A hub motor electric vehicle coordinated control method based on AFS and DYC

Abstract

The application discloses a kind of based on AFS and DYC's hub electric vehicle coordination control method, belong to electric vehicle control field, the device mainly contains centroid side slip angle observation module, lateral acceleration sensor, state identification module, AFS module, DYC module and coordination control module.Main steps:1, centroid side slip angle observation module and lateral acceleration sensor vehicle state information is passed to state identification module;2, state identification module according to the state information collected, divides vehicle driving area;3, coordination control module according to the determination result of state identification module, designs the weight distribution scheme of AFS controller and DYC controller.The main advantages of the application are: firstly, centroid side slip angle and lateral acceleration two variables are simultaneously included in vehicle driving state determination;Secondly, coordination control strategy not only guarantees vehicle safety stability, but also can improve ride comfort.

Description

TECHNICAL FIELD

[0001] The application relates to a chassis coordinated control of a wheel hub type electric vehicle, in particular to vehicle running state judgment and coordinated weight design of an AFS controller and a DYC controller, and belongs to the field of automobile active safety control. BACKGROUND

[0002] It is known that a vehicle active safety system plays a very important role in reducing traffic accidents and can mainly intervene before the vehicle is about to lose control. In recent years, with the development of electronic technology, various new technologies are continuously applied to vehicles to improve their driving safety, such as AFS, DYC, ESP and the like, which all realize control on yaw motion by controlling lateral force of the vehicle.

[0003] An active front wheel steering system (AFS) refers to that within a linear range of tire lateral force, additional front wheel steering angle independent of steering wheel angle is generated to change the lateral force of the vehicle and improve steering stability. However, the control effect of AFS is limited after the tire lateral force reaches a limit value, and other active safety control systems need to be used at this time.

[0004] Direct yaw moment control (DYC) is a control method for vehicle lateral stability, which uses the difference between driving force or braking force between left and right wheels to generate additional yaw moment to improve stability in the turning process by means of driver's operating signal and vehicle state information. Although DYC can still maintain good control ability in extreme conditions, it has a great impact on the longitudinal motion of the vehicle, causing the vehicle speed to decrease and affecting the passenger comfort and the like. SUMMARY

[0005] In order to solve the coordinated control problem of AFS and DYC of an electric vehicle, the application provides a wheel hub type electric vehicle coordinated control device based on AFS and DYC to solve the mutual coupling problem between AFS and DYC. The device can maximize the function of each subsystem, not only improves the stability of the vehicle running in extreme conditions, but also increases the passenger comfort.

[0006] The technical scheme of the application includes the following parts:

[0007] A wheel hub type electric vehicle coordinated control device based on AFS and DYC, which mainly comprises a centroid side slip angle observation module, a lateral acceleration sensor, a state identification module, an AFS module, a DYC module and a coordinated control module, wherein,

[0008] The centroid side slip angle observation module is used for observing the value of the centroid side slip angle in real time and sending the value of the centroid side slip angle and its derivative to the state identification module;

[0009] Lateral acceleration sensor: used to collect the actual lateral acceleration of the vehicle and send its value to the state identification module;

[0010] State identification module: analyze the values of the collected center of mass side slip angle and its derivative and lateral acceleration, and determine the driving area of the vehicle according to the above state variables;

[0011] Coordination control module: design the weights of AFS controller and DYC controller according to the results of state identification module;

[0012] AFS module: provides an additional front wheel steering angle independent of the driver;

[0013] DYC module: converts additional yaw moment into brake / drive torque distribution to four wheels.

