CN115520176A - Hub type electric automobile coordination control method based on AFS and DYC - Google Patents

Abstract

The invention discloses a hub type electric vehicle coordination control method based on AFS and DYC, belonging to the field of electric vehicle control. The method mainly comprises the following steps: 1. the mass center slip angle observation module and the lateral acceleration sensor transmit the vehicle state information to the state identification module; 2. the state identification module divides a vehicle driving area according to the collected state information; 3. and the coordination control module designs a weight distribution scheme of the AFS controller and the DYC controller according to the judgment result of the state identification module. The main advantages of the invention are: firstly, simultaneously incorporating two variables of a centroid slip angle and a lateral acceleration into vehicle running state judgment; secondly, the coordination control strategy not only ensures the safety and stability of the vehicle, but also improves the riding comfort.

Description

Hub type electric vehicle coordination control method based on AFS and DYC

Technical Field

The invention relates to chassis coordination control of a hub type electric automobile, in particular to vehicle running state judgment and coordination weight design of an AFS (automatic front-end system) controller and a DYC (dynamic gravity center) controller, belonging to the field of automobile active safety control.

Background

It is well known that active safety systems for vehicles play a very important role in reducing traffic accidents, being able to intervene mainly immediately before the vehicle loses control. In recent years, with the development of electronic technology, various new technologies, such as AFS, DYC, ESP, etc., which control yaw motion by controlling lateral force of a vehicle, are being applied to the vehicle to improve driving safety.

Active front steering systems (AFS) refer to improving steering stability by creating additional front wheel steering angles independent of steering wheel steering angle to change the lateral force of the vehicle over the linear range of the vehicle tire lateral force. However, AFS has limited control effectiveness after tire lateral force reaches a threshold, requiring the assistance of other active safety control systems.

Direct yaw moment control (DYC) is a control method of lateral stability of a vehicle, and an additional yaw moment is generated to improve stability of a turning process by using a driving force or braking force difference between left and right wheels by means of a driver manipulation signal and vehicle state information. Although the DYC can still maintain good control capability under the limit working condition, the DYC has great influence on the longitudinal movement of the vehicle, and the problems of vehicle speed reduction, ride comfort influence and the like are caused.

Disclosure of Invention

In order to solve the problem of coordination control of AFS and DYC of electric vehicles, the invention provides a hub type electric vehicle coordination control device based on AFS and DYC, which is used for solving the problem of mutual coupling between AFS and DYC.

The technical scheme of the invention comprises the following parts:

a hub type electric automobile coordination control device based on AFS and DYC mainly comprises a mass center side deflection angle observation module, a lateral acceleration sensor, a state identification module, an AFS module, a DYC module and a coordination control module, wherein,

centroid side slip angle observation module: the system is used for observing the value of the centroid slip angle in real time and sending the value of the centroid slip angle and the derivative thereof to the state identification module;

a lateral acceleration sensor: the system is used for acquiring and detecting the actual lateral acceleration of the vehicle and sending the value to the state identification module;

a state identification module: analyzing the collected values of the centroid slip angle and the derivative thereof and the lateral acceleration, and judging and dividing the vehicle driving area according to the state variables;

a coordination control module: designing the weights of the AFS controller and the DYC controller according to the division result of the state identification module;

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

a DYC module: the additional yaw moment is converted into a braking/driving moment which is distributed to the four wheels.

Further, the hub type electric vehicle coordination control device based on the AFS and the DYC comprises the following steps of:

step 1, constructing a vehicle linear two-degree-of-freedom vehicle dynamics model, and designing an active front wheel steering second-order sliding mode controller based on disturbance observation according to the error between the actual yaw velocity and an ideal value of the actual yaw velocity;

step 2, constructing a nonlinear seven-degree-of-freedom vehicle dynamics model of the vehicle, and designing a new self-adaptive supercoiled direct yaw moment sliding-mode controller according to the error between the actual yaw velocity and an ideal value of the actual yaw velocity;

step 3, constructing a parameter performance index analysis module, and transmitting the actual centroid slip angle and the derivative value thereof observed by the state observer and the real-time value acquired by the lateral acceleration sensor to a state identification module;

step 4, the state identification module strictly and quantitatively analyzes the collected centroid slip angle, derivative value and lateral acceleration value, and judges and divides the vehicle driving area according to the state variables;

step 5, designing a weight distribution coefficient according to the result of dividing the vehicle driving area by the state identification module;

and 6, outputting the weight distribution coefficient to the AFS subsystem and the DYC subsystem to construct a coordination control module.

