CN109291932B

CN109291932B - Feedback-based electric vehicle yaw stability real-time control device and method

Info

Publication number: CN109291932B
Application number: CN201811201424.9A
Authority: CN
Inventors: 袁小芳
Original assignee: Zhejiang Dongfang Electromechanical Co ltd
Current assignee: ZHEJIANG DONGFANG ELECTROMECHANICAL Co.,Ltd.
Priority date: 2018-10-16
Filing date: 2018-10-16
Publication date: 2021-03-12
Anticipated expiration: 2038-10-16
Also published as: CN109291932A

Abstract

The invention discloses a feedback-based real-time control device and method for yaw stability of an electric automobile, wherein the device comprises an upper layer controller and a lower layer controller, wherein the upper layer controller is provided with a sensor unit, a model prediction controller and a direct yaw moment calculation unit; the lower layer controller is provided with a judgment decision unit and a direct yaw moment execution unit, and the direct yaw moment execution unit controls the driving moment of the target wheel. The method adopts a layered control structure, required signals are provided by sensors, measurable quantities are utilized, a sensor real-time feedback and model predictive control algorithm is adopted to design an upper controller, and a direct yaw moment required for maintaining yaw stability in the whole vehicle movement process is calculated. The lower-layer controller ensures that a direct yaw moment instruction sent by the upper-layer controller is realized by distributing driving torque, the vehicle can be ensured to stably run under different working conditions, and the yaw stability of the electric vehicle is improved.

Description

Feedback-based electric vehicle yaw stability real-time control device and method

Technical Field

The invention relates to the technical field of electric automobiles and stability control thereof, in particular to a sensor feedback-based real-time control method for yaw stability of a distributed driving electric automobile, and particularly relates to a feedback-based real-time control device and method for yaw stability of an electric automobile.

Background

The development of electric vehicles is becoming more and more popular today with increasingly severe energy shortage and increasingly severe environmental pollution. The distributed driving electric automobile is provided with four independent hub motors, meanwhile, the response speed of the motors is high, and parameters such as torque and rotating speed are easy to obtain. Therefore, the driving torque of each wheel can be directly and independently controlled accurately to improve the running performance of the electric automobile under the condition of a bad road surface.

The yaw stability control of the vehicle is very important for the distributed drive electric automobile, and the yaw stability control has the main functions of ensuring the stability and controllability of the vehicle during turning, braking and driving, assisting a driver to control the vehicle under the condition of extreme operation and preventing the vehicle from over-steering or under-steering.

Aiming at the problem of yaw stability control of a distributed driving electric automobile, domestic and foreign scholars propose various control methods, including a torque distribution method based on wheel slip rate, a beta method, a differential torque distribution method and the like, wherein the methods are mainly realized by an estimation strategy of the wheel slip rate or a mass center slip angle. However, the running parameters such as the slip rate and the centroid slip angle cannot be accurately acquired through the sensors and effective real-time feedback is established, so that the yaw stability control precision is low. On the other hand, the control of the yaw stability needs to take the constraint conditions of the vehicle, such as the maximum output torque of the motor, the safety constraint of the vehicle and the like into consideration, and the traditional algorithm has difficulty in meeting the requirement of the multi-target constraint optimization control difficulty.

Disclosure of Invention

An object of the present invention is to solve at least the above problems and to provide at least the advantages described later.

The application aims to provide a sensor feedback-based real-time control method for yaw stability of a distributed driving electric automobile, so as to solve the problem of accurate control of yaw stability in the driving process of the distributed driving electric automobile.

The method adopts a layered control structure, required signals are provided by sensors, measurable quantities are utilized, a sensor real-time feedback and model predictive control algorithm is adopted to design an upper controller, and a direct yaw moment required for maintaining yaw stability in the whole vehicle movement process is calculated. The lower-layer controller ensures that a direct yaw moment instruction sent by the upper-layer controller is realized by distributing driving torque, the vehicle can be ensured to stably run under different working conditions, and the yaw stability of the electric vehicle is improved.

To achieve these objects and other advantages in accordance with the present invention, there is provided a feedback-based yaw stability real-time control apparatus for an electric vehicle, comprising:

the upper controller is provided with three parts, namely a sensor unit, a model prediction controller and a direct yaw moment calculation unit, wherein the output end of the sensor unit is connected with the input end of the model prediction controller, and the output end of the model prediction controller is connected with the input end of the direct yaw moment calculation unit;

and the lower layer controller is provided with two parts, namely a judgment decision unit and a direct yaw moment execution unit, wherein the input end of the judgment decision unit is connected with the output end of the sensor unit, the input end of the direct yaw moment execution unit is respectively connected with the output ends of the judgment decision unit and the direct yaw moment calculation unit, and the direct yaw moment execution unit controls the driving moment of the target wheel.

