A method for calculating weights of coordinated control of distributed drive electric vehicles

Abstract

The present application relates to a method for calculating the weight of coordinated control of a distributed drive electric vehicle. The present application divides the vehicle motion state into three categories, namely, a stable state category, a transition state category, and an unstable state category, and determines the weight factor according to the Euclidean distance between the cluster centers of different categories, and constructs an AFS / DYC coordinated controller objective function based on the weight factor; and finally solves and obtains the optimal weight coefficients of the AFS system and the DYC system, thereby meeting the vehicle stability control requirements and improving the vehicle's lateral stability.

Description

Technical Field

[0001] The present application relates to the field of automobile control, and in particular to a method for calculating weights of coordinated control of distributed drive electric vehicles. Background Art

[0002] As advanced active safety control systems, active front steering (AFS) and direct yaw moment control (DYC) systems can be used to improve the vehicle's handling stability. When the vehicle is in the stability zone, the DYC system is prone to cause longitudinal speed fluctuations in the vehicle, which deteriorates the vehicle's driving smoothness. The AFS system has a faster response speed and control effect. In order to improve the vehicle's handling performance, it is necessary to increase the control weight of the AFS system; when the vehicle is in the unstable zone, the tire lateral force tends to be saturated, and the control accuracy of the AFS system decreases, which cannot meet the vehicle stability control requirements under this condition. In order to improve the vehicle's lateral stability, it is necessary to increase the control weight of the DYC system. Therefore, a high-speed and accurate AFS and DYC system coordinated control weight calculation method determines the intervention and exit time of the active safety control system, which is of great significance for improving the vehicle's driving safety performance.

[0003] At present, the research on the coordinated control weight of the vehicle stability system mainly uses the phase plane theory to judge the stability state of the vehicle, and calculates the coordinated control weight according to the phase plane instability error. Its stable area is mainly divided by regular shapes such as straight lines, circles or diamonds. This is a simplified form of the stable area that cannot fully reflect the actual stable boundary. In addition, in the phase plane, the judgment criteria related to lateral stability are mainly represented by some characteristic values, such as yaw rate or center of mass sideslip angle, which have the problems of weak data foundation and single characteristic parameters. Summary of the invention

[0004] In order to overcome at least one deficiency in the prior art, the present application provides a method for calculating coordinated control weights of a distributed drive electric vehicle.

[0005] In a first aspect, a method for calculating coordinated control weights of a distributed drive electric vehicle is provided, comprising:

[0006] Constructing a vehicle motion state data set; the vehicle motion state data in the vehicle motion state data set are characteristic parameters related to the lateral stability state;

[0007] Perform cluster analysis on the vehicle motion state data in the vehicle motion state data set, divide the vehicle motion state data into three categories, namely, stable state category, transition state category and unstable state category, and determine the cluster centers of the three categories;

[0008] Determine the midpoint of the line connecting the cluster center of the stable state category and the cluster center of the transition state category, and calculate the Euclidean distance between the midpoint and the cluster center of the stable state category, recorded as the first distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the transition state category, recorded as the second distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the unstable state category, recorded as the third distance; calculate the Euclidean distance between the vehicle motion state data at the current moment and the cluster center of the stable state category, recorded as the fourth distance;

[0009] Determining a weight factor in an objective function of an AFS / DYC coordinated controller according to a relationship among the first distance, the second distance, the third distance, and the fourth distance;

[0010] Construct the objective function of AFS / DYC coordinated controller according to the weight factor;

[0011] The objective function of the AFS / DYC coordinated controller is solved to obtain the optimal weight coefficients of the AFS system and the DYC system.

[0012] In one embodiment, the characteristic parameters related to the lateral stability state include: longitudinal speed, steering wheel angle, lateral speed, sideslip angle, roll angle, yaw rate, roll rate, lateral acceleration, front axle load transfer rate and rear axle load transfer rate.

