Distributed drive electric vehicle stability control method, system and medium

Abstract

This invention discloses a method, system, and medium for controlling the driving stability of a distributed drive electric vehicle. The method uses an improved particle swarm optimization sliding mode control strategy to coordinate the distribution of driving torque in the distributed drive electric vehicle, thereby enabling the vehicle to achieve the desired yaw torque and maintain stable driving. Furthermore, the improved particle swarm optimization algorithm regionalizes the particle population and proposes using regional weights as a reference index for particle division. Particles in each region are updated and iterated simultaneously, shortening the convergence time of the fitness value. Further, a two-degree-of-freedom vehicle model is used to calculate the desired yaw rate and sideslip angle during vehicle operation. The errors between the expected and actual values ​​of the yaw rate and sideslip angle are collected as inputs to the stability controller. By rationally distributing the driving torque of each wheel, the overall vehicle driving stability is improved.

Description

Technical Field

[0001] This invention relates to the field of electric vehicle chassis control technology, specifically to a method, system, and medium for controlling the driving stability of a distributed drive electric vehicle. Background Technology

[0002] In recent years, with the increasing pressure on society from energy shortages and environmental pollution, electric vehicles have become a research hotspot. Distributed-drive electric vehicles, which embed the motor directly into the wheel hub, possess unique advantages compared to traditional internal combustion engine vehicles or centrally integrated electric vehicles. Their compact structure, high transmission efficiency, and strong fault tolerance in the drive system have attracted widespread attention from experts and scholars both domestically and internationally. Because distributed-drive electric vehicles rely on the driving force generated by all four wheels, they offer greater controllability and aid in the study of vehicle stability. By rationally distributing the torque of the four wheels, yaw moment can be generated, and a favorable yaw moment can promote vehicle stability.

[0003] Existing stability control strategies for distributed drive electric vehicles are not mature. Most scholars at home and abroad adopt the idea of ​​hierarchical control to design chassis stability control systems. Hierarchical control generally consists of an upper-level controller and a lower-level controller. The upper-level controller uses sliding mode control, fuzzy control, PID control and other methods to calculate additional yaw moment, and the lower-level controller reasonably distributes the torque to each wheel to meet the vehicle's desired yaw moment requirements. Summary of the Invention

[0004] The present invention proposes a distributed drive electric vehicle driving stability control method, device and storage medium, which can at least solve one of the technical problems in the background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A method for driving stability control of a distributed drive electric vehicle includes a vehicle state acquisition module, a desired yaw rate and center of gravity sideslip angle calculation module, a vehicle center of gravity sideslip angle estimation module, a desired yaw moment calculation module, a longitudinal speed tracking module, and a drive torque distribution module.

[0007] The modules communicate with each other via a CAN bus.

[0008] The vehicle status acquisition module uses sensors installed on the vehicle to monitor and collect the vehicle's speed, longitudinal acceleration, lateral acceleration, front wheel steering angle and yaw rate, and transmits them to the center of gravity sideslip angle estimation module to estimate the vehicle's center of gravity sideslip angle in real time.

[0009] The desired yaw rate and sideslip angle are calculated using a two-degree-of-freedom vehicle model.

[0010] The longitudinal speed tracking module calculates the total vehicle driving torque required to maintain the current driver's desired speed based on the speed error.

[0011] The desired yaw moment calculation module uses sliding mode control to calculate the desired yaw moment required for the vehicle to maintain stability, and introduces an improved particle swarm optimization algorithm to tune the reaching law parameters of the sliding mode control.

[0012] The drive torque distribution module aims to maintain the vehicle at the desired yaw moment, taking into account the motor drive limit and the road adhesion limit, and uses a dynamic load distribution method to distribute torque to the four wheels.

