters. For the fuzzy controller, the fuzzy inference rules and corresponding membership functions are tuned based on experimental experience to improve the control output. For the sliding mode controller, a joint control system is designed with yaw rate and lateral deviation angle of the center of gravity as control targets. The error between the target and actual values ​​of these two controllers, and the first derivative of this error, are defined as intermediate variables. , , among which and and These are the actual yaw angular velocity and its first derivative, respectively. and These represent the actual lateral deviation angle of the center of gravity and its first derivative, respectively. The point above the variable represents the first derivative. and These are the error between the actual yaw rate and the target yaw rate, and the first derivative of the error, respectively. and These represent the error between the actual and target center of gravity lateral deviation angles, and the first derivative of the error, respectively. Considering the robustness requirements of the control, a constant velocity approach rate is selected to design the sliding surface. s , ,in k This is a weighting coefficient, with a value range of (0, 1], which is adjusted according to the actual condition of the vehicle. The relative weighting coefficient between the yaw rate error and its reciprocal is set to a value greater than 0. Finally, a Lyapunov function is defined to verify the stability of the joint control system of yaw rate and center of gravity lateral deviation angle. Based on the controller tracking control results described above, the vehicle steering yaw compensation torque required to complete the vehicle steering is obtained. and , To supplement the steering yaw torque output by the fuzzy controller, It provides additional torque to the steering yaw rate output by the diaphragm controller.

[0095] B400: Based on the supplementary yaw moment, establish constraints based on the desired state objective function to distribute the supplementary moment and tune the final torque value of each wheel.T i .

[0096] Specifically, this involves determining the vehicle steering yaw compensation torque required for the vehicle to complete its steering maneuver. and Combined with longitudinal torque that meets the driver's expectations T l Establish a torque distribution strategy, the details of which are as follows: , The steering yaw compensation torque is provided for the outputs of the fuzzy controller and the sliding diaphragm controller, respectively. j =1, 2.

[0097] Based on the above allocation rules and dual controllers, experimental verification was conducted on both high-attachment and low-attachment ground surfaces. The experimental results are as follows: Figure 12 and Figure 13 As shown. Under high-adhesion conditions, compared to the steering condition without supplemental reinforcement, the steering radius is effectively reduced by at least 20% after supplemental reinforcement of the outer steering wheel. Under low-adhesion conditions, the steering radius is effectively reduced by at least 15% after supplemental reinforcement of the outer steering wheel, and the tracking error is also effectively reduced.

[0098] B500: Based on the final torque value of each wheel T i The motor responds to the requested torque output and completes a small-radius steering maneuver.

[0099] In step B of this control method, firstly, on-board sensors acquire driver operation signals such as accelerator pedal opening and steering wheel angle signals, and based on the driver's intention, determine the longitudinal force to maintain vehicle movement and the front wheel steering angle. Secondly, combining real-time vehicle status information acquired by the vehicle controller, such as front wheel steering angle, longitudinal vehicle speed, and vehicle static parameters, calculate the target yaw rate and desired center of gravity lateral deviation angle reflecting the driver's operation. Then, a dual-controller system based on fuzzy controller and sliding mode controller tracks and controls the target yaw rate and desired center of gravity lateral deviation angle, and outputs a supplementary yaw moment for vehicle steering. Finally, the torque of each wheel is tuned according to the target torque function of each wheel's driving force and the constraint conditions. By adopting a dual-controller structure, the torque of each wheel can be effectively distributed and adjusted under complex working conditions, ensuring flexible handling and stable driving of the vehicle in confined spaces. Through this innovative control strategy, this invention significantly improves the vehicle's power and passability under various ground conditions, enhancing driving safety and comfort.

[0100] In addition, the present invention also provides a distributed electric drive vehicle four-wheel differential torque enhanced steering control system, including an upper-level torque decision module, a lower-level torque coordination module and on-board sensors configured on the vehicle.

[0101] The vehicle-mounted sensor is used to acquire vehicle dynamic information after the auxiliary function button is pressed, and to determine whether the vehicle is in a stationary turning or small-radius turning state by combining the vehicle static data.