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:

firstly, constructing the following vehicle linear two-degree-of-freedom model containing disturbance:

wherein beta is the centroid slip angle, omega _r To yaw rate, K _f 、K _r Respectively front and rear wheel side deflection stiffness, m is the overall vehicle mass, V _x For longitudinal vehicle speed, a, b are the distances from the center of mass to the front and rear axles, respectively, delta _f D (t) is the lumped disturbance containing the system uncertainty and the external interference;

obtaining ideal yaw angular velocity omega based on the linear two-degree-of-freedom model _rd The calculation formula of (a) is as follows:

wherein the stability factor

L = a + b, μ is a road adhesion coefficient, and g is a gravitational acceleration;

finally, based on disturbance observation, the active front wheel steering second-order sliding mode controller delta _f Is designed as

Wherein s = ω _r -ω _rd ，

Is the observed value of the centroid slip angle, lambda and alpha are control gains, v is an intermediate variable, m is a parameter to be adjusted,

is to the lumped disturbance

Sign is a sign function.

Further, in the step 1, a second-order sliding mode controller is obtained by the following control algorithm

Wherein x is ₁ 、x ₂ Is a state variable, lambda and alpha are control gains, and m is more than or equal to 2 and is a parameter to be adjusted.

Further, in said step 1, the estimated value of the disturbance

Derived from the following disturbance observation module

Wherein P is an internal state, L ₁ To gain, G ₁ ＝B ₂ ，G ₂ ＝1，F＝A ₂₁ β+A ₂₂ ω _r Sliding variable s = ω _r -ω _rd ，δ _f Is the AFS module front wheel steering input.

Further, in the step 2, a new adaptive supercoil direct yaw moment sliding mode controller design process is as follows:

firstly, constructing a nonlinear seven-degree-of-freedom model of a vehicle:

wherein, I _z Is moment of inertia, omega _r As yaw rate, F _yfl 、F _yfr 、F _yrl 、F _yrr The lateral forces of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel are respectively, a and b are respectively the distances from the center of mass to the front axle and the rear axle, d _f For front wheel track, M _z Delta is the front wheel steering angle for an additional yaw moment;

then, designing a new self-adaptive supercoil direct yaw moment sliding mode controller according to the model:

wherein the content of the first and second substances,

s＝ω _r -ω _rd and v is an intermediate variable,

is an adaptive gain.

Further, in the step 2, the gain is adapted

Is obtained by the following adaptive module

Wherein ρ ₁ 、ρ ₂ 、k ₁ 、k ₂ Is the parameter to be adjusted, x ₁ ＝s，

The adaptation module is used to update the control gain in real time.

Further, in step 3, the parameter performance index analysis module is constructed as follows:

firstly, a centroid side deflection angle observer is constructed, and a two-degree-of-freedom model is rewritten

Based on the above equation, a state equation is constructed

Designing an observer according to a state equation

The estimated value of the centroid slip angle is

Wherein z is ₁ And z ₂ Are respectively a state variable x ₁ 、x ₂ Tracking value of beta ₁ 、β ₂ Is observer gain, h is the upper bound of the lumped disturbance;

then, according to the observed barycenter slip angle and its derivative value, and by means of phase plane method defining barycenter slip angle performance index

Wherein SI represents a performance index, beta,

Respectively a centroid slip angle and a derivative thereof;

then according to the sideThe real-time value obtained from the acceleration sensor defines the performance index (0.22-0.002V) of the lateral acceleration _x )g≤a _y ＜0.67μg，

Wherein, a _y For lateral acceleration, V _x μ is the road adhesion coefficient, and g is the acceleration of gravity, for the longitudinal vehicle speed.

Further, in the step 4, the state identification module strictly and quantitatively analyzes the collected centroid slip angle, derivative value and lateral acceleration value, and judges and divides the vehicle driving area according to the state variables:

(1) Region of saturation

SI > 1 or a _y ≥0.67μg，

(2) Linear region of

SI is less than or equal to 0.8 and a _y ＜(0.22-0.002V _x )g，

(3) Non-linear region

And the rest is the case.