[0013] On the other hand, the present invention also discloses a method for controlling the driving stability of a distributed drive electric vehicle, comprising the following steps:

[0014] 1) By establishing a two-degree-of-freedom vehicle model, calculate the desired yaw rate and the desired sideslip angle of the center of mass;

[0015] The specific model formula is as follows:

[0016] (1)

[0017] When the vehicle enters a steady-state driving condition, its sideslip angle and yaw rate are both constant values, and , Substituting this into equation (1), we get:

[0018] (2)

[0019] (3)

[0020] Because the lateral force of vehicle tires is limited by the road surface adhesion coefficient, the values ​​of yaw rate and center of gravity sideslip angle are restricted. The constraints are as follows:

[0021] (4)

[0022] (5)

[0023] Therefore, the desired yaw rate and the desired sideslip angle are:

[0024] (6)

[0025] (7)

[0026] In the formula, , is a stability factor characterizing the steady-state response of a vehicle. δ f For the front wheel steering angle, kf For the front wheel lateral stiffness, k r For rear wheel lateral stiffness, a The distance from the vehicle's center of gravity to the front axle. b The distance from the vehicle's center of gravity to the rear axle. m For the overall vehicle quality, L Wheelbase µ The road surface adhesion coefficient, v x For longitudinal vehicle speed, g This is the acceleration due to gravity.

[0027] 2) Use sensors to detect yaw rate. ω Lateral acceleration a y and longitudinal velocity v x Estimate the centroid sideslip angle β ;

[0028] (8)

[0029] In the formula, v y This refers to the lateral velocity.

[0030] 3) Subtract the expected values ​​of the yaw rate and the sideslip angle from the actual values ​​to obtain the yaw rate error and sideslip angle error, and establish the sliding surface:

[0031] (9)

[0032] In the formula, s For sliding surface, ω d For the desired yaw rate, β d To determine the desired centroid sideslip angle, σ The weight is the centroid sideslip angle.

[0033] Differentiating both sides of equation (9), we get:

[0034] (10)

[0035] Design exponent convergence law:

[0036] (11)

[0037] In the formula, ε and k These are the constant velocity approach law coefficient and the exponential approach law coefficient, respectively. The quality of their values ​​determines the control effect of sliding mode control.

[0038] 4) Calculate the driving torque of the entire vehicle. T d :

[0039] (12)

[0040] In the formula, k p , k i , k d These are the proportional factor, integral factor, and derivative factor for PID control. e v This represents the difference between the desired vehicle speed and the actual vehicle speed.

[0041] 5) Calculate the expected additional yaw moment Δ M :

[0042] (13)

[0043] In the formula, I z Let be the moment of inertia of the vehicle about the z-axis.

[0044] 6) Distribute the torque to each wheel using a dynamic load distribution method:

[0045] (14)

[0046] In the formula, η ij ( ij = fl , fr , rl , rr This represents the ratio of the vertical load on each wheel to the total vertical load on the vehicle. T ij This represents the desired torque for each wheel.

[0047] 7) The approach law parameter in equation (11) is adaptively tuned by the improved particle swarm optimization algorithm. The specific steps of the improved particle swarm optimization algorithm are as follows:

[0048] 1) Initialize the particle swarm, including particle size, number of iterations, particle velocity, and particle position;

[0049] Randomly initialize the velocity v of each particle i =(v i1 ,v i2 ,…,v id ) and particle position x i = (x i1 ,x i2 ,…,x id), where i = 1, 2, ..., n; the total number of particles is n; d is the dimension of the particles.

[0050] The number of iterations is T max Second-rate.

[0051] 2) Particle swarm localization: Divide the particle swarm into sub-regions, average the sub-regions into m, randomly design different region weights and update strategies (different inertia weights and learning factors), and randomly assign particles to each sub-region according to the region weights, and perform updates in each sub-region according to its update strategy.

[0052] 3) Iterate the particle T1 times ( <T max (This process is repeated several times). The fitness function is used to calculate and evaluate the fitness value of each sub-region. Then, the particles are sorted according to their fitness values, and the particles are assigned to positions with region weights from largest to smallest according to their fitness values. The positions and velocities of each particle are updated again. When the maximum number of iterations T is reached... max After that, the optimal fitness value of each sub-region is obtained. By comparing the optimal fitness values ​​of each sub-region, the global optimal fitness value and particle are obtained. This particle contains the global optimal solution.