[0014] Further, a hub electric vehicle coordination control device based on AFS and DYC, comprising the following steps:

[0015] Step 1, construct a linear two-degree-of-freedom vehicle dynamics model of the vehicle, and design a second-order sliding mode controller based on disturbance observation according to the error between the actual yaw rate and its ideal value;

[0016] Step 2, construct a nonlinear seven-degree-of-freedom vehicle dynamics model of the vehicle, and design a new adaptive super-spiral direct yaw moment sliding mode controller according to the error between the actual yaw rate and its ideal value;

[0017] Step 3, construct a parameter performance index analysis module, and transfer the values of the actual center of mass side slip angle and its derivative observed by the state observer and the real-time values obtained by the lateral acceleration sensor to the state identification module;

[0018] Step 4, the state identification module strictly quantitatively analyzes the collected values of the center of mass side slip angle and its derivative and lateral acceleration, and determines the driving area of the vehicle according to the above state variables;

[0019] Step 5, design the weight distribution coefficient according to the results of the state identification module for the driving area of the vehicle;

[0020] Step 6, output the weight distribution coefficient to the AFS subsystem and the DYC subsystem to construct the coordination control module.

[0021] Further, in the step 1, the design process of the active front wheel steering second-order sliding mode controller based on disturbance observation is as follows:

[0022] First, construct the following vehicle linear two-degree-of-freedom model containing disturbance:

[0023]

[0024] where β is the centroid side slip angle, ω r is the yaw rate, K f , K r are the front and rear wheel cornering stiffness, m is the vehicle mass, V x is the longitudinal vehicle speed, a, b are the distances from the centroid to the front and rear axles, δ f is the front wheel steering input, d(t) is the lumped disturbance containing system uncertainties and external disturbances;

[0025] Based on the above linear two-degree-of-freedom model, the calculation formula of ideal yaw rate ω rd is as follows:

[0026]

[0027] where the stability factor L = a + b, μ is the road adhesion coefficient, g is the gravitational acceleration;

[0028] Finally, the active front wheel steering second-order sliding mode controller δ f is designed as

[0029]

[0030] where s = ω r - ω rd , is the centroid side slip angle observation value, λ, α are control gains, v is the intermediate variable, m is the parameter to be adjusted, is the estimated value of the lumped disturbance , sign is the sign function.

[0031] Further, in the step 1, the second-order sliding mode controller is obtained from the following control algorithm

[0032]

[0033] where x1, x2 are state variables, λ, α are control gains, m ≥ 2 is the parameter to be adjusted.

[0034] Further, in the step 1, the estimated value of the disturbance is obtained from the following disturbance observation module

[0035] where P is the internal state, L1 is the gain, G1 = B2, G2 = 1, F = A 21 β + A 22 ω r , the sliding variable s = ωr -ω rd δ f Input the front wheel steering angle for the AFS module.

[0036] Furthermore, in step 2, the design process of a novel adaptive superspiral direct yaw moment sliding mode controller is as follows:

[0037] First, construct a nonlinear seven-degree-of-freedom model of the vehicle:

[0038]

[0039] Among them, I z Let ω be the moment of inertia. r F is the yaw rate. yfl F yfr F yrl F yrr Let be the lateral forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; let a and b be the distances from the center of mass to the front and rear axles, respectively; and let d be the lateral forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. f M is the front wheel track. z To add yaw moment, δ is the front wheel steering angle;

[0040] Next, based on the above model, a new adaptive superspiral direct yaw moment sliding mode controller is designed:

[0041]

[0042] in, s=ω r -ω rd υ is an intermediate variable. For adaptive gain.

[0043] Furthermore, in step 2, the adaptive gain Derived from the following adaptive module

[0044]

[0045] Where ρ1, ρ2, k1, and k2 are parameters to be adjusted, and x1 = s. The adaptive module is used to update the control gain in real time.

[0046] Furthermore, in step 3, the parameter performance index analysis module is constructed as follows:

[0047] First, a centroid sideslip angle observer is constructed to rewrite the two-degree-of-freedom model.

[0048]

[0049] Based on the above equations, a state equation is constructed.

[0050]

[0051] Design the observer based on the state equation.