Further, in the step 5, a weight distribution coefficient is designed according to a result of the state identification module dividing the vehicle driving area:

(1) Region of saturation

η _DYC ＝1，

η _AFS ＝0

(2) Non-linear region

η _AFS ＝1-η _DYC

(3) Linear region of

η _DYC ＝0，

η _AFS ＝1

Wherein eta is _AFS 、η _DYC Representing the weights of the AFS controller and DYC controller, respectively.

Further, in the step 6, the weight distribution coefficient is output to the AFS subsystem and the DYC subsystem, and a coordination control module is constructed:

U＝η _AF sδ _f +η _DYC M _z ，

wherein, delta _f For AFS module front wheel steering input, M _z Adding yaw moment to the DYC module.

The invention has the following outstanding effects:

1) In the process of judging the driving state of the vehicle, two state variables of the mass center and the lateral deflection angle are simultaneously taken into consideration, quantitative analysis is carried out on the state variables, weight distribution is carried out according to the result, and the control effect is improved;

2) The proposed coordination control strategy can not only ensure the safety and stability of the vehicle, but also improve the riding comfort.

Drawings

Fig. 1 is a block diagram showing the overall configuration of the control system of the present invention.

FIG. 2 is a graph of lateral wind disturbance versus time for extreme operating conditions.

Fig. 3 is a graph of steering wheel angle over time for extreme operating conditions.

FIG. 4 is a graph of yaw rate over time under extreme operating conditions.

FIG. 5 is a graph of centroid slip angle versus time for extreme operating conditions.

Fig. 6 is a time-dependent change curve of the four-wheel torque of DYC individual control in the extreme condition.

Fig. 7 is a graph showing four-wheel torque with time in the coordinated control under the extreme conditions.

FIG. 8 is a graph of vehicle travel trajectory over time under extreme operating conditions.

Detailed Description

The invention provides a hub type electric automobile coordination control device based on AFS and DYC. In order to make the objects, technical solutions and effects of the present invention clearer and clearer, the technical solutions in the embodiments of the present invention will be described in detail and completely with reference to the drawings in the embodiments of the present invention. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

FIG. 1 is a block diagram of the system of the present invention, which includes a vehicle system, a centroid slip angle observation module, a lateral acceleration sensor, a state identification module, an AFS module, a DYC module, and a coordination control module.

Based on the system, the method for controlling the stability of the automobile under the extreme working condition is explained by adopting Carism and Simulink combined simulation, the autonomously designed complex extreme experimental working condition is selected, the speed of the automobile is 80km/h, the road adhesion coefficient is 0.5, and the simulation duration is 15s.

A hub type electric automobile coordination control device based on AFS and DYC mainly comprises a mass center side deflection angle observation module, a lateral acceleration sensor, a state identification module, an AFS module, a DYC module and a coordination control module, wherein,

centroid side slip angle observation module: the system is used for observing the value of the centroid slip angle in real time and sending the value of the centroid slip angle and the derivative thereof to the state identification module;

lateral acceleration sensor: the system is used for acquiring and detecting the actual lateral acceleration of the vehicle and sending the value to the state identification module;

a state identification module: analyzing the collected values of the centroid slip angle and the derivative thereof and the lateral acceleration, and judging and dividing the vehicle driving area according to the state variables;

a coordination control module: according to the division result of the state identification module, the weights of the AFS controller and the DYC controller are designed;

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

a DYC module: the additional yaw moment is converted into a braking/driving moment which is distributed to the four wheels.

Further, the hub type electric automobile coordination control device based on the AFS and the DYC comprises the following steps:

step 1, constructing a vehicle linear two-degree-of-freedom vehicle dynamics model, and designing an active front wheel steering second-order sliding mode controller based on disturbance observation according to an error between an actual yaw velocity and an ideal value of the actual yaw velocity;

step 2, constructing a nonlinear seven-degree-of-freedom vehicle dynamics model of the vehicle, and designing a new self-adaptive supercoiled direct yaw moment sliding mode controller according to the error between the actual yaw velocity and an ideal value of the actual yaw velocity;

step 3, constructing a parameter performance index analysis module, and transmitting the actual centroid slip angle and the actual centroid slip angle derivative value observed by the state observer and the actual value obtained by the lateral acceleration sensor to the state identification module;

step 4, the state identification module strictly and quantitatively analyzes the collected centroid slip angle, derivative value and lateral acceleration value, and judges and divides the vehicle driving area according to the state variables;

step 5, designing a weight distribution coefficient according to the result of dividing the vehicle driving area by the state identification module;

and 6, outputting the weight distribution coefficient to the AFS subsystem and the DYC subsystem to construct a coordination control module.