[0053] Calculate the number of particles assigned to each sub-region:

[0054] (15)

[0055] In the formula, Ф i ( i =1,2,…, m ) to be allocated to the i The number of particles in each sub-region, of which m The number of sub-regions. n The total number of particles, λ i ( i =1,2,…, m ) represents the regional weight coefficient for each sub-region, where, λ i ∈(0, 1) and .

[0056] The selection of regional weighting coefficients is based on 1 / m As the midpoint, when λ i = 1 / m When the probability of an optimal solution existing in each sub-region is equal, the same number of particles are allocated to each sub-region.

[0057] The fitness function described in step 3 is designed based on the ITAE index and is defined as follows:

[0058] (16)

[0059] In the formula, J For fitness value, s Let t be the sliding surface, and t represent time.

[0060] The particle velocity update formula is:

[0061] (17)

[0062] In the formula, Let be the velocity of the particle when iterates to generation t+1. Let t be the position of the particle when it reaches generation t. This represents the position of the particle swarm at generation t. Let be the position of the individual particle at generation t. ω q This is the inertia weighting coefficient. c 1. c 2 represents the learning factors for individuals and groups, respectively. r 1. r 2 is a random number generated between (0,1). i " indicates the first i One particle, d " indicates the first i The dimension of each particle.

[0063] The particle position update formula is:

[0064] (18)

[0065] In the formula, This represents the position of the individual particle at generation t+1.

[0066] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0067] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0068] As can be seen from the above technical solution, the distributed drive electric vehicle driving stability control method of the present invention relates to the field of electric vehicle chassis control, and mainly studies the stability control strategy of distributed drive electric vehicles. The system includes a vehicle state acquisition module, a desired yaw rate and center-of-gravity sideslip angle calculation module, a vehicle center-of-gravity sideslip angle estimation module, a desired yaw moment calculation module, a longitudinal speed tracking module, and a drive torque distribution module. An improved particle swarm optimization algorithm is used to optimize and tune the reaching law parameters of the sliding mode control, solve for the additional yaw moment required when the vehicle turns, and use a dynamic load distribution method to distribute the vehicle's drive torque so that the vehicle reaches the desired yaw moment, thereby improving vehicle driving stability.

[0069] Specifically, this method uses an improved particle swarm optimization sliding mode control strategy to coordinate the distribution of driving torque in a distributed electric vehicle, thereby enabling the vehicle to achieve the desired yaw moment and maintain stable driving. Furthermore, the improved particle swarm optimization algorithm regionalizes the particle population and randomly designs different weights for each sub-region. It proposes using the fitness value of the particles after a certain number of iterations as a reference index for further dividing the sub-regions, with particles in each region updating and iterating simultaneously, shortening the convergence time of the fitness value. Simultaneously, the improved particle swarm optimization algorithm is used to tune the reaching law coefficient in the sliding mode control, further reducing the chattering phenomenon caused by the sliding mode controller, thus making the controller input smoother and improving the system's stability and control accuracy. Furthermore, a two-degree-of-freedom vehicle model is used to calculate the desired yaw rate and sideslip angle during vehicle operation. The errors between the expected and actual values ​​of the yaw rate and sideslip angle are collected as inputs to the stability controller. By rationally distributing the driving torque of each wheel, the overall vehicle driving stability is improved.

[0070] The beneficial effects of this invention are as follows:

[0071] 1. This invention proposes a method for controlling the driving stability of a distributed drive electric vehicle. This method is based on an improved particle swarm optimization algorithm to adaptively tune the reaching law parameters of sliding mode control, which improves the accuracy and reliability of the parameters and is applicable to nonlinear systems with strong robustness.