[0052]

[0053] The estimated value of the centroid sideslip angle is

[0054]

[0055] Where z1 and z2 are the tracked values ​​of state variables x1 and x2, respectively, β1 and β2 are the observer gains, and h is the upper bound of the lumped disturbance;

[0056] Next, based on the observed centroid sideslip angle and its derivative, and using the phase plane method, a performance index for the centroid sideslip angle is defined.

[0057]

[0058] Where SI represents the performance index, β, These are the centroid sideslip angle and its derivative;

[0059] Then, based on the real-time values ​​obtained from the lateral acceleration sensor, the performance index of lateral acceleration (0.22-0.002V) is defined. x g≤a y <0.67μg,

[0060] Among them, a y For lateral acceleration, V x denoted as longitudinal vehicle speed, μ as road surface adhesion coefficient, and g as gravitational acceleration.

[0061] Furthermore, in step 4, the state identification module performs rigorous quantitative analysis on the collected centroid sideslip angle, its derivative, and lateral acceleration values, and classifies the vehicle's driving area based on the aforementioned state variables:

[0062] (1) Saturation region

[0063] SI>1 or a y ≥0.67μg,

[0064] (2) Linear region

[0065] SI≤0.8 and a y <(0.22-0.002V) x )g,

[0066] (3) Nonlinear region

[0067] Other cases.

[0068] Further, in the step 5, the weight distribution coefficient is designed according to the result of the vehicle running area division by the state identification module:

[0069] (1) Saturated region

[0070] η DYC = 1,

[0071] η AFS = 0

[0072] (2) Nonlinear region

[0073]

[0074] η AFS = 1-η DYC

[0075] (3) Linear region

[0076] η DYC = 0,

[0077] η AFS = 1

[0078] Wherein, η AFS , η DYC represent the weight of the AFS controller and the DYC controller respectively.

[0079] Further, in the step 6, the weight distribution coefficient is output to the AFS subsystem and the DYC subsystem to build a coordinated control module:

[0080] U = η AF sδ f + η DYC M z ,

[0081] Wherein, δ f is the front wheel angle input of the AFS module, M z is the additional yaw moment of the DYC module.

[0082] The present application has the following outstanding effects:

[0083] 1) In the process of identifying the vehicle running state, the centroid side slip angle and the lateral acceleration are simultaneously considered in the consideration category, and quantitative analysis is carried out, and the weight distribution is carried out according to the result, and the control effect is improved;

[0084] 2) The proposed coordinated control strategy can not only ensure the safety and stability of the vehicle, but also can improve the riding comfort. BRIEF DESCRIPTION OF DRAWINGS

[0085] Figure 1is the overall structure block diagram of the control system of the application.

[0086] Figure 2 is the curve of lateral wind interference with time under extreme working conditions.

[0087] Figure 3 is the curve of steering wheel angle with time under extreme working conditions.

[0088] Figure 4 is the curve of yaw rate with time under extreme working conditions.

[0089] Figure 5 is the curve of center of mass side slip angle with time under extreme working conditions.

[0090] Figure 6 is the curve of four-wheel torque of DYC single control with time under extreme working conditions.

[0091] Figure 7 is the curve of four-wheel torque of coordinated control with time under extreme working conditions.

[0092] Figure 8 is the curve of vehicle trajectory with time under extreme working conditions. DETAILED DESCRIPTION

[0093] The application provides a hub electric vehicle coordinated control device based on AFS and DYC. In order to make the purpose, technical scheme and effect of the application more clear and explicit, the technical scheme in the embodiment of the application will be described clearly and completely in combination with the drawings in the embodiment of the application. It should be understood that the specific embodiments described herein are only used to explain the application and not to limit the application.

[0094] Figure 1 The system structure block diagram of the application is shown, which includes a vehicle system, a center of mass side slip angle observation module, a lateral acceleration sensor, a state identification module, an AFS module, a DYC module and a coordinated control module.