Preferably, the sensor unit is configured to collect vehicle motion information, and the vehicle motion information at least includes a vehicle speed and a measured wheel angular velocity ω'_iLateral acceleration and steering wheel angle.

Preferably, the model predictive controller receives the output signals of the sensor units and is used to calculate an active steering angle δ that generates a signal to maintain yaw stability of the vehicle_fAnd calculating the wheel angular velocity omega_iAnd i ═ fl, fr, rl, rr, where fl denotes front left, fr denotes front right, rl denotes back left, and rr denotes back right.

Preferably, the direct yaw moment calculation unit receives an output signal of the model predictive controller and calculates a desired direct yaw moment required to secure yaw stability of the vehicle.

Preferably, the judgment and decision unit receives the vehicle motion information collected by the sensor unit, and judges the state of the vehicle and selects the target wheel which ensures the most effective vehicle stability according to the stability range required by the vehicle to keep stable and the vehicle state-judgment decision corresponding information, wherein the stability range is related to the vehicle yaw rate and the mass center slip angle.

Preferably, the direct yaw moment performing unit receives the direct yaw moment calculated by the direct yaw moment calculating unit, converts it into the driving moment of the target wheel, and performs it, thereby controlling the vehicle yaw rate and the center-of-mass slip angle to be maintained in stable ranges.

A stability control method of an electric vehicle comprises the following steps:

step one, collecting vehicle motion information and establishing real-time feedback of wheel angular speed;

step two, calculating and generating an active steering angle delta for keeping the yaw stability of the vehicle according to the vehicle motion information_fAnd calculating the wheel angular velocity omega_i；

Step three, obtaining the wheel angular velocity omega according to calculation_iCalculating and generating an expected direct yaw moment;

judging the vehicle state according to the vehicle motion information, and selecting a target wheel which is most effective in correcting excessive or insufficient steering;

and step five, converting the expected direct yaw moment into a driving moment and acting on the target wheels.

The invention at least comprises the following beneficial effects:

the sensor feedback-based real-time control method for the yaw stability of the distributed driving electric automobile provided by the invention utilizes the characteristic that the hub motor of the distributed driving electric automobile is independently controllable, the sensor feeds back accurate operation parameter information in real time and the capability of model prediction control processing multi-target constraint, the real-time accurate control of the yaw stability of the electric automobile is realized through the design of a direct yaw moment controller, the effect of actively controlling the deflection of an automobile body is achieved, and the yaw stability of the automobile under the limit working condition is ensured.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.

Drawings

FIG. 1: the control principle block diagram of the invention;

FIG. 2: a schematic block diagram of an upper layer controller;

FIG. 3: a schematic diagram of a vehicle body dynamics model;

FIG. 4: a schematic view of a tire model;

FIG. 5: a model predictive control schematic block diagram;

reference numbers in the figures: delta-input vehicle steering angle, v-vehicle speed, gamma_r-desired yaw rate, β_r-desired centroid slip angle, β -centroid slip angleγ -yaw rate, a_x-longitudinal acceleration of the vehicle, a_y-vehicle lateral acceleration, d-vehicle wheelbase, O-vehicle center of gravity position, ρ -vehicle pitch angle, M_zVehicle yaw moment, δ_f-vehicle steering angle, L, output by the controller_rDistance from center of gravity of vehicle to axle center of rear wheel, L_fDistance from center of gravity of vehicle to axle center of front wheel, v_x-vehicle longitudinal speed, α -tire slip angle. Omega_flLeft front wheel angular velocity, ω_frRight front wheel angular velocity, ω_rlLeft rear wheel angular velocity, ω_rrRight rear wheel angular velocity, T_dfl-direct yaw moment of the left front wheel, T_dfr-direct yaw moment of the right front wheel, T_drl-direct yaw moment of the left rear wheel, T_drr-direct yaw moment of the right rear wheel, T_eflLeft front wheel drive torque, T_efrRight front wheel drive torque, T_erlLeft rear wheel drive torque, T_errRight rear wheel drive torque, T_cflLeft front wheel output Torque, T_cfr-right front wheel output torque, T_crlLeft rear wheel output Torque, T_crr-right rear wheel output torque. F_yflLeft front wheel lateral force, F_yfr-right front wheel lateral force, F_yrl-left rear wheel lateral force, F_yrr-right rear wheel transverse force, F_xflLeft front wheel longitudinal force, F_xfr-right front wheel longitudinal force, F_xrl-longitudinal force of the left rear wheel, F_xrr-right rear wheel longitudinal force, F_z-vertical component of force of the vehicle on the wheel, minJ-rolling optimization objective function.