[0102] The upper-level torque decision module, upon confirming that the vehicle is in a stationary turning state, calculates the slip ratio of each wheel and establishes inference rules corresponding to the slip ratio of each wheel based on fuzzy experience derived from experiments to estimate the current road surface adhesion coefficient. m i The peak yaw rate is calculated based on the theory of attached ellipse, and combined with the accelerator pedal opening signal output by the driver, a decision is made on the desired yaw rate that conforms to the road conditions. c desier The BPPID adaptive PID control algorithm based on a BP neural network is used. The proportional, integral, and derivative parameters of the PID control are tuned using the neural network information transmission mode and learning rules, and the desired yaw rate is obtained. c desier Perform tracking control and obtain the desired yaw moment. M d The desired yaw moment during steering is determined by the upper-level torque decision module. M d The output; based on the adhesion coefficient and slip ratio. m - l The curve model calculates the optimal slip ratio. l opt When the actual slip ratio is different from the expected optimal slip ratio l opt When errors exist, a PID control algorithm is used to achieve the desired slip ratio. l opt As a reference input, the output wheel drive torque adjustment amount is used. △T When the vehicle is confirmed to be in a small-radius turning state, the accelerator pedal opening signal and steering wheel angle signal output by the driver are collected through on-board sensors. Based on the accelerator pedal opening and steering wheel angle, the longitudinal force to maintain the vehicle's longitudinal movement and the front wheel angle are determined. The real-time front wheel angle and longitudinal speed during vehicle overload are obtained. Combined with the vehicle's static parameters, the target yaw rate and target center of gravity lateral deviation angle that conform to the real-time state of the vehicle are calculated. Based on the calculated values ​​of the target yaw rate and target center of gravity lateral deviation angle, a dual controller consisting of a fuzzy controller constructed based on fuzzy inference rules established based on experimental experience and a sliding diaphragm controller based on a sliding diaphragm control algorithm is used to perform weight allocation matching tracking to obtain the supplementary yaw moment based on the vehicle's turning state. According to the supplementary yaw moment, constraints based on the objective function of the desired state are established to allocate the supplementary moment and tune the final value of the torque of each wheel. T i ;

[0103] The lower-level torque coordination module receives the desired yaw torque when the vehicle is in a stationary turning state. M d The output request is processed, and the output request for the desired yaw moment is allocated according to the torque distribution strategy. The results of the allocation are then processed. T d The output is sent to each motor, and the determined compensation wheel drive torque adjustment amount is received. △T Upon receiving the request, the vehicle's real-time steering status is determined based on the vehicle's real-time status parameters returned by onboard sensors and the vehicle controller, according to the steering phase judgment rules. Based on the motor's torque command execution, the feedback on the vehicle's real-time steering status, and the set torque compensation strategy, the wheel drive torque is adjusted. △T Intervention is performed to compensate and maintain the stability of the vehicle's state during the stationary turning process; when the vehicle is in a small-radius turning state, it is based on the final value of the torque of each wheel. T i The motor responds to the requested torque output and completes a small-radius steering maneuver.

[0104] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for distributed electrically driven vehicle four-wheel differential torque enhancement steering control, characterized in that, The vehicle is provided with an upper torque decision module, a lower torque coordination module and a vehicle sensor, and specifically includes the following steps: The steering state is determined by the vehicle sensor combined with the vehicle body static data: if it is original steering, the slip rate of each wheel and the road adhesion coefficient are obtained, and step A is executed; if it is small radius steering, step B is executed; Step A: According to the road adhesion coefficient, the expected yaw rate that meets the road condition is decided γ desier ; The obtained expected yaw rate γ desier is tracked and the expected yaw moment is obtained M d The output of the expected yaw moment during steering is completed by the upper torque decision module M d ; According to the adhesion coefficient and the slip rate μ - λ curve model, the optimal slip rate is calculated λ opt ; The lower layer torque cooperation module receives an output request of the desired yaw moment M d and allocates the output request of the desired yaw moment according to a torque distribution strategy, and outputs a result of the allocation to each motor T d ​ Step B: Collect the accelerator pedal opening degree signal and steering wheel angle signal output by the driver through the vehicle-mounted sensor, and decide to keep the longitudinal force of the vehicle longitudinal driving and the front wheel angle of the vehicle based on the accelerator pedal opening degree and the steering wheel angle; Calculate the target yaw angular velocity and target gravity center lateral deviation angle conforming to the real-time state of the vehicle; Further obtain the supplementary yaw moment based on the steering state of the vehicle, distribute the supplementary moment based on the constraint condition of the expected state target function, and set the final value of each wheel torque T i based on the final value of each wheel torque T i Request torque output, and the motor executes a small-radius steering response.