[0072] 2. The improved particle swarm optimization algorithm rationally divides the search range of particles, enabling them to traverse the search range and enhancing their global search capability. This avoids particles getting trapped in local optima. Furthermore, during computer operation, each sub-region can be processed in parallel, with particles in each sub-region iteratively updated simultaneously. This greatly reduces the convergence time of the fitness value. Attached Figure Description

[0073] Figure 1This is a schematic diagram of a distributed drive electric vehicle driving stability control method in one embodiment of the present invention;

[0074] Figure 2 This is a schematic diagram of a two-degree-of-freedom vehicle model in one embodiment of the present invention;

[0075] Figure 3 This is a flowchart of an improved particle swarm optimization algorithm in one embodiment of the present invention;

[0076] Figure 4 This is a simulation result diagram of the fitness value when the road surface adhesion coefficient is 0.8 in one embodiment of the present invention;

[0077] Figure 5 This is a comparison diagram of yaw rate when the road surface adhesion coefficient is 0.8 in one embodiment of the present invention;

[0078] Figure 6 This is a comparison diagram of the centroid sideslip angle when the road surface adhesion coefficient is 0.8 in one embodiment of the present invention;

[0079] Figure 7 This is a simulation result diagram of the fitness value when the road surface adhesion coefficient is 0.4 in one embodiment of the present invention;

[0080] Figure 8 This is a comparison diagram of yaw rate when the road surface adhesion coefficient is 0.4 in one embodiment of the present invention;

[0081] Figure 9 This is a comparison diagram of the centroid sideslip angle when the road surface adhesion coefficient is 0.4 in one embodiment of the present invention. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0083] The purpose of this invention is to provide a method for controlling the driving stability of a distributed drive electric vehicle. This method is based on an improved particle swarm optimization sliding mode control strategy to coordinate the distribution of driving torque in the distributed drive electric vehicle, thereby enabling the vehicle to achieve the desired yaw torque and maintain stable driving. Furthermore, the improved particle swarm optimization algorithm divides the particle population into regions and proposes using region weights as a reference index for particle division. Particles in each region are updated and iterated simultaneously, shortening the convergence time of the fitness value. Furthermore, a two-degree-of-freedom vehicle model is used to calculate the desired yaw rate and sideslip angle of the vehicle during driving. The error between the expected and actual values ​​of the yaw rate and sideslip angle is collected as the input of the stability controller. By rationally distributing the driving torque of each wheel, the overall driving stability of the vehicle is improved.

[0084] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0085] like Figure 1 As shown in the figure, a schematic diagram of a distributed drive electric vehicle driving stability control method in one embodiment of the present invention is included, which includes a vehicle state acquisition module, a desired yaw rate and center of gravity sideslip angle calculation module, a vehicle center of gravity sideslip angle estimation module, a desired yaw moment calculation module, a longitudinal speed tracking module, a drive torque distribution module, and four hub motors.

[0086] like Figure 2 The image shows a schematic diagram of a two-degree-of-freedom vehicle model.

[0087] 1) By establishing a two-degree-of-freedom vehicle model, calculate the desired yaw rate and the desired sideslip angle of the center of mass;

[0088] The specific model formula is as follows:

[0089] (1)

[0090] When the vehicle enters a steady-state driving condition, its sideslip angle and yaw rate are both constant values, and , Substituting this into equation (1), we get:

[0091] (2)

[0092] (3)

[0093] Because the lateral force of vehicle tires is limited by the road surface adhesion coefficient, the values ​​of yaw rate and center of gravity sideslip angle are restricted. The constraints are as follows:

[0094] (4)

[0095] (5)

[0096] Therefore, the desired yaw rate and the desired sideslip angle are:

[0097] (6)

[0098] (7)

[0099] In the formula, , is a stability factor characterizing the steady-state response of a vehicle. δ f For the front wheel steering angle, k f For the front wheel lateral stiffness, k r For rear wheel lateral stiffness, a The distance from the vehicle's center of gravity to the front axle. b The distance from the vehicle's center of gravity to the rear axle. m For the overall vehicle quality, L Wheelbase µ The road surface adhesion coefficient, v x For longitudinal vehicle speed, g This is the acceleration due to gravity.