Detailed Description

The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.

It will be understood that terms such as "having," "including," and "comprising," as used herein, do not preclude the presence or addition of one or more other elements or groups thereof.

As shown in FIG. 1, the feedback-based yaw stability real-time control device for the electric vehicle comprises an upper layer controller 1 and a lower layer controller 2, wherein the upper layer controller 1 and the lower layer controller 2 are separated, and the transmission of the vehicle state is completed through a signal transmission line.

As shown in fig. 2, the upper controller 1 is provided with three parts, a sensor unit 3, a model predictive controller 4 and a direct yaw moment calculation unit 5, wherein the output end of the sensor unit 3 is connected with the input end of the model predictive controller 4, and the output end of the model predictive controller 4 is connected with the input end of the direct yaw moment calculation unit 5.

The sensor unit 3 is used for collecting vehicle motion information, and the vehicle motion information at least comprises vehicle speed and measured wheel angular speed omega'_iThe lateral acceleration and the steering wheel angle, and provides real-time feedback wheel angular speed information, and the steering angle delta of the input vehicle can be obtained through the steering wheel angle information.

The model predictive controller 4 mainly solves the multi-objective constraint optimization problem and calculates the angular velocity values of the four wheels according to a reference model. The model predictive controller 4 receives the output signals of the sensor units 3 and is used to calculate the active steering angle δ that generates the vehicle yaw stability_fAnd calculating the wheel angular velocity omega_iAnd i ═ fl, fr, rl, rr, where fl denotes front left, fr denotes front right, rl denotes back left, and rr denotes back right. Wherein the active steering angle delta_fI.e. the vehicle steering angle, the active steering angle delta, output by the controller_fDistinguished from the input vehicle steering angle delta, active steering angle delta_fIs generated by a controller, the input vehicle steering angle delta is a driver input, and the active steering angle delta_fFor correcting the input vehicle steering angle delta to improve the accuracy of the vehicle steering angle. Simultaneously measuring the angular speed omega of the wheel'_iAnd the wheel angular velocity omega_iAnd comparing to eliminate the error between the two, so as to improve the accuracy of the angular speed of the wheel.

The direct yaw moment calculation unit 5 receives the output signal of the model predictive controller 4 and calculates a desired direct yaw moment required to ensure yaw stability of the vehicle.

The lower level controller 2 is provided with two parts, a decision-making judgment unit 23 and a direct yaw moment execution unit 24, the input of the decision-making judgment unit 23 is connected to the output of the sensor unit 3, the input of the direct yaw moment execution unit 24 is connected to the outputs of the decision-making judgment unit 23 and the direct yaw moment calculation unit 5, respectively, and the direct yaw moment execution unit 24 controls the driving moment of the target wheels.

Fig. 3 is a schematic diagram of a vehicle body dynamic model, and the linear eight-degree-of-freedom dynamic model is composed of a vehicle model and a tire model.

The vehicle model is as follows:

tire model:

the vehicle model is obtained by derivation of a magic equation (3), front and rear wheel side slip angles and wheel moment of inertia.

Magic equation:

F_yi＝-D_ysin(C_yarctan(B_yα_i-E_y(B_yα_i-arctanB_yα_i))) (3)

front and rear wheel side slip angle calculation formula:

wheel moment of inertia formula:

wherein beta represents the centroid slip angle, gamma represents the vehicle body yaw angular velocity, F_yl、F_yrRespectively representing the lateral force of a left tire and a right tire, m representing the mass of the whole vehicle, v representing the running speed of the vehicle, and L_fIndicating the vertical distance, L, of the center of gravity of the vehicle from the axle of the front wheel_rIndicating the vertical distance of the center of gravity of the vehicle from the axle of the front wheel, I_zRepresenting the moment of inertia of the tire and δ representing the input vehicle steering angle.

By yaw moment M of the vehicle_zKinetic equation (7) was derived.