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:

firstly, constructing the following vehicle linear two-degree-of-freedom model containing disturbance:

wherein beta is the centroid slip angle, omega _r To yaw rate, K _f 、K _r Respectively front and rear wheel side deflection stiffness, m is the overall vehicle mass, V _x For longitudinal vehicle speed, a, b are the distances from the center of mass to the front and rear axles, respectively, delta _f D (t) is a lumped disturbance comprising system uncertainty and external interference;

obtaining ideal yaw angular velocity omega based on the linear two-degree-of-freedom model _rd The calculation formula of (a) is as follows:

wherein the stability factor

L = a + b, μ is a road adhesion coefficient, and g is a gravitational acceleration;

finally, a second-order sliding mode controller delta for active front wheel steering based on disturbance observation _f Is designed as

Wherein s = ω _r -ω _rd ，

Is the observed value of the centroid slip angle, lambda and alpha are control gains, v is an intermediate variable, m is a parameter to be adjusted,

is to the lumped disturbance

Sign is a sign function.

Further, in the step 1, the second-order sliding mode controller is obtained by the following control algorithm

Wherein x is ₁ 、x ₂ Is a state variable, lambda and alpha are control gains, and m is more than or equal to 2 and is a parameter to be adjusted.

Further, in said step 1, the estimated value of the disturbance

By the action ofLower disturbance observation module

Wherein P is an internal state, L ₁ To gain, G ₁ ＝B ₂ ，G ₂ ＝1，F＝A ₂₁ β+A ₂₂ ω _r Sliding variable s = w _r -w _rd ，δ _f Is the AFS module input.

Further, in the step 2, a new adaptive supercoil direct yaw moment sliding mode controller design process is as follows:

firstly, constructing a nonlinear seven-degree-of-freedom model of a vehicle:

wherein, I _z Is moment of inertia, w _r To yaw angular velocity, F _ufl 、F _yfr 、F _yrl 、F _yrr The lateral forces of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel are respectively, a and b are respectively the distances from the center of mass to the front axle and the rear axle, d _f For front wheel track, M _z Delta is the front wheel steering angle for an additional yaw moment;

then, designing a new self-adaptive supercoil direct yaw moment sliding mode controller according to the model:

wherein the content of the first and second substances,

s＝ω _r -ω _rd and v is an intermediate variable,

is an adaptive gain.

Further, in the step 2, the gain is adapted

Is obtained by the following adaptive module

Where ρ is ₁ 、ρ ₂ 、k ₁ 、k ₂ Is the parameter to be adjusted, x ₁ ＝s，

The adaptation module is used to update the control gain in real time.

Further, in step 3, the parameter performance index analysis module is constructed as follows:

firstly, a centroid side deflection angle observer is constructed, and a two-degree-of-freedom model is rewritten

Based on the above equation, a state equation is constructed

Designing an observer according to a state equation

The estimated value of the centroid slip angle is

Wherein z is ₁ And z ₂ Are respectively state variablesx ₁ 、x ₂ Tracking value of beta ₁ 、β ₂ Is observer gain, h is the upper bound of the lumped disturbance;

then, according to the observed barycenter slip angle and its derivative value, and by means of phase plane method defining barycenter slip angle performance index

Wherein SI represents a performance index, beta,

Respectively, the centroid slip angle and its derivative;

then, according to the real-time value obtained by the lateral acceleration sensor, a performance index (0.22-0.002V) of the lateral acceleration is defined _x )g≤a _y ＜0.67μg，

Wherein, a _y As lateral acceleration, V _x μ is the road adhesion coefficient, and g is the acceleration of gravity, for the longitudinal vehicle speed.

Further, in the step 4, the state identification module strictly and quantitatively analyzes the collected centroid slip angle, derivative value and lateral acceleration value, and judges and divides the vehicle driving area according to the state variables:

(1) Region of saturation

SI > 1 or a _y ≥0.67μg，

(2) Linear region

SI is less than or equal to 0.8 and a _y ＜(0.22-0.002V _x )g，

(3) Non-linear region

And the rest is the case.