[0100] 2) Use sensors to detect yaw rate. ω Lateral acceleration a y and longitudinal velocity v x Estimate the centroid sideslip angle β ;

[0101] (8)

[0102] In the formula, v y This refers to the lateral velocity.

[0103] 3) Subtract the expected values ​​of the yaw rate and the sideslip angle from the actual values ​​to obtain the yaw rate error and sideslip angle error, and establish the sliding surface:

[0104] (9)

[0105] In the formula, s For sliding surface, ω d For the desired yaw rate, β d To determine the desired centroid sideslip angle, σ The weight is the centroid sideslip angle.

[0106] Differentiating both sides of equation (9), we get:

[0107] (10)

[0108] Design exponent convergence law:

[0109] (11)

[0110] In the formula, The derivative of the sliding surface, ε and k These are the constant velocity approach law coefficient and the exponential approach law coefficient, respectively. The quality of their values ​​determines the control effect of sliding mode control.

[0111] 4) Calculate the driving torque of the entire vehicle. T d :

[0112] (12)

[0113] In the formula, k p , k i , k d These are the proportional factor, integral factor, and derivative factor for PID control. e v This represents the difference between the desired vehicle speed and the actual vehicle speed.

[0114] 5) Calculate the expected additional yaw moment Δ M :

[0115] (13)

[0116] In the formula, I z Let be the moment of inertia of the vehicle about the z-axis.

[0117] 6) Distribute the torque to each wheel using a dynamic load distribution method:

[0118] (14)

[0119] In the formula, η ij ( ij = fl , fr , rl , rr This represents the ratio of the vertical load on each wheel to the total vertical load on the vehicle. T ij For the desired torque of each wheel, ij = fl ,fr , rl , rr These represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle, respectively.

[0120] 7) The approach law parameter in equation (11) is adaptively tuned by the improved particle swarm optimization algorithm, such as... Figure 3 As shown, the specific steps of the improved particle swarm optimization algorithm are as follows:

[0121] Step 1: Initialize the particle swarm, including particle size, number of iterations, particle velocity, and spatial dimension;

[0122] Initialize the particle swarm, including particle size, number of iterations, particle velocity, and particle position;

[0123] Randomly initialize the velocity v of each particle i =(v i1 ,v i2 ,…,v id ) and particle position x i = (x i1 ,x i2 ,…,x id ), where i = 1, 2, ..., n; the total number of particles is n; d is the dimension of the particles.

[0124] The number of iterations is T max Second-rate.

[0125] Step 2: Particle swarm localization, dividing the particle into sub-regions, averaging the sub-regions into m, randomly designing different region weights and update strategies (different inertia weights and learning factors), and randomly assigning particles to each sub-region according to the region weights, and performing updates in each sub-region according to its update strategy.

[0126] Step 3: Iterate the particles T1 times ( <T max (This process is repeated several times). The fitness function is used to calculate and evaluate the fitness value of each sub-region. Then, the particles are sorted according to their fitness values, and the particles are assigned to positions with region weights from largest to smallest according to their fitness values. The positions and velocities of each particle are updated again. When the maximum number of iterations T is reached... max After that, the optimal fitness value of each sub-region is obtained. By comparing the optimal fitness values ​​of each sub-region, the global optimal fitness value and particle are obtained. This particle contains the global optimal solution.

[0127] Calculate the number of particles assigned to each sub-region:

[0128] (15)

[0129] In the formula, Ф i ( i =1,2,…,m ) to be allocated to the i The number of particles in each sub-region, of which m The number of sub-regions. n The total number of particles, λ i ( i =1,2,…, m ) represents the regional weight coefficient for each sub-region, where, λ i ∈(0, 1) and .

[0130] The selection of regional weighting coefficients is based on 1 / m As the midpoint, when λ i = 1 / m When the probability of an optimal solution existing in each sub-region is equal, the same number of particles are allocated to each sub-region.

[0131] The fitness function described in step 3 is designed based on the ITAE index and is defined as follows:

[0132] (16)

[0133] In the formula, J For fitness value, s Let t be the sliding surface, and t represent time.