[0095] Based on the above system, the following explains the vehicle stability control method of the application under extreme working conditions by adopting Carism and Simulink joint simulation, selects a complex extreme experimental working condition designed independently, the vehicle speed is 80km / h, the road adhesion coefficient is 0.5, and the simulation duration is 15s.

[0096] A hub electric vehicle coordinated control device based on AFS and DYC, the device mainly contains a center of mass side slip angle observation module, a lateral acceleration sensor, a state identification module, an AFS module, a DYC module and a coordinated control module, wherein,

[0097] Center of mass side slip angle observation module: for observing the value of the center of mass side slip angle in real time, and sending the value of the center of mass side slip angle and its derivative to the state identification module;

[0098] Lateral acceleration sensor: for collecting the actual lateral acceleration of the vehicle and sending its value to the state identification module;

[0099] State identification module: analyzing the collected values of the center of mass side slip angle and its derivative and the lateral acceleration, and determining and dividing the vehicle driving area according to the above state variables;

[0100] Coordination control module: designing the weights of the AFS controller and the DYC controller according to the results of the state identification module;

[0101] AFS module: providing an additional front wheel steering angle independent of the driver;

[0102] DYC module: converting the additional yaw moment into brake / drive torque distribution to the four wheels.

[0103] Further, a hub electric vehicle coordination control device based on AFS and DYC includes the following steps:

[0104] Step 1, constructing a linear two-degree-of-freedom vehicle dynamics model of the vehicle, and designing a second-order sliding mode controller based on disturbance observation according to the error between the actual yaw rate and its ideal value;

[0105] Step 2, constructing a nonlinear seven-degree-of-freedom vehicle dynamics model of the vehicle, and designing a new adaptive super-spiral direct yaw moment sliding mode controller according to the error between the actual yaw rate and its ideal value;

[0106] Step 3, constructing a parameter performance index analysis module, and transmitting the values of the actual center of mass side slip angle and its derivative observed by the state observer and the real-time values obtained by the lateral acceleration sensor to the state identification module;

[0107] Step 4, the state identification module strictly quantitatively analyzes the collected values of the center of mass side slip angle and its derivative and the lateral acceleration, and determines and divides the vehicle driving area according to the above state variables;

[0108] Step 5, designing the weight distribution coefficient according to the results of the state identification module for dividing the vehicle driving area;

[0109] Step 6, outputting the weight distribution coefficient to the AFS subsystem and the DYC subsystem to construct the coordination control module.

[0110] Further, in the step 1, the design process of the active front wheel steering second-order sliding mode controller based on disturbance observation is as follows:

[0111] First, construct the following linear two-degree-of-freedom model of the vehicle containing disturbances:

[0112]

[0113] Where β is the centroid sideslip angle, ω r K is the yaw rate. f K r These represent the lateral stiffness of the front and rear wheels, respectively; m is the vehicle mass; and V is the weight of the vehicle. x Let δ be the longitudinal speed, a and b be the distances from the center of mass to the front and rear axles, respectively. f Let d(t) be the front wheel steering angle, and d(t) be the lumped disturbance that includes system uncertainties and external disturbances.

[0114] Based on the above linear two-degree-of-freedom model, the ideal yaw rate ω is obtained. rd The calculation formula is as follows:

[0115]

[0116] Among them, the stability coefficient L = a + b, μ is the road surface adhesion coefficient, and g is the acceleration due to gravity.

[0117] Finally, the active front wheel steering second-order sliding mode controller δ based on disturbance observation is developed. f Designed for

[0118]

[0119] Where s=ω r -ω rd , Here, λ and α are the observed centroid sideslip angles, v is an intermediate variable, and m is the parameter to be adjusted. It is a lumped disturbance The estimated value, where sign is the sign function.

[0120] Furthermore, in step 1, the second-order sliding mode controller is derived from the following control algorithm.

[0121]

[0122] Where x1 and x2 are state variables, λ and α are control gains, and m≥2 are parameters to be adjusted.