Yaw moment M of vehicle_zThe calculation formula of (2) is as follows:

the kinetic equation is:

wherein d represents the vehicle wheelbase, F_xfl、F_xfr、F_xrl、F_xrrThe longitudinal forces of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel are respectively expressed, i ═ fl, fr, rl, rr, L_f、L_rRespectively representing the distance from the center of gravity of the vehicle to the front wheel and the center of the rear wheel axle, M_iRepresenting direct yaw moment, J representing wheel moment of inertia, r representing tire radius, delta representing input vehicle steering angle, F_xiRepresenting the longitudinal force, T, produced by a direct yaw moment_eiRepresenting the drive torque applied to the wheels.

As shown in fig. 4, a and b represent a tire model analyzed from the lateral direction and the longitudinal direction, respectively, and the tire model is composed of six parts of magic equation (3), Tayor expansion, front and rear wheel slip angles, wheel moment of inertia, wheel angular velocity and vertical force.

The Tayor expansion formula is:

the wheel angular velocity calculation formula is as follows:

the vertical force calculation formula is as follows:

the magic equation considers the interaction between longitudinal and lateral forces, which depend on vertical forces, slip angle and slip rate, where F_yiRepresenting lateral forces, applied vertically by the vehicle F_zAnd the wheel slip angle alpha_iDenotes i ═ fl, fr, rl, rr. Simplifying the magic model by the Tayor expansion, maintaining its non-linear characteristics using the method of Tayor expansion, where C_f、C_rRespectively representing the front wheel and rear wheel rotor stiffness.

Wherein, delta_fIndicating the steering angle, k, of the tire output from the controller_a、k_bThe fitting coefficients are represented. J represents the moment of inertia of the wheel, T_eRepresenting motor drive torque, r representing tire radius, k_iIndicates the slip ratio of the wheel, ω indicates the wheel angular velocity, i ═ fl, fr, rl, rr,

F_z0＝4000N，p_k1，p_k2，p_k3respectively represent 19.061, -0.466168,0.483251, C_kiFrom F_ziAnd calculating. F_zfl、F_zfr、F_zrl、F_zrrRespectively representing the vertical acting force of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel, g represents the gravity acceleration, h_cgVertical height of center of gravity from ground, a_xRepresenting the longitudinal acceleration of the vehicle, a_yIndicating the vehicle lateral acceleration. The wheel angular velocity (9) is derived from the moment of inertia (5) of the wheel.

Fig. 5 shows a schematic block diagram of model predictive control, which mainly comprises a prediction model 17, a rolling optimization 18 and a feedback correction 19, wherein the prediction model 17 designs a nonlinear state space equation (11) of a continuous time system according to a linear eight-degree-of-freedom dynamic model, and the rolling optimization 18 is composed of a quadratic objective function (12).

Specifically, the model predictive control 4 adopts a discrete time-based state space equation as a predictive model 17, and calculates an active steering angle delta for keeping the yaw stability of the vehicle through rolling optimization 18 and feedback correction 19_fAnd calculating the wheel angular velocity omega_i,i＝fl,fr,rl,rr。

Nonlinear state space equation:

quadratic objective function:

where i ═ fl, fr, rl, rr, T_sRepresenting sample times, r (k) representing reference trajectories, y (k) representing prediction outputs, Q, R, S representing weight values, respectively. u (k) denotes a control variable T_ci，u₁For the steering angle of the front wheels, u₂-u₅Is wheel torque, x₁(k) Representing the centroid slip angle beta, x₂(k) Representing yaw rate gamma, x₃(k)、x₄(k)、x₅(k)、x₆(k) Respectively representing the angular velocity omega of the left front wheel_flRight front wheel angular velocity ω_frAngular velocity ω of left rear wheel_rlRight rear wheel angular velocity ω_rr。

The feedback correction 19 consists of a closed loop feedback, and the active steering angle δ is calculated from a reference model_fAnd wheel angular velocity omega_iI ═ fl, fr, rl, rr. The model predictive controller 4 adopted by the invention solves the multi-target complex optimization control problems of yaw angular velocity, mass center and side deviation angle and the like, treats the active steering angle as time domain constraint, and effectively realizes the vehicleThe method comprises the following steps of carrying out compromise optimization between vehicle stability and the performance of the whole vehicle, constructing a cost function, and carrying out optimization solution to obtain optimized angular velocity signals of four wheels, wherein the cost function is considered to mainly comprise three aspects including: vehicle stability (preventing oversteer or understeer), driving comfort (the wheel angular velocity cannot vary too much), operational economy (saving energy while meeting performance).