2. The control method of claim 1, wherein, The steering state is determined by the vehicle sensor combined with the vehicle body static data, specifically including that an inertial navigation system and an MCU calculation unit are also installed on the vehicle, and the inertial navigation system, the MCU calculation unit and the vehicle sensor obtain and calculate the vehicle state parameters: the inertial navigation system obtains the vehicle lateral and longitudinal acceleration and yaw rate, and the vehicle sensor obtains the wheel speed; The MCU computing unit estimates real-time slip rates of each wheel in combination with vehicle longitudinal and lateral accelerations, wheel speeds and body static parameters, and the slip rate of each wheel is defined as: ; wherein, λ 1, λ 2, λ 3, λ 4 are the slip rates of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel respectively; V x , V y are longitudinal and lateral velocities respectively, and are obtained by integrating vehicle longitudinal and lateral accelerations respectively; B is the wheel base of the vehicle; a , b are the distances from the center of mass to the front and rear axles respectively; γ is the yaw angular velocity at the center of mass; ω 1, ω 2, ω 3, ω 4 are wheel speeds; r is the tire rolling radius; Then the vehicle control unit estimates the current road adhesion coefficient according to the slip ratio of each wheel of the current vehicle and the inference rule corresponding to the slip ratio of each wheel established according to fuzzy experience μ i , wherein is the adhesion utilization ratio of each wheel, is the adhesion confidence of each wheel.

3. The control method of claim 2, wherein, Decide a desired yaw rate that corresponds to a road condition γ desier The specific content is: According to the current road adhesion coefficient, the relationship between the longitudinal force and the lateral force is defined by the method of fitting the adhesion ellipse, and the tire longitudinal force is constrained by combining the rotational dynamics equation of the wheel: the yaw angular acceleration at the center of mass based on the adhesion ellipse and the dynamics constraint is defined as: wherein , respectively represent the longitudinal force and lateral force of the left front wheel, right front wheel, left rear wheel and right rear wheel, i = 1, 2, 3, 4; is the whole vehicle yaw moment of inertia; is the yaw angular acceleration at the center of mass; the accelerator pedal opening degree of the driver output collected by the sensor is defined as: ; wherein is an acceleration pedal opening weight factor with a maximum value of 1 ; is an acceleration pedal opening signal; is an acceleration pedal opening signal maximum limit value; is an acceleration pedal opening free travel; desired yaw angular velocity based on the yaw angular acceleration at the center of mass and the acceleration pedal opening, subject to road conditions γ desier is defined as: wherein denotes the unit step response adjustment time at different road surface adhesion limits, is the yaw angular acceleration at the center of mass, is the desired yaw angular velocity γ desier derivative of 4. The control method of claim 3, wherein, Desired yaw moment M d The specific content is: based on the obtained desired yaw rate γ desier According to the adaptive PID control algorithm BPPID based on BP neural network, an adaptive parameter setting yaw rate controller is established, and the learning rule is defined as follows: ; ; wherein represents t adaptive tuning parameters at the time instant t, represents t adaptive tuning parameters at the time instant t-1, is a learning rate, y is a set value, is a predicted value of the current output, is a feature quantity of the current input; It is defined by the incremental PID algorithm: ； ； the control output at the current time, k the control output at the previous time, k -1, an increment between the error at the current time and the error at the previous time; the error at the current time, k and the errors at the times k -1 and k -2, , and are respectively a proportional gain parameter, an integral gain parameter and a differential gain parameter; based on the controller tracking control result to obtain the expected target yaw moment M d , and send to the upper layer torque decision module.​​ 5. The control method of claim 4, wherein, when the actual slip ratio differs from the desired optimal slip ratio λ opt there is an error, a PID control algorithm is employed to bring the actual slip ratio to the desired optimal slip ratio μ opt as a reference input, an output wheel drive torque adjustment amount △T , and the specific content is: based on the adhesion coefficient and slip ratio of the standard ground λ - λ curve model, combining the obtained wheel adhesion coefficient estimates, the optimal slip ratio of the wheel is calculated by processing the optimal slip ratio of the standard ground with similar weights λ opt , the standard ground similarity is defined as: , , is the adhesion coefficient of the current ground, is the adhesion coefficient of the current ground, is the average of the adhesion coefficients of the six different standard grounds; the optimal slip ratio λ opt is defined as: , wherein the numerator , , , , , is the optimal slip ratio under different standard ground similarities; , , , , , is the different standard ground similarity; based on the obtained wheel slip ratio estimation results, a PID controller is established, taking the optimal slip ratio as the reference input, to obtain the vehicle driving torque adjustment amount △T , and send it to the upper layer torque decision module.