[0013] In one embodiment, according to the relationship between the first distance, the second distance, the third distance and the fourth distance, the weight factor in the objective function of the AFS / DYC coordination controller is determined using the following formula:

[0014]

[0015] λ3=1-λ1-λ2

[0016] Among them, λ1 is the weight factor related to the lateral displacement, λ2 is the weight factor related to the yaw angular velocity, and λ3 is the weight factor related to the sideslip angle of the center of mass; xx1 is the fourth distance, L1 is the first distance, L2 is the second distance, and L3 is the third distance.

[0017] In one embodiment, the objective function of the AFS / DYC coordination controller is expressed by the following formula:

[0018]

[0019] Where J is the objective function value, λ1 is the weight factor related to the lateral displacement, λ2 is the weight factor related to the yaw rate, and λ3 is the weight factor related to the sideslip angle of the center of mass; N is the number of sampling moments, e di is the lateral displacement deviation at sampling time i; edmax is the maximum value of the lateral displacement deviation, β i is the sideslip angle of the center of mass at sampling time i, β ref is the reference center of mass sideslip angle, β max is the maximum value of the center of mass sideslip angle, w i is the yaw angular velocity at sampling time i, w ref is the reference yaw rate, w max is the maximum value of the yaw angular velocity;

[0020] e di , β i 、w i It is determined based on the discrete vehicle motion differential equation, which is related to the weight coefficient of the AFS system.

[0021] In one embodiment, a KDPC algorithm is used to perform cluster analysis on the vehicle motion state data in the vehicle motion state data set.

[0022] In one embodiment, a particle swarm optimization algorithm is used to solve the objective function of the AFS / DYC coordination controller.

[0023] In a second aspect, a distributed drive electric vehicle coordinated control weight calculation device is provided, comprising:

[0024] A data set construction module is used to construct a vehicle motion state data set; the vehicle motion state data in the vehicle motion state data set are characteristic parameters related to the lateral stability state;

[0025] A category division module is used to perform cluster analysis on the vehicle motion state data in the vehicle motion state data set, divide the vehicle motion state data into three categories, namely, a stable state category, a transition state category and an unstable state category, and determine the cluster centers of the three categories;

[0026] The distance calculation module is used to determine the midpoint of the line connecting the cluster center of the stable state category and the cluster center of the transition state category, and calculate the Euclidean distance between the midpoint and the cluster center of the stable state category, which is recorded as the first distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the transition state category, which is recorded as the second distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the unstable state category, which is recorded as the third distance; calculate the Euclidean distance between the vehicle motion state data at the current moment and the cluster center of the stable state category, which is recorded as the fourth distance;

[0027] A weight factor determination module, used to determine a weight factor in an objective function of an AFS / DYC coordination controller according to a relationship among the first distance, the second distance, the third distance, and the fourth distance;

[0028] An objective function building module is used to build the objective function of the AFS / DYC coordination controller according to the weight factor;

[0029] The solution module is used to solve the objective function of the AFS / DYC coordinated controller and obtain the optimal weight coefficients of the AFS system and the DYC system.

[0030] In a third aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned method for calculating the coordinated control weight of distributed drive electric vehicles is implemented.

[0031] In a fourth aspect, a computer program product is provided, including a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for calculating the coordinated control weight of a distributed drive electric vehicle.