[0123] Furthermore, in step 1, the estimated value of the disturbance... Obtained from the following disturbance observation module

[0124]

[0125] Where P is the internal state, L1 is the gain, G1 = B2, G2 = 1, and F = A 21 β+A 22 ω r Sliding variable s = w r -w rd δ f Input for the AFS module.

[0126] Furthermore, in step 2, the design process of a novel adaptive superspiral direct yaw moment sliding mode controller is as follows:

[0127] First, construct a nonlinear seven-degree-of-freedom model of the vehicle:

[0128]

[0129] Among them, I z For the moment of inertia, w r F is the yaw rate. ufl F yfr F yrl F yrr Let be the lateral forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; let a and b be the distances from the center of mass to the front and rear axles, respectively; and let d be the lateral forces of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. f M is the front wheel track. z To add yaw moment, δ is the front wheel steering angle;

[0130] Next, based on the above model, a new adaptive superspiral direct yaw moment sliding mode controller is designed:

[0131]

[0132] in, s=ω r -ω rd v is an intermediate variable. For adaptive gain.

[0133] Furthermore, in step 2, the adaptive gain Derived from the following adaptive module

[0134]

[0135] Where ρ1, ρ2, k1, and k2 are parameters to be adjusted, and x1 = s. The adaptive module is used to update the control gain in real time.

[0136] Furthermore, in step 3, the parameter performance index analysis module is constructed as follows:

[0137] First, a centroid sideslip angle observer is constructed to rewrite the two-degree-of-freedom model.

[0138]

[0139] Based on the above equations, a state equation is constructed.

[0140]

[0141] Design the observer based on the state equation.

[0142]

[0143] The estimated value of the centroid sideslip angle is

[0144]

[0145] Where z1 and z2 are the tracked values ​​of state variables x1 and x2, respectively, β1 and β2 are the observer gains, and h is the upper bound of the lumped disturbance;

[0146] Next, based on the observed centroid sideslip angle and its derivative, and using the phase plane method, a performance index for the centroid sideslip angle is defined.

[0147]

[0148] Where SI represents the performance index, β, These are the centroid sideslip angle and its derivative;

[0149] Then, based on the real-time values ​​obtained from the lateral acceleration sensor, the performance index of lateral acceleration (0.22-0.002V) is defined. x g≤a y <0.67μg,

[0150] Among them, a y For lateral acceleration, V x denoted as longitudinal vehicle speed, μ as road surface adhesion coefficient, and g as gravitational acceleration.

[0151] Furthermore, in step 4, the state identification module performs rigorous quantitative analysis on the collected centroid sideslip angle, its derivative, and lateral acceleration values, and classifies the vehicle's driving area based on the aforementioned state variables:

[0152] (1) Saturation region

[0153] SI>1 or a y ≥0.67μg,

[0154] (2) Linear region

[0155] SI≤0.8 and a y< (0.22-0.002V x )g,

[0156] (3) Nonlinear region

[0157] The rest of the case.

[0158] Further, in the step 5, according to the state recognition module to the vehicle driving area division result, the weight distribution coefficient is designed:

[0159] (1) Saturation region

[0160] η DYC = 1,

[0161] η AFS = 0

[0162] (2) Nonlinear region

[0163]

[0164] η AFS = 1-η DYC

[0165] (3) Linear region

[0166] η DYC = 0,

[0167] η AFS = 1

[0168] Wherein, η 4FS , η DYC respectively represent the weight of AFS controller and DYC controller.

[0169] Further, in the step 6, the weight distribution coefficient is output to the AFS subsystem and the DYC subsystem, and the coordinated control module is constructed:

[0170] U = η AFS δ f + η DYC M z ,

[0171] Wherein, δ f is the AFS module front wheel angle input, M z is the DYC module additional yaw moment.