Further, in the step 5, a weight distribution coefficient is designed according to a result of the state identification module dividing the vehicle driving area:

(1) Region of saturation

η _DYC ＝1，

η _AFS ＝0

(2) Non-linear region

η _AFS ＝1-η _DYC

(3) Linear region of

η _DYC ＝0，

η _AFS ＝1

Wherein eta is _4FS 、η _DYC Representing the weights of the AFS controller and DYC controller, respectively.

Further, in the step 6, the weight distribution coefficient is output to the AFS subsystem and the DYC subsystem, and a coordination control module is constructed:

U＝η _AFS δ _f +η _DYC M _z ，

wherein, delta _f For AFS module front wheel steering input, M _z Adding yaw moment to the DYC module.

In order to compare the effects of AFS (automatic flight control), DYC (dynamic cruise control) and coordinated control, a simulation platform is established based on Matlab and Carsim software and used for verifying the effectiveness of three kinds of control under the condition of crosswind interference. A simulation experiment was performed on a road surface having an initial speed of 80km/h and a road surface adhesion coefficient of 0.5. FIG. 2 is a graph of lateral wind disturbance versus time for extreme operating conditions. Fig. 3 is a graph of steering wheel angle over time for extreme operating conditions. FIG. 4 is a graph of yaw rate over time under extreme operating conditions. FIG. 5 is a graph of centroid slip angle versus time for extreme operating conditions. Fig. 6 is a time-dependent change curve of the four-wheel torque of DYC individual control in the extreme condition. Fig. 7 is a graph showing four-wheel torque with time in the coordinated control under extreme conditions. FIG. 8 is a graph of vehicle travel trajectory over time under extreme operating conditions.

Through the simulation experiment of extreme operating mode, synthesize, the control effect of coordinated controller is better than AFS independent control and DYC independent control to the torque that distributes four-wheel under the coordinated controller is littleer, under the prerequisite of guaranteeing safety, and is littleer to riding comfort.

Claims (10)

1. A hub type electric automobile coordination control method based on AFS and DYC is characterized by comprising the following steps:

step 1, constructing a vehicle linear two-degree-of-freedom vehicle dynamics model, and designing an active front wheel steering second-order sliding mode controller based on disturbance observation according to an error between an actual yaw velocity and an ideal value of the actual yaw velocity;

step 2, constructing a nonlinear seven-degree-of-freedom vehicle dynamics model of the vehicle, and designing a new self-adaptive supercoiled direct yaw moment sliding-mode controller according to the error between the actual yaw velocity and an ideal value of the actual yaw velocity;

step 3, constructing a parameter performance index analysis module, and transmitting the actual centroid slip angle and the actual centroid slip angle derivative value observed by the state observer and the actual value obtained by the lateral acceleration sensor to the state identification module;

step 4, strictly and quantitatively analyzing the collected mass center slip angle, derivative value and lateral acceleration value by a state identification module, and judging and dividing a vehicle driving area according to the state variables;

step 5, designing a weight distribution coefficient according to the result of dividing the vehicle driving area by the state identification module;

and 6, outputting the weight distribution coefficient to the AFS subsystem and the DYC subsystem to construct a coordination control module.

2. The AFS and DYC based hub type electric vehicle coordination control method according to claim 1, wherein in the step 1, the design process of the second order sliding mode controller for active front wheel steering based on disturbance observation is as follows:

firstly, constructing the following vehicle linear two-degree-of-freedom model containing disturbance:

wherein, I _z Is the moment of inertia, beta is the centroid slip angle, omega _r To yaw rate, K _f 、K _r Respectively front and rear wheel side deflection stiffness, m is the overall vehicle mass, V _x For longitudinal vehicle speed, a, b are the distances from the center of mass to the front and rear axles, respectively, δ _f D (t) is the lumped disturbance containing the system uncertainty and the external interference;

obtaining ideal yaw angular velocity omega based on the linear two-degree-of-freedom model _rd The calculation formula of (a) is as follows:

wherein the stability factor

L = a + b, μ is a road adhesion coefficient, and g is a gravitational acceleration;

finally, based on disturbance observation, the active front wheel steering second-order sliding mode controller delta _f Is designed as

Wherein s = ω _r -ω _rd ，

Is the observed value of the centroid slip angle, lambda and alpha are control gains, v is an intermediate variable, m is a parameter to be adjusted,

is to the lumped disturbance

Sign is a sign function.