[0032] Compared with the prior art, the present application has the following beneficial effects: the present application divides the vehicle motion state into three categories, namely, a stable state category, a transition state category and an unstable state category, and determines the weight factor according to the Euclidean distance between the cluster centers of different categories, and constructs the AFS / DYC coordination controller objective function based on the weight factor; and finally solves and obtains the optimal weight coefficients of the AFS system and the DYC system, thereby meeting the vehicle stability control requirements and improving the vehicle's lateral stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present application may be better understood by referring to the following description given in conjunction with the accompanying drawings, which together with the following detailed description are included in this specification and form a part of this specification. In the drawings:

[0034] Figure 1 A flowchart of a method for calculating coordinated control weights of distributed drive electric vehicles according to an embodiment of the present application is shown;

[0035] Figure 2 A schematic diagram showing a steering wheel angle is shown;

[0036] Figure 3 The distribution diagram of the data point clustering results is shown;

[0037] Figure 4 The simulation result diagram of the coordinated control weight of the distributed drive electric vehicle is shown;

[0038] Figure 5 A structural block diagram of a distributed drive electric vehicle coordinated control weight calculation device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0039] The exemplary embodiments of the present application will be described below in conjunction with the accompanying drawings. For the sake of clarity and conciseness, not all features of the actual embodiments are described in the specification. However, it should be understood that many implementation-specific decisions can be made in the process of developing any such actual embodiments in order to achieve the specific goals of the developer, and these decisions may vary from embodiment to embodiment.

[0040] It is also necessary to explain here that, in order to avoid obscuring the present application due to unnecessary details, only the device structure closely related to the scheme according to the present application is shown in the drawings, while other details that are not closely related to the present application are omitted.

[0041] It should be understood that the present application is not limited to the described implementation forms due to the following description with reference to the accompanying drawings. In this article, where feasible, the embodiments can be combined with each other, features between different embodiments can be replaced or borrowed, and one or more features can be omitted in one embodiment.

[0042] Many attribute parameters of a vehicle can be used to reflect lateral stability, such as longitudinal speed, front wheel steering angle, lateral acceleration, etc. Therefore, the present application provides a method for calculating the coordinated control weights of a distributed drive electric vehicle, which aims to comprehensively analyze multiple attribute parameters related to handling stability through a K-means-density peak clustering (KDPC) algorithm, classify stability types, and calculate the optimal weight coefficients of the AFS system and the DYC system in real time based on the PSO algorithm.

[0043] Figure 1 A flow chart of a method for calculating the coordinated control weight of a distributed drive electric vehicle according to an embodiment of the present application is shown. Figure 1 , methods include:

[0044] Step S1, constructing a vehicle motion state data set; the vehicle motion state data in the vehicle motion state data set are characteristic parameters related to the lateral stability state.

[0045] A steering wheel angle step input test was carried out based on a 10-DOF vehicle driving simulator. The longitudinal, lateral and vertical motion state information of the vehicle at different front wheel steering angles and different driving speeds were collected to construct a vehicle motion state dataset.

[0046] The driving simulator has good safety, and any experimental scene can be designed according to the experimental needs. It is not affected by weather, etc., and has higher flexibility than the actual vehicle experiment, and the driver behavior trends in the two data collection methods have good consistency. Therefore, the vehicle motion state data set is collected based on PreScan, MATLAB / Simulink and Logitech G29 driving simulator. Prescan is used to provide a virtual driving environment and establish a standardized vehicle test simulation scene; MATLAB / Simulink is used to establish a vehicle dynamics model, including 10 degrees of freedom such as longitudinal, lateral, vertical, yaw, roll, pitch and tire movement of the vehicle; Logitech G29 driving simulator is mainly used to collect the driver's steering wheel angle, acceleration and brake pedal opening signals to simulate the actual vehicle driving behavior. At the same time, this embodiment takes into account the handling behavior of drivers with different driving styles, and selects 27 drivers (23 male drivers and 4 female drivers) to conduct simulator tests at different driving speeds. The test condition is set as a standard handling stability test scenario. After a lot of debugging, Figure 2 A schematic diagram of the steering wheel angle is shown, starting from zero and gradually changing to 180 degrees after 1 second, with the longitudinal speed set from 30km / h to 120km / h and the speed interval length being 10km / h.

[0047] In order to comprehensively consider the motion state of the vehicle, the characteristic parameters related to the lateral stability state are collected as follows, including: longitudinal speed, steering wheel angle, lateral speed, sideslip angle, roll angle, yaw angular velocity, roll rate, lateral acceleration, front axle load transfer rate and rear axle load transfer rate.