[0172] In order to compare the control effect of AFS single control, DYC single control and coordinated control, a simulation platform is built based on Matlab and Carsim software, which is used to verify the effectiveness of three kinds of control under the condition of crosswind interference. The initial speed of the vehicle is set to 80km / h, and the simulation experiment is carried out on the road with the road adhesion coefficient of 0.5.Figure 2 is a curve of lateral wind disturbance under extreme conditions versus time. Figure 3 is a curve of steering wheel angle under extreme conditions versus time. Figure 4 is a curve of yaw rate under extreme conditions versus time. Figure 5 is a curve of center of mass side slip angle under extreme conditions versus time. Figure 6 is a curve of four-wheel torque under DYC individual control under extreme conditions versus time. Figure 7 is a curve of four-wheel torque under coordinated control under extreme conditions versus time. Figure 8 is a curve of vehicle trajectory under extreme conditions versus time.

[0173] Through the simulation experiment of extreme conditions, in summary, the control effect of the coordinated controller is better than that of AFS individual control and DYC individual control, and the torque distributed to the four wheels under the coordinated controller is smaller, which has less influence on the comfort of the passengers under the premise of ensuring safety.

Claims

1. A hub type electric vehicle coordination control method based on AFS and DYC, characterized in that, The method comprises the following steps: Step 1, a linear two-degree-of-freedom vehicle dynamics model of a vehicle is constructed, and a second-order sliding mode controller based on disturbance observation is designed according to the error between an actual yaw rate and an ideal value of the actual yaw rate; Step 2, a nonlinear seven-degree-of-freedom vehicle dynamics model of the vehicle is constructed, and a new adaptive hyper-spiral direct yaw moment sliding mode controller is designed according to the error between the actual yaw rate and the ideal value of the actual yaw rate; Step 3, a parameter performance index analysis module is constructed, and actual values of a mass center side slip angle and a derivative of the mass center side slip angle observed by a state observer and real-time values obtained by a lateral acceleration sensor are transmitted to a state identification module; Step 4, the state identification module strictly quantitatively analyzes the collected values of the mass center side slip angle, the derivative of the mass center side slip angle and lateral acceleration, and determines and divides a vehicle driving region according to the state variables; Step 5, according to the result of the vehicle driving region division of the state identification module, a weight distribution coefficient is designed; Step 6, the weight distribution coefficient is output to an AFS subsystem and a DYC subsystem to construct a coordinated control module.

2. The AFS and DYC-based coordinated control method for in-wheel motor electric vehicles according to claim 1, characterized by, In the step 1, the second-order sliding mode controller based on disturbance observation is designed as follows: Firstly, the following vehicle linear two-degree-of-freedom model containing disturbance is constructed: Among them, I z Let ω be the moment of inertia, β be the sideslip angle of the center of mass, and ω be the rotational inertia. r K is the yaw rate. f K r These represent the lateral stiffness of the front and rear wheels, respectively; m is the vehicle mass; and V is the weight of the vehicle. x Let δ be the longitudinal speed, a and b be the distances from the center of mass to the front and rear axles, respectively. f The input is the front wheel steering angle, and d(t) is the lumped disturbance that includes system uncertainties and external disturbances; Based on the above linear two-degree-of-freedom model, the calculation formula of ideal yaw rate ω rd is as follows: wherein the stability factor L = a + b, μ is the road adhesion factor, and g is the acceleration due to gravity. Finally, the active front-wheel-steering second-order sliding mode controller δ f is designed as where s = ω r - ω rd , is the centred side-slip angle observation, λ, α are control gains, v is an intermediate variable, m is the parameter to be tuned, is an estimate of the lumped disturbance , sign is the sign function.

3. The disturbance-observation-based active front-wheel-steering second-order sliding mode controller according to claim 2, wherein, The second-order sliding mode controller is obtained by the following control algorithm Wherein, x1 and x2 are state variables, λ and α are control gains, and m≥2 is a to-be-adjusted parameter.