[0134] The particle velocity update formula is:

[0135] (17)

[0136] In the formula, Let be the velocity of the particle when iterates to generation t+1. Let t be the position of the particle when it reaches generation t. This represents the position of the particle swarm at generation t. Let be the position of the individual particle at generation t. ω q This is the inertia weighting coefficient. c 1. c 2 represents the learning factors for individuals and groups, respectively. r 1. r 2 is a random number generated between (0,1). i " indicates the first i One particle, d " indicates the first i The dimension of each particle.

[0137] The particle position update formula is:

[0138] (18)

[0139] In the formula, This represents the position of the individual particle at generation t+1.

[0140] To verify the reliability of the proposed improved particle swarm optimization sliding mode control for vehicle stability control, simulations were conducted comparing the effects on high-adhesion and low-adhesion road surfaces under dual lane-change conditions. The simulation results are as follows: Figures 4-9 As shown. Among them, Figures 4-6 The simulation results are for a road surface adhesion coefficient of 0.8. Figures 7-9 The figure shows the simulation results when the road adhesion coefficient is 0.4. "IPSO" represents the fitness curve of the improved particle swarm optimization algorithm, and "PSO" represents the fitness curve of the traditional particle swarm optimization algorithm. The comparison shows that compared to using traditional PSO, IPSO requires fewer particle iterations, has a faster fitness convergence speed, and the fitness value is closer to the ideal value. The error between the expected and actual values ​​of yaw rate and sideslip angle is significantly reduced, resulting in a significant improvement in the vehicle's lateral stability.

[0141] In another aspect, the present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method described above.

[0142] In another aspect, the present invention also discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method described above.

[0143] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute any of the distributed drive electric vehicle driving stability control methods described above.

[0144] It is understood that the systems, devices, and storage media provided in the embodiments of the present invention correspond to the methods provided in the embodiments of the present invention, and the explanations, examples, and beneficial effects of the relevant content can be referred to the corresponding parts of the above methods.

[0145] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., a solid-state disk (SSD)).

[0146] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0147] The various embodiments in this specification are described in a related manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0148] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for controlling the driving stability of a distributed-drive electric vehicle, characterized in that, Includes the following steps: S1. Calculate the desired yaw rate and the desired sideslip angle of the center of mass; S2. Detecting the front wheel steering angle using sensors. δ f yaw rate ω Lateral acceleration a y and longitudinal velocity v x To estimate the centroid sideslip angle β ; S3, respectively, the yaw angular velocity ω and centroid side slip angle β The difference between the expected and actual values ​​yields the yaw rate error and the centroid sideslip angle error. An improved particle swarm optimization algorithm is used to tune the reaching law coefficients, and a sliding surface is established. （9） In the formula, s For sliding surface, ω d For the desired yaw rate, β d To determine the desired centroid sideslip angle, σ The weight is the centroid sideslip angle. The improved particle swarm optimization algorithm tunes the convergence law coefficients as follows: First, initialize the particle swarm, including particle size, number of iterations, particle velocity, and spatial dimension; then, divide the particle swarm into regions, design region weights, and allocate particles to each sub-region according to the region weights; finally, calculate and evaluate the fitness value of each sub-region using the fitness function, update the position and velocity of each particle, and obtain the optimal fitness value of each sub-region after reaching the maximum number of iterations. Compare the optimal fitness values ​​of each sub-region to obtain the global optimal fitness value and the corresponding particle, which contains the global optimal solution for the convergence law coefficients. S4. Calculate the driving torque of the entire vehicle. T d : （12） In the formula, k p , k i , k d These are the proportional factor, integral factor, and derivative factor for PID control. e v This represents the difference between the desired vehicle speed and the actual vehicle speed. S5. Calculate the expected additional yaw moment Δ M : （13） In the formula, The derivative of the sliding surface, I z Let be the moment of inertia of the vehicle about the z-axis. k f For the front wheel lateral stiffness, k r For rear wheel lateral stiffness, a The distance from the vehicle's center of gravity to the front axle. b The distance from the vehicle's center of gravity to the rear axle; S6. Distribute the torque of each wheel using a dynamic load distribution method: （14） In the formula, η ij , ij = fl , fr , rl , rr This represents the ratio of the vertical load on each wheel to the total vertical load on the vehicle. T ij The desired torque for each wheel; ij = fl , fr , rl , rr These represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle, respectively.