[0048] The sampling frequency is set to 0.05s, the simulation time is set to 10s, and finally a vehicle motion state data set under different vehicle speeds and different turning angle inputs is established, including a total of 3825 sets of data. The vehicle motion state data set is defined as X, which is expressed as:

[0049]

[0050] X i represents the i-th group of data, m is the number of groups of data, y ij Represents X i The data of the j-th dimension in , D represents the number of dimensions, and in this embodiment, D=10.

[0051] Considering that the unit dimensions of different vehicle state parameters are different, in order to summarize the distribution range of the samples, the sample data is limited to the range of [0,1] based on the normalization method. i Normalize and get the normalized vehicle motion state data x i for:

[0052] x i =(xi1 ,x i2 ,…x ij …,x iD )

[0053]

[0054] Among them, x ij For x i The data of the jth dimension in , y j_min is the minimum value of the data in the jth dimension of the data set X, y j_max is the maximum value of the data in the jth dimension in the dataset X.

[0055] Step S2, cluster analysis is performed on the vehicle motion state data in the vehicle motion state data set, the vehicle motion state data is divided into three categories, namely, a stable state category, a transition state category and an unstable state category, and cluster centers of the three categories are determined.

[0056] Specifically, the KDPC algorithm is used to perform cluster analysis on the vehicle motion state data in the vehicle motion state data set.

[0057] In the KDPC algorithm, DPC (Density Peak Clustering Algorithm) is a new clustering method based on density peak, which aims to obtain the initial cluster center and the number of categories, and then use the K-means algorithm to update the position of the cluster center to avoid falling into local convergence. The specific implementation process of the KDPC algorithm is a well-known technology in the field and will not be repeated here.

[0058] Figure 3 The data point clustering result distribution diagram is shown. It can be seen from the figure that the data points are divided into three clusters in turn and distributed in various positions of the image. The green data point in the far left area indicates that the vehicle is in a "stable state". As the speed and steering wheel angle increase, the vehicle enters the red "transition state" and the blue "instability state" in turn.

[0059] Step S3, determine the midpoint of the line connecting the cluster center of the stable state category and the cluster center of the transition state category, and calculate the Euclidean distance between the midpoint and the cluster center of the stable state category, recorded as the first distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the transition state category, recorded as the second distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the unstable state category, recorded as the third distance; calculate the Euclidean distance between the vehicle motion state data at the current moment and the cluster center of the stable state category, recorded as the fourth distance.

[0060] Step S4, determining a weight factor in the objective function of the AFS / DYC coordination controller according to the relationship among the first distance, the second distance, the third distance and the fourth distance.

[0061] Step S5, constructing the AFS / DYC coordinated controller objective function according to the weight factor.

[0062] Step S6, solving the objective function of the AFS / DYC coordinated controller to obtain the optimal weight coefficients of the AFS system and the DYC system.

[0063] Specifically, the particle swarm optimization algorithm (PSO) can be used to solve the AFS / DYC coordination controller objective function to obtain the optimal weight coefficient of the AFS system, and the optimal weight coefficient of the DYC system can be obtained by subtracting the optimal weight coefficient of the AFS system from 1. Here, the position of the particle in the particle swarm optimization algorithm is defined as the weight coefficient of the AFS system. The particle swarm optimization algorithm is a well-known technology in the art and will not be described in detail here.

[0064] In this embodiment, the vehicle motion state is divided into three categories, namely, a stable state category, a transition state category and an unstable state category, and the weight factor is determined according to the Euclidean distance between the cluster centers of different categories, and the AFS / DYC coordination controller objective function is constructed based on the weight factor; finally, the optimal weight coefficients of the AFS system and the DYC system are solved to meet the vehicle stability control requirements and improve the lateral stability of the vehicle.