As shown in fig. 2, the vehicle direct yaw moment is determined by the vehicle angular velocity, specifically, the linear eight-degree-of-freedom steering dynamics model determines the magnitudes of the four wheel angular velocities, and the direct yaw moment relational expression (13) in the direct yaw moment calculation means 5 calculates the direct yaw moment to be implemented from the wheel angular velocities.

Specifically, the direct yaw moment relational expression in the direct yaw moment calculation unit 5 calculates the expected direct yaw moment of the four wheels based on the four wheel angular velocities output from the model predictive controller 4 and the wheel angular velocity information fed back in real time by the sensor unit 3. And the stability range constructed by the yaw angular velocity and the centroid slip angle is a judgment basis of the vehicle stable state.

Direct yaw moment relation:

in the formula: m_iRepresenting direct yaw moment, ω_iIndicating angular velocity of the wheel, D_f、D_rIndicating front and rear track widths, L_f、L_rRespectively representing the distance from the center of gravity of the vehicle to the front wheel and the center of the rear wheel axle, F_xiDenotes a longitudinal force, F_yiRepresenting a lateral force.

Vehicle steady state is characterized by a stable range of yaw rate and centroid slip angle.

The stability range characterization formula is:

in the formula: e₁And E₂To stabilize the boundary constant, β is the centroid slip angle, γ is the yaw rate, μ is the ground adhesion coefficient, and v is the vehicle speed.

When the inequality is established, the vehicle state is considered to be stable, and when the inequality is not established, the vehicle will have a tendency to understeer or oversteer.

As shown in fig. 1, the lower level controller 2 is provided with two parts, a judgment decision unit 23 and a direct yaw moment execution unit 24, with the objective of ensuring that the direct yaw moment command issued by the upper level controller 1 is realized by the distribution of the driving torque. The decision-making judging unit 23 accurately judges the vehicle state according to the vehicle motion information such as the vehicle speed, the wheel angular velocity, the lateral acceleration, the steering wheel angle and the like acquired by the sensor unit 3, the stability range and the decision-making table, further makes a decision to select the wheel which is most effective in correcting excessive or insufficient steering, and the direct yaw moment executing unit 24 calculates the direct yaw moment M in the direct yaw moment relational expression in the upper layer controller 1_iAnd the i-fl, fr, rl and rr are converted into driving torque through a dynamic equation and act, so that the yaw velocity and the mass center slip angle are limited in a stable region, and the stability of the vehicle is maintained.

TABLE 1 decision table

Note: in the table, the physical quantities are all + counterclockwise and-clockwise.

The stability control method specifically comprises the following steps:

step one, collecting vehicle motion information and establishing real-time feedback of wheel angular speed; the vehicle motion information includes vehicle speed, wheel angular velocity, lateral acceleration, steering wheel angle, and the like.

Step two, modelThe predictive controller 4 calculates and generates an active steering angle δ that maintains yaw stability of the vehicle from the vehicle motion information_fAnd calculating the wheel angular velocity omega_i；

Step three, the direct yaw moment calculation unit 5 calculates and generates an expected direct yaw moment according to the calculated wheel angular speed;

and step five, the direct yaw moment execution unit 24 receives the direct yaw moment calculated by the direct yaw moment calculation unit 5, converts the direct yaw moment into the driving moment of the target wheel and implements the driving moment, and further controls the yaw rate and the centroid slip angle of the vehicle to be kept in a stable range.

According to the sensor feedback-based real-time control method for the yaw stability of the distributed driving electric automobile, the characteristics that the hub motor of the distributed driving electric automobile is independently controllable are utilized, the sensor feeds back accurate operation parameter information in real time and the model predictive control processing capacity of multi-target constraint, the real-time accurate control of the yaw stability of the electric automobile is realized through the design of the direct yaw moment controller, the effect of actively controlling the deflection of the automobile body is achieved, and the yaw stability of the automobile under the limit working condition is ensured.

While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.