2. The method for controlling the driving stability of a distributed drive electric vehicle as described in claim 1, characterized in that, S1 specifically includes calculating the desired yaw rate and the desired center-of-gravity sideslip angle by establishing a two-degree-of-freedom vehicle model; The desired yaw rate and desired sideslip angle are calculated as shown in equations (6) and (7): （6） （7） In the formula, , is a stability factor characterizing the steady-state response of a vehicle. m For the overall vehicle quality, L Wheelbase µ The road surface adhesion coefficient, v x For longitudinal vehicle speed, g This is the acceleration due to gravity.

3. The method for controlling the driving stability of a distributed drive electric vehicle as described in claim 1, characterized in that, Estimate the centroid sideslip angle β As shown in the following formula: （8） In the formula, v y This refers to the lateral velocity.

4. The method for controlling the driving stability of a distributed drive electric vehicle as described in claim 1, characterized in that, The region weights are determined based on the probability that an optimal solution exists in each sub-region, and the number of particles assigned to each sub-region is calculated: （15） In the formula, Ф i , i =1,2,…, m To be allocated to the first i The number of particles in each sub-region, of which m The number of sub-regions. n The total number of particles, λ i , i =1,2,…, m Here, represents the regional weight coefficient for each sub-region, where... λ i ∈(0, 1) and .

5. The method for controlling the driving stability of a distributed drive electric vehicle as described in claim 4, characterized in that, The fitness function is designed based on the ITAE metric and is defined as follows: （16） In the formula, J For fitness value, s Let t be the sliding surface, and t represent time.

6. The method for controlling the driving stability of a distributed drive electric vehicle as described in claim 1, characterized in that, The particle velocity update formula is: （17） In the formula, Let be the velocity of the particle when iterates to generation t+1. Let t be the position of the individual particle at generation t. This represents the position of the particle swarm at generation t. Let be the position of the individual particle at generation t. This is the inertia weighting coefficient. c 1. c 2 represents the learning factors for individuals and groups, respectively. r 1. r 2 is a random number generated between (0,1). i " indicates the first i One particle, d " indicates the first i The dimension of each particle; The particle position update formula is: （18） In the formula, This represents the position of the individual particle at generation t+1.

7. A distributed drive electric vehicle driving stability control system, used to implement the distributed drive electric vehicle driving stability control method according to any one of claims 1-6, comprising a vehicle state acquisition module for information exchange via a CAN bus, a desired yaw rate and center of gravity sideslip angle calculation module, a vehicle center of gravity sideslip angle estimation module, a desired yaw moment calculation module, a longitudinal speed tracking module, and a drive torque distribution module; characterized in that: The vehicle status acquisition module uses sensors installed on the vehicle to monitor and collect the vehicle's speed, longitudinal acceleration, lateral acceleration, front wheel steering angle and yaw rate, and transmits them to the center of gravity sideslip angle estimation module to estimate the vehicle's center of gravity sideslip angle in real time. The desired yaw rate and sideslip angle are calculated using a two-degree-of-freedom vehicle model. The longitudinal speed tracking module calculates the total vehicle driving torque required to maintain the current driver's desired vehicle speed based on the speed error. The desired yaw moment calculation module uses sliding mode control to calculate the desired yaw moment required for the vehicle to maintain stability, and introduces an improved particle swarm optimization algorithm to tune the reaching law parameters of the sliding mode control. The drive torque distribution module aims to maintain the vehicle at the desired yaw moment, taking into account the motor drive limit and the road adhesion limit, and uses a dynamic load distribution method to distribute torque to the four wheels.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the processor performs the steps of the method as described in any one of claims 1 to 6.

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