[0065] In one embodiment, when the vehicle is in a stable state, there is a large stability margin, and the goal of the coordinated controller is mainly to improve the accuracy of the vehicle path tracking, so the weight coefficient λ1 related to the lateral displacement is large. Similarly, when the vehicle is in a transitional state, the vehicle handling performance is dominant compared to the path tracking accuracy and lateral stability performance, so the weight coefficient λ2 related to the yaw rate is large. When the vehicle is in an unstable state, it is necessary to suppress the size of the center of mass side slip angle to correct the vehicle's unstable state, and the weight coefficient λ3 related to the center of mass side slip angle is large. Specifically, in step S4, the weight factor in the objective function of the AFS / DYC coordinated controller is determined according to the relationship between the first distance, the second distance, the third distance and the fourth distance, using the following formula:

[0066]

[0067] λ3=1-λ1-λ2

[0068] Among them, λ1 is the weight factor related to the lateral displacement, λ2 is the weight factor related to the yaw angular velocity, and λ3 is the weight factor related to the sideslip angle of the center of mass; xx1 is the fourth distance, L1 is the first distance, L2 is the second distance, and L3 is the third distance.

[0069] In one embodiment, the objective function of the AFS / DYC coordination controller is expressed by the following formula:

[0070]

[0071] Where J is the objective function value, λ1 is the weight factor related to the lateral displacement, λ2 is the weight factor related to the yaw rate, and λ3 is the weight factor related to the sideslip angle of the center of mass; N is the number of sampling moments, e di is the lateral displacement deviation at sampling time i; e dmax is the maximum value of the lateral displacement deviation, β i is the sideslip angle of the center of mass at sampling time i, β ref is the reference center of mass sideslip angle, β max is the maximum value of the center of mass sideslip angle, w i is the yaw angular velocity at sampling time i, w ref is the reference yaw rate, w max is the maximum value of the yaw angular velocity;

[0072] e di , β i 、w i Determined according to the discrete vehicle motion differential equation, the discrete vehicle motion differential equation is related to the weight coefficient of the AFS system, which can be determined by the following formula:

[0073]

[0074] b 11 =k1 / mv x ,b 12 =k2 / mv x ,b 21 =ak1 / I z ,b 22 =-bk2 / I z

[0075] Among them, z * is the vehicle motion state information, i is the sampling time, e d is the lateral displacement deviation, e ψ is the heading angle deviation, β is the sideslip angle of the center of mass, w is the yaw angular velocity, E * , D * are coefficient matrices, b 11 , b21 、a 11 、a 12 、a 21 、a 22 are all kinetic model parameters; v x is the longitudinal velocity of the vehicle, I z is the moment of inertia of the vehicle around the z-axis, k1 and k2 are the cornering stiffness of the front and rear wheels respectively, m is the vehicle mass, a and b are the distances from the center of mass of the vehicle to the front and rear axles respectively, P is the weight coefficient of the AFS system, ΔM is the additional yaw moment, Ψ d is the reference heading angle, δ f is the front wheel turning angle.

[0076] Reference yaw rate w ref for

[0077]

[0078] Among them, K is the vehicle stability coefficient, l is the wheelbase, l=a+b, μ is the ground adhesion absorption, and g is the gravitational acceleration.

[0079] Reference center of mass sideslip angle β ref for:

[0080]

[0081] In order to further verify the effectiveness of the method of the present application, a single lane change condition is adopted to further illustrate the coordinated control weights during a simulation experiment at a driving speed of 80 km / h on a road surface with a road adhesion coefficient of 0.3.

[0082] Figure 4 The simulation results of the coordinated control weights of distributed drive electric vehicles are shown. Figure 4 ,In the stable area, the AFS system has a larger weight, and when the vehicle is in the unstable area, the AFS and DYC weights fluctuate dramatically, which jointly improve the vehicle’s handling stability.

[0083] Based on the same inventive concept as the method for calculating the coordinated control weight of a distributed drive electric vehicle, this embodiment also provides a corresponding device for calculating the coordinated control weight of a distributed drive electric vehicle. Figure 5 The structure block diagram of the distributed drive electric vehicle coordinated control weight calculation device according to an embodiment of the present application is shown, including:

[0084] The data set construction module 51 is used to construct a vehicle motion state data set; the vehicle motion state data in the vehicle motion state data set are characteristic parameters related to the lateral stability state;

[0085] A category classification module 52 is used to perform cluster analysis on the vehicle motion state data in the vehicle motion state data set, divide the vehicle motion state data into three categories, namely, a stable state category, a transition state category and an unstable state category, and determine the cluster centers of the three categories;

[0086] The distance calculation module 53 is used to calculate the Euclidean distance between the midpoint of the line connecting the cluster center of the stable state category and the cluster center of the transition state category and the cluster center of the stable state category, which is recorded as the first distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the transition state category, which is recorded as the second distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the unstable state category, which is recorded as the third distance; calculate the Euclidean distance between the vehicle motion state data at the current moment and the cluster center of the stable state category, which is recorded as the fourth distance;

[0087] A weight factor determination module 54, for determining a weight factor in an objective function of an AFS / DYC coordination controller according to a relationship among the first distance, the second distance, the third distance, and the fourth distance;

[0088] An objective function building module 55 is used to build an AFS / DYC coordination controller objective function according to a weight factor;

[0089] The solution module 56 is used to solve the objective function of the AFS / DYC coordinated controller to obtain the optimal weight coefficients of the AFS system and the DYC system.

[0090] The distributed drive electric vehicle coordinated control weight calculation device of this embodiment has the same inventive concept as the distributed drive electric vehicle coordinated control weight calculation method mentioned above. Therefore, the specific implementation method of the device can be seen in the implementation example part of the distributed drive electric vehicle coordinated control weight calculation method mentioned above, and its technical effect corresponds to the technical effect of the above method, which will not be repeated here.

[0091] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned method for calculating the coordinated control weight of a distributed drive electric vehicle is implemented.

[0092] An embodiment of the present application provides a computer program product, including a computer program / instruction. When the computer program / instruction is executed by a processor, the above-mentioned distributed drive electric vehicle coordinated control weight calculation method is implemented.

[0093] The above are only various implementations of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for calculating the coordinated control weight of a distributed drive electric vehicle, characterized in that: include: Constructing a vehicle motion state data set; the vehicle motion state data in the vehicle motion state data set are characteristic parameters related to the lateral stability state; Performing cluster analysis on the vehicle motion state data in the vehicle motion state data set, dividing the vehicle motion state data into three categories, namely, a stable state category, a transition state category, and an unstable state category, and determining cluster centers of the three categories; Determine the midpoint of the line connecting the cluster center of the stable state category and the cluster center of the transition state category, and calculate the Euclidean distance between the midpoint and the cluster center of the stable state category, recorded as the first distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the transition state category, recorded as the second distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the unstable state category, recorded as the third distance; calculate the Euclidean distance between the vehicle motion state data at the current moment and the cluster center of the stable state category, recorded as the fourth distance; Determining a weight factor in an objective function of an AFS / DYC coordinated controller according to a relationship between the first distance, the second distance, the third distance, and the fourth distance; Constructing an AFS / DYC coordinated controller objective function according to the weight factors; Solving the objective function of the AFS / DYC coordinated controller to obtain the optimal weight coefficients of the AFS system and the DYC system; According to the relationship between the first distance, the second distance, the third distance and the fourth distance, the weight factor in the objective function of the AFS / DYC coordination controller is determined using the following formula: in, is the weight factor related to the lateral displacement, is the weight factor related to the yaw rate, is the weight factor related to the sideslip angle of the center of mass; is the fourth distance, is the first distance, is the second distance, is the third distance; The objective function of the AFS / DYC coordinated controller is expressed by the following formula: in, is the objective function value, is the weight factor related to the lateral displacement, is the weight factor related to the yaw rate, is the weight factor related to the sideslip angle of the center of mass; is the number of sampling moments, The sampling time lateral displacement deviation; is the maximum value of the lateral displacement deviation, The sampling time The sideslip angle of the center of mass, is the reference center of mass sideslip angle, is the maximum value of the sideslip angle of the center of mass, The sampling time The yaw rate, is the reference yaw rate, is the maximum value of the yaw angular velocity; , , The weight coefficients are determined based on a discrete vehicle motion differential equation, which is related to the weight coefficients of the AFS system.