6. The control method of claim 5, wherein, The output request of the expected yaw moment is allocated according to a torque allocation strategy, and the result generated by the allocation T d The specific content output to each motor is that the lower-layer torque cooperation module receives a target yaw moment request of the upper-layer torque decision module, and the expected yaw moment output by the upper-layer torque decision module M d That is, the expected target yaw moment output by the adaptive PID control algorithm BPPID based on the BP neural network based on the average allocation rule M d The allocation is performed, and the allocation rule is: The torques of the wheels are allocated Output to each motor and execute; The lower layer torque coordination module receives a request to determine a compensation wheel drive torque adjustment amount △T , and simultaneously returns vehicle real-time state parameters through vehicle-mounted sensors and a vehicle controller, determines the vehicle steering real-time state according to steering stage judgment rules; according to the motor execution torque command condition and the feedback vehicle steering real-time state, and the set torque compensation strategy, the wheel drive torque adjustment amount △T is compensated, and the stability of the vehicle state during the original steering process is maintained.

7. The control method of claim 2, wherein, The acceleration pedal opening degree signal and the steering wheel turning angle signal of the driver output are collected by the vehicle-mounted sensor in step B, and the longitudinal force of the vehicle for keeping longitudinal driving and the front wheel turning angle of the vehicle are decided based on the acceleration pedal opening degree and the steering wheel turning angle. The specific content is that the acceleration pedal opening degree is collected by the vehicle-mounted sensor K 0and the steering wheel turning angle θ The maximum total driving torque is formulated based on the motor external characteristic and the limitation of the high and low rotating speed on the motor output torque , wherein n is the motor rotating speed Based on the linear relationship between total drive torque and accelerator pedal, a decision is made to apply a longitudinal force moment that matches the driver's expectation : . The front wheel angle based on the steering wheel angle is determined by a proportional coefficient between the steering wheel and the front wheel angle θ .​ 8. The control method of claim 7, wherein, The target yaw angular velocity and the target lateral deviation angle of the gravity center meeting the real-time state of the vehicle are calculated in step B. Specifically, the vehicle steering state parameters are collected in real time based on the vehicle controller, the MCU calculation unit combines the vehicle real-time state information collected by the vehicle controller with the vehicle static parameters to define the target yaw angular velocity and the target lateral deviation angle of the gravity center meeting the real-time state of the vehicle as K L a b ​​​​​​​​​​​ 9. The control method of claim 8, wherein, The supplementary yaw moment based on the vehicle steering state is obtained in step B, the constraint condition based on the desired state target function is established to distribute the supplementary moment and set the final value of each wheel torque T i The specific content is that a double controller composed of a fuzzy controller and a sliding mode controller is used to track and set the target state parameters, wherein for the fuzzy controller, the fuzzy reasoning rules and the corresponding membership functions are set based on the experimental experience to perfect the control output; for the sliding mode controller, a joint control system with the yaw rate and the lateral deviation angle of the gravity center as the control targets is designed, and the error between the target values and the actual values and the first derivative of the error are defined as , , wherein and are the actual yaw rate and the first derivative thereof, and are the actual lateral deviation angle of the gravity center and the first derivative thereof, the dot above the variable represents the first derivative, and are the error between the actual yaw rate and the target yaw rate and the first derivative of the error; and are the error between the actual lateral deviation angle of the gravity center and the target lateral deviation angle of the gravity center and the first derivative of the error; considering the robustness requirement of the control, the sliding mode surface is designed with the equal speed approaching rate s , , wherein k is the weight coefficient, the value range is (0, 1], and the value is set according to the actual state of the vehicle; is the relative weight coefficient between the yaw rate error and the reciprocal, the value is greater than 0; finally, the Lyapunov function is defined to check the stability of the joint control system of the yaw rate and the lateral deviation angle of the gravity center: ; based on the tracking control result of the controller, the vehicle steering yaw supplementary moment and , is the steering yaw supplementary moment output by the fuzzy controller, is the steering yaw supplementary moment output by the sliding mode controller; based on the obtained vehicle steering yaw supplementary moment and , the longitudinal force moment T l is combined to establish the torque distribution strategy, and the content is as follows: , is the steering yaw supplementary moment output by the fuzzy controller and the sliding mode controller respectively, j =1, 2.

10. A distributed electric drive vehicle four-wheel differential torque enhancement steering control system, characterized by, The upper torque decision module, the lower torque coordination module and the vehicle sensor are configured on the vehicle; The vehicle sensor is used to obtain the vehicle dynamic information combined with the vehicle body static data to determine whether the vehicle is in the original steering or small radius steering state after the auxiliary function button is pressed; The upper-level torque decision module, upon confirming that the vehicle is in a stationary turning state, calculates the slip ratio of each wheel and establishes inference rules corresponding to the slip ratio of each wheel based on fuzzy experience derived from experiments to estimate the current road surface adhesion coefficient. μ i The peak yaw rate is calculated based on the theory of attached ellipse, and combined with the accelerator pedal opening signal output by the driver, a decision is made on the desired yaw rate that conforms to the road conditions. γ desier The BPPID adaptive PID control algorithm based on a BP neural network is used. The proportional, integral, and derivative parameters of the PID control are tuned using the neural network information transmission mode and learning rules, and the desired yaw rate is obtained. γ desier Perform tracking control and obtain the desired yaw moment. M d The desired yaw moment during steering is determined by the upper-level torque decision module. M d The output; based on the adhesion coefficient and slip ratio. μ - λ The curve model calculates the optimal slip ratio. λ opt When the actual slip ratio is different from the expected optimal slip ratio λ opt When errors exist, a PID control algorithm is used to achieve the desired optimal slip ratio. λ opt As a reference input, the output wheel drive torque adjustment amount is used. △T When the vehicle is confirmed to be in a small-radius turning state, the accelerator pedal opening signal and steering wheel angle signal output by the driver are collected by the vehicle sensors. Based on the accelerator pedal opening and steering wheel angle, the longitudinal force to maintain the longitudinal movement of the vehicle and the front wheel angle are determined. The real-time front wheel angle and longitudinal speed during vehicle overload are obtained. Combined with the static parameters of the vehicle body, the target yaw rate and target center of gravity lateral deviation angle that conform to the real-time state of the vehicle are calculated. Based on the calculated values of target yaw rate and target lateral deviation angle, a double controller composed of a fuzzy controller constructed by using fuzzy inference rules based on experimental experience and a sliding mode controller based on sliding mode control algorithm is used to perform weight distribution matching tracking to obtain a supplementary yaw moment based on the steering state of the vehicle; and according to the supplementary yaw moment, a constraint condition based on a desired state target function is established to distribute the supplementary moment and set the final value of each wheel torque T i ; The lower-level torque coordination module receives the desired yaw torque when the vehicle is in a stationary turning state. M d The output request is processed, and the output request for the desired yaw moment is allocated according to the torque distribution strategy. The results of the allocation are then processed. T d The output is sent to each motor, and the determined compensation wheel drive torque adjustment amount is received. △T Upon receiving the request, the vehicle's real-time steering status is determined based on the vehicle's real-time status parameters returned by onboard sensors and the vehicle controller, according to the steering phase judgment rules. Based on the motor's torque command execution, the feedback on the vehicle's real-time steering status, and the set torque compensation strategy, the wheel drive torque is adjusted. △T Intervention is performed to compensate and maintain the stability of the vehicle's state during the stationary turning process; when the vehicle is in a small-radius turning state, it is based on the final value of the torque of each wheel. T i The motor responds to the requested torque output and completes a small-radius steering maneuver.

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