3. The perturbed-observation-based active front wheel steering second order sliding-mode controller according to claim 2, wherein the second order sliding-mode controller is derived from the following control algorithm

Wherein x is ₁ 、x ₂ Is a state variable, lambda and alpha are control gains, and m is more than or equal to 2 and is a parameter to be adjusted.

4. The AFS and DYC based hub electric car coordination control method according to claim 2, wherein the estimated value of disturbance

Derived from the following disturbance observation module

Wherein P is an internal state, L ₁ To gain, G ₁ ＝B ₂ ，G ₂ ＝1，F＝A ₂₁ β+A ₂₂ ω _r Sliding variable s = ω _r -ω _rd ，δ _f Is the AFS module front wheel steering input.

5. The coordinated control method for hub electric vehicles based on AFS and DYC as claimed in claim 1, wherein in step 2, a new adaptive supercoiled direct yaw moment sliding mode controller is designed as follows:

firstly, constructing a nonlinear seven-degree-of-freedom model of a vehicle:

wherein, I _z Is moment of inertia, ω _r As yaw rate, F _ufl 、F _ufr 、F _yrl 、F _yrr The lateral forces of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel are respectively, a and b are respectively the distances from the center of mass to the front axle and the rear axle, d _f For front wheel track, M _z Delta is the steering angle of the front wheel for additional yaw moment;

then, a new self-adaptive supercoil direct yaw moment sliding mode controller is designed according to the model:

wherein, the first and the second end of the pipe are connected with each other,

s＝ω _r -ω _rd and v is an intermediate variable, and v is,

is an adaptive gain.

6. The AFS and DYC based hub type electric vehicle coordination control method according to claim 5, wherein the adaptive gain control method is characterized in that

Is obtained by the following adaptive module

Where ρ is ₁ 、ρ ₂ 、k ₁ 、k ₂ Is the parameter to be adjusted, x ₁ ＝s，x ₂ = v, adaptive module is used for real time updateThe gain is controlled newly.

7. The AFS and DYC based hub type electric vehicle coordination control method according to claim 1, wherein in the step 3, the parameter performance index analysis module is constructed as follows:

firstly, a centroid side deflection angle observer is constructed, and a two-degree-of-freedom model is rewritten

Based on the above equation, a state equation is constructed

Designing an observer according to the equation of state

Then the estimated value of the centroid slip angle is

Wherein z is ₁ And z ₂ Are respectively a state variable x ₁ 、x ₂ A tracking value of beta ₁ 、β ₂ For observer gain, h is the upper bound of the lumped disturbance, e ₁ Error representing tracking value and state variable;

then, according to the observed value of the centroid slip angle and the derivative thereof, and by means of a phase plane method, defining the performance index of the centroid slip angle

Wherein SI represents a performance index, beta,

Respectively a centroid slip angle and a derivative thereof;

then, according to the real-time value obtained by the lateral acceleration sensor, a performance index (0.22-0.002V) of the lateral acceleration is defined _x )g≤a _y ＜0.67μg，

Wherein, a _y As lateral acceleration, V _x Mu is the road adhesion coefficient, and g is the acceleration of gravity.

8. The AFS and DYC based hub type electric vehicle coordination control method according to claim 1, wherein in the step 4, the state identification module performs a strict quantitative analysis on the collected centroid slip angle, derivative value thereof and lateral acceleration value, and performs decision division on the vehicle driving area according to the state variables:

(1) Region of saturation

SI > 1 or a _y ≥0.67μg，

(2) Linear region

SI is less than or equal to 0.8 and a _y ＜(0.22-0.002V _x )g，

(3) Non-linear region

And the rest condition.

9. The method as claimed in claim 1, wherein in the step 5, the weight distribution coefficients are designed according to the result of dividing the driving area of the vehicle by the state recognition module:

(1) Region of saturation

η _DYC ＝1，

η _AFS ＝0

(2) Non-linear region

η _AFS ＝1-η _DYC

(3) Linear region

η _DYC ＝0，

η _AFS ＝1

Wherein eta _AFS 、η _DYC Representing the weights of the AFS controller and DYC controller, respectively.

10. The AFS and DYC based hub type electric vehicle coordination control method according to claim 1, wherein in the step 6, the weight distribution coefficients are outputted to the AFS subsystem and the DYC subsystem, so as to construct a coordination control module:

U＝η _AFS δ _f +η _DYC M _z ，

wherein, delta _f For AFS module front wheel steering input, M _z Adding yaw moment to the DYC module.

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