2. The method according to claim 1, characterized in that The characteristic parameters related to the lateral stability state include: longitudinal speed, steering wheel angle, lateral speed, sideslip angle, roll angle, yaw angular velocity, roll rate, lateral acceleration, front axle load transfer rate and rear axle load transfer rate.

3. The method according to claim 1, characterized in that The KDPC algorithm is used to perform cluster analysis on the vehicle motion state data in the vehicle motion state data set.

4. The method according to claim 1, characterized in that The particle swarm optimization algorithm is used to solve the objective function of the AFS / DYC coordinated controller.

5. A distributed drive electric vehicle coordinated control weight calculation device, characterized in that: include: A data set construction module is used to construct a vehicle motion state data set; the vehicle motion state data in the vehicle motion state data set are characteristic parameters related to the lateral stability state; A category division module, used for performing cluster analysis on the vehicle motion state data in the vehicle motion state data set, dividing the vehicle motion state data into three categories, namely, a stable state category, a transition state category and an unstable state category, and determining cluster centers of the three categories; A distance calculation module is used to determine the midpoint of the line connecting the cluster center of the stable state category and the cluster center of the transition state category, and calculate the Euclidean distance between the midpoint and the cluster center of the stable state category, recorded as the first distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the transition state category, recorded as the second distance; calculate the Euclidean distance between the cluster center of the stable state category and the cluster center of the unstable state category, recorded as the third distance; calculate the Euclidean distance between the vehicle motion state data at the current moment and the cluster center of the stable state category, recorded as the fourth distance; a weight factor determination module, configured to determine a weight factor in an objective function of an AFS / DYC coordination controller according to a relationship among the first distance, the second distance, the third distance, and the fourth distance; An objective function building module, used to build an AFS / DYC coordinated controller objective function according to the weight factor; A solution module, used for solving the objective function of the AFS / DYC coordinated controller to obtain the optimal weight coefficients of the AFS system and the DYC system; The weight factor determination module is further used to determine the weight factor using the following formula: in, is the weight factor related to the lateral displacement, is the weight factor related to the yaw rate, is the weight factor related to the sideslip angle of the center of mass; is the fourth distance, is the first distance, is the second distance, is the third distance; The objective function of the AFS / DYC coordinated controller is expressed by the following formula: in, is the objective function value, is the weight factor related to the lateral displacement, is the weight factor related to the yaw rate, is the weight factor related to the sideslip angle of the center of mass; is the number of sampling moments, The sampling time lateral displacement deviation; is the maximum value of the lateral displacement deviation, The sampling time The sideslip angle of the center of mass, is the reference center of mass sideslip angle, is the maximum value of the sideslip angle of the center of mass, The sampling time The yaw rate, is the reference yaw rate, is the maximum value of the yaw angular velocity; , , The weight coefficients are determined based on a discrete vehicle motion differential equation, which is related to the weight coefficients of the AFS system.

6. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for calculating the coordinated control weight of a distributed drive electric vehicle according to any one of claims 1 to 4 is implemented.

7. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements the method for calculating the coordinated control weight of a distributed drive electric vehicle as described in any one of claims 1 to 4.

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