4. The AFS and DYC-based coordinated control method for in-wheel motor electric vehicles according to claim 2, characterized by, estimate of the disturbance is derived from a disturbance observation module Where P is the internal state, L1 is the gain, G1 = B2, G2 = 1, F = A 21 β + A 22 ω r , the sliding variable s = ω r - ω rd , δ f is the front wheel angle input to the AFS module.

5. The AFS and DYC-based coordinated control method for in-wheel motor electric vehicles according to claim 1, characterized by, In the step 2, the new adaptive hyper-spiral direct yaw moment sliding mode controller is designed as follows: Firstly, a vehicle nonlinear seven-degree-of-freedom model is constructed: where I z is the moment of inertia, ω r is the yaw rate, F ufl , F ufr , F yrl , F yrr are the lateral forces of the front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively, a, b are the distances from the center of mass to the front and rear axles, d f is the front wheel track, M z is the additional yaw moment, and δ is the front wheel steering angle. Then, a new adaptive hyper-spiral direct yaw moment sliding mode controller is designed according to the model: wherein s = ω r - ω rd v is an intermediate variable, is an adaptive gain.

6. The AFS and DYC-based coordinated control method for in-wheel motor electric vehicles according to claim 5, characterized by, Adaptive gain derived from the following adaptive module Wherein, ρ1, ρ2, k1 and k2 are to-be-adjusted parameters, x1=s and x2=v, and an adaptive module is used to update the control gains in real time.

7. The AFS and DYC-based coordinated control method for in-wheel motor electric vehicles according to claim 1, characterized by, In the step 3, the parameter performance index analysis module is constructed as follows: Firstly, a mass center side slip angle observer is constructed, and the two-degree-of-freedom model is rewritten as Based on the equation, a state equation is constructed According to the state equation, an observer is designed Then, the estimated value of the mass center side slip angle is Wherein, z1 and z2 are tracking values of the state variables x1 and x2 respectively, β1 and β2 are observer gains, h is an upper limit of the lumped disturbance, and e1 represents the error between the tracking value and the state variable; Then, according to the observed values of the mass center side slip angle and the derivative of the mass center side slip angle, and by means of a phase plane method, a mass center side slip angle performance index is defined where SI represents the performance index, β, are the centroid side-slip angle and its derivative, respectively; Then, from the real-time value acquired by the lateral acceleration sensor, a performance index of lateral acceleration (0.22 - 0.002V x ) g ≤ a y < 0.67 μg, where a y is the lateral acceleration, V x is the longitudinal vehicle speed, μ is the road adhesion coefficient, and g is the gravitational acceleration.

8. The AFS and DYC-based coordinated control method for in-wheel motor electric vehicles according to claim 1, characterized by, In the step 4, the state identification module strictly quantitatively analyzes the collected values of the mass center side slip angle, the derivative of the mass center side slip angle and lateral acceleration, and determines and divides a vehicle driving region according to the state variables: (1) a saturation region SI > 1 or a y ≥ 0.67 pg, (2) a linear region SI < 0.8 and a y < 0.22 - 0.002V x ) g, (3) a nonlinear region and the rest.

9. The AFS and DYC-based coordinated control method for in-wheel motor electric vehicles according to claim 1, characterized by, In the step 5, according to the result of the vehicle driving region division of the state identification module, a weight distribution coefficient is designed: (1) a saturation region η DYC = 1, η AFS = 0 (2) a nonlinear region η AFS = 1 - η DYC (3) a linear region η DYC = 0, η AFS = 1 wherein η AFS , η DYC represent the weights of the AFS controller and the DYC controller, respectively.

10. The AFS and DYC-based coordinated control method for in-wheel motor electric vehicles according to claim 1, characterized by, In the step 6, the weight distribution coefficient is output to an AFS subsystem and a DYC subsystem to construct a coordinated control module: U = η AFS δ f + η DYC M z , where δ f is the AFS module front wheel angle input, M z is the DYC module additional yaw moment.

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