Claims

1. The utility model provides an electric automobile yaw stability real-time control device based on feedback which characterized in that includes:

the upper controller (1) is provided with three parts, namely a sensor unit (3), a model prediction controller (4) and a direct yaw moment calculation unit (5), wherein the output end of the sensor unit (3) is connected with the input end of the model prediction controller (4), and the output end of the model prediction controller (4) is connected with the input end of the direct yaw moment calculation unit (5);

a lower layer controller (2) which is provided with two parts, namely a judgment decision unit (23) and a direct yaw moment execution unit (24), wherein the input end of the judgment decision unit (23) is connected with the output end of the sensor unit (3), the input end of the direct yaw moment execution unit (24) is respectively connected with the output ends of the judgment decision unit (23) and the direct yaw moment calculation unit (5), and the direct yaw moment execution unit (24) controls the driving moment of a target wheel;

the direct yaw moment calculation unit (5) receives the output signal of the model predictive controller (4) and calculates an expected direct yaw moment required to ensure yaw stability of the vehicle;

the model predictive controller (4) receives the output signals of the sensor units (3) and is used for calculating and generating an active steering angle delta for maintaining the yaw stability of the vehicle_fAnd calculating the wheel angular velocity omega_iI ═ fl, fr, rl, rr, where fl denotes front left, fr denotes front right, rl denotes back left, rr denotes back right; the judgment decision unit (23) receives the vehicle motion information collected by the sensor unit (3), judges the state of the vehicle according to the stability range required by the vehicle to keep stable and the corresponding information of the vehicle state-judgment decision, and selects the target wheel which ensures the most effective vehicle stability, wherein the stability range is related to the vehicle yaw rate and the mass center side slip angle; the direct yaw moment execution unit (24) receives the expected direct yaw moment calculated by the direct yaw moment calculation unit (5), converts the expected direct yaw moment into the driving moment of the target wheel and implements the driving moment, and then controls the vehicle yaw speed and the centroid slip angle to be kept in a stable range;

when i ═ fl, fr, the direct yaw moment relationship of the front left and right wheels:

when i ═ rl, rr, D in the above formula_f、L_fIs replaced by D_r、L_rObtaining a direct yaw moment relational expression of the left rear wheel and the right rear wheel;

in the formula: m_iRepresenting direct yaw moment, ω_iIndicating angular velocity of the wheel, D_f、D_rIndicating front and rear track widths, L_f、L_rRespectively representing the distance from the center of gravity of the vehicle to the front wheel and the center of the rear wheel axle, F_xiDenotes a longitudinal force, wherein F_xfl、F_xfr、F_xrl、F_xrrRespectively showing the longitudinal forces of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel, F_yiDenotes a transverse force, wherein F_yfl、F_yfr、F_yrl、F_yrrRespectively, the lateral forces of the left front wheel, the right front wheel, the left rear wheel and the right rear wheel, delta represents the steering angle of the input vehicle, k_iExpressing the slip rate of the wheel, r the radius of the tire, v the running speed of the vehicle, and the formula is shown as i-fl, fr, rl, rr;

the model prediction controller (4) comprises a prediction model (17), a rolling optimization (18) and a feedback correction (19), wherein the prediction model (17) designs a nonlinear state space equation of a continuous time system according to a linear eight-degree-of-freedom dynamic model, the rolling optimization (18) is composed of a quadratic objective function, the model prediction controller (4) adopts a discrete time-based state space equation as the prediction model (17), and an active steering angle delta for keeping the yaw stability of the vehicle is calculated through links of the rolling optimization (18) and the feedback correction (19)_fAnd calculating the wheel angular velocity omega_iI ═ fl, fr, rl, rr; wherein, the nonlinear state space equation:

quadratic objective function:

wherein, T_sRepresenting sample time, r (k) representing reference trajectory, y (k) representing prediction output, Q, R, S representing weight values, respectively; u (k) denotes a control variable T_ci，u₁For the steering angle of the front wheels, u₂-u₅Is wheel torque, x₁(k) Representing the centroid slip angle beta, x₂(k) Representing yaw rate gamma, x₃(k)、x₄(k)、x₅(k)、x₆(k) Respectively representing the angular velocity omega of the left front wheel_flRight front wheel angular velocity ω_frAngular velocity ω of left rear wheel_rlRight rear wheel angular velocity ω_rr(ii) a m represents the vehicle mass, k represents the sampling point, F_yf、F_yrRespectively representing the front and rear tire lateral forces, M_zRepresenting vehicle yaw moment, I_zRepresenting the moment of inertia of the tire, J representing the moment of inertia of the wheel, minJ representing the roll optimization objective function, C_kiFrom F_ziCalculated to be F_ziIndicating a vertical force.

2. Feedback-based yaw stability real-time control device for electric vehicles according to claim 1, characterized in that the sensor unit (3) is adapted to collect vehicle motion information comprising at least vehicle speed, measured wheel angular velocity ω'_iLateral acceleration and steering wheel angle.

3. A control method of the feedback-based yaw stability real-time control device of the electric vehicle according to claim 2, characterized by comprising the following steps: