Adaptive control method for steering stability and energy efficiency of four-wheel independent drive electric vehicle

Abstract

The application discloses a kind of four-wheel independent drive electric vehicle steering stability and energy-saving adaptive control method.Method includes: the time domain regulation model based on steering stability energy-saving evaluation parameter is established, and after real-time adjustment by fuzzy controller, expected level parameter and battery remaining capacity obtain adaptive coefficient;When the phase plane of mass side slip angle is in stable region, adaptive coefficient and level parameter are input into model and processed, and then output real-time change prediction time domain and control time domain, and as the time domain of MPC algorithm, expected mass side slip angle and yaw angular velocity are processed by improved MPC algorithm, and then output additional yaw moment, and then adaptive control is carried out to electric vehicle.The method of the application utilizes inherent parameters and real-time parameters of the whole vehicle, combines phase plane analysis and fuzzy controller, realizes variable time domain control of MPC algorithm, so as to coordinate stability and energy-saving, can obtain minimized additional yaw moment, and achieve the purpose of coordinating stability and energy-saving.

Description

TECHNICAL FIELD

[0001] The application relates to a control method for an electric vehicle, in particular to a steering stability and energy saving adaptive control method for a four-wheel independent drive electric vehicle. BACKGROUND

[0002] Steering stability is an unavoidable problem for vehicle driving safety, and maximum driving mileage is a key index. Therefore, coordination of stability and energy saving is a research focus at present. Among numerous electric vehicle power system layouts, a four-wheel independent drive electric vehicle can realize complex dynamic control and has outstanding advantages in stability and energy saving control.

[0003] In steering stability control, upper and lower hierarchical control is usually adopted to reduce the dimension of optimization strategies. An upper controller adopts direct yaw moment control to obtain an additional yaw moment by tracking an ideal state, and a lower controller adopts an optimal torque distribution algorithm to ensure that the vehicle reaches the expected steering control. How to output appropriate additional yaw moment through the upper controller becomes a key to coordination of stability and energy saving. SUMMARY

[0004] In order to solve the problems in the background art, the application provides a steering stability and energy saving adaptive control method for a four-wheel independent drive electric vehicle. The method realizes variable time domain control of an MPC strategy by using inherent parameters and real-time parameters of the whole vehicle, combining phase plane analysis and a fuzzy controller, so as to coordinate stability and energy saving, obtain minimized additional yaw moment, and achieve the purpose of coordination of stability and energy saving.

[0005] The technical scheme adopted by the application is as follows:

[0006] The steering stability and energy saving adaptive control method for the four-wheel independent drive electric vehicle comprises the following steps.

[0007] Step 1, when the four-wheel independent drive electric vehicle is steering, the center of mass side slip angle, yaw angular velocity and battery residual capacity SOC (State of Charge) of the four-wheel independent drive electric vehicle are acquired in real time, and a stable region of the center of mass side slip angle phase plane is obtained after phase plane analysis.

[0008] Step 2, a time domain regulation model of the four-wheel independent drive electric vehicle based on steering stability and energy saving evaluation parameters is established, adaptive coefficients are obtained by adjusting the expected horizontal parameters of the four-wheel independent drive electric vehicle and the battery residual capacity SOC in real time through a fuzzy controller, when the center of mass side slip angle phase plane of the four-wheel independent drive electric vehicle is in the stable region, the real-time adjusted adaptive coefficients and the horizontal parameters of the four-wheel independent drive electric vehicle are input into the time domain optimization model, and real-time changing prediction time domain and control time domain are output after processing of the time domain optimization model.

[0009] Step 3, the improved model predictive control (MPC) algorithm is obtained by taking the real-time changing prediction time domain and control time domain as the time domain of the model predictive control (MPC) algorithm, and the expected centroid side slip angle and yaw rate of the four-wheel independent drive electric vehicle are processed by the improved model predictive control (MPC) algorithm to output the additional yaw moment of the four-wheel independent drive electric vehicle, and then the adaptive control of the four-wheel independent drive electric vehicle is carried out.

[0010] The method of the application obtains adaptive coefficients through phase plane analysis and fuzzy controllers, and then quantifies the stability level of the whole vehicle, realizes variable time domain control of the MPC algorithm, and minimizes the additional yaw moment output by the upper controller.

[0011] In step 1, the centroid side slip angle The stable region of the phase plane is specifically as follows:

[0012]

[0013] Wherein, β and are the centroid side slip angle and its derivative of the four-wheel independent drive electric vehicle respectively; B1, B2 and B3 are the first, second and third stable region boundary coefficients respectively; p1, p2, p3, p4, p5, p6, p7, p8 and p9 are the first, second, third, fourth, fifth, sixth, seventh, eighth and ninth stable region characteristic values respectively; μ represents the road adhesion coefficient; v x is the speed of the vehicle body of the four-wheel independent drive electric vehicle in the x direction; δ f is the front wheel steering angle of the four-wheel independent drive electric vehicle.

[0014] The stable region considers the influence of the road adhesion coefficient and the vehicle speed on the stable region, wherein the vehicle speed affects the slope of the stable boundary, and the road adhesion coefficient affects the position of the saddle point.

[0015] In step 2, the time domain regulation model of the four-wheel independent drive electric vehicle is specifically as follows:

[0016] p = 2 [10-round (10p)]

[0017] c = p / 2

[0018]

[0019] Wherein, p and c are the prediction time domain and the control time domain respectively, which can be changed according to the stability of the whole vehicle; round() is the rounding function; p is the steering stability and energy saving evaluation parameter, p ∈ [0, 1]; I ind is the horizontal parameter of the four-wheel independent drive electric vehicle, Iind = max{I β ,I γ}, max{} is the maximum function, I β and I γ are the centroid side slip angle level parameter and the yaw rate level parameter respectively; z is an adaptive coefficient.

[0020] The steering stability and energy saving evaluation parameter p is introduced to quantitatively describe the current stability state of the vehicle, the size of the adaptive coefficient z is related to the steering stability and energy saving of the vehicle, the smaller the adaptive coefficient z is, the closer the steering stability and energy saving evaluation parameter p is to 1, then the stability of the four-wheel independent drive electric vehicle is lower, and the energy saving is lower; the steering stability and energy saving evaluation parameter p equal to 0 corresponds to the range of the level parameter I ind is also larger, and the evaluation result is more intuitive.

[0021] The expected level parameter I i ' of the four-wheel independent drive electric vehicle is the expected value of the level parameter of the four-wheel independent drive electric vehicle, I nd ' = max{I' i ,I nd}, I' β and I γ ' are the expected centroid side slip angle level parameter and the expected yaw rate level parameter respectively.

[0022] The centroid side slip angle level parameter I β and the yaw rate level parameter I γ are as follows:

[0023]

[0024] γ β = μg / v γ , γ max = - μg / v x

[0025] Wherein, β min , β x and β max are the centroid side slip angle and its derivative and upper and lower limits of the four-wheel independent drive electric vehicle respectively; γ min , γ max , γ min and γ x are the yaw rate and its derivative and upper and lower limits of the four-wheel independent drive electric vehicle respectively; B1, B2 and B3 are the first, second and third stability region boundary coefficients respectively; μ and g represent the road adhesion coefficient and the acceleration of gravity respectively; v ss is the vehicle body speed of the four-wheel independent drive electric vehicle.

[0026] The expected side slip angle γ of the four-wheel independent drive electric vehicle in step 3 ss And the expected yaw rate β ss Specifically as follows:

[0027]

[0028] κ=m(l f K f -l r K r ) / (LK r K f )

[0029] Where, v x The body speed of the four-wheel independent drive electric vehicle; L is the distance from the front axle to the rear axle of the four-wheel independent drive electric vehicle; κ is the stability parameter; δ f The front wheel angle of the four-wheel independent drive electric vehicle; μ and g respectively represent the road adhesion coefficient and the acceleration of gravity; sign() is an indication parameter, the input parameter is greater than 0, the indication parameter is equal to 1, the input parameter is less than 0, the indication parameter is equal to-1, the input parameter is equal to 0, the indication parameter is equal to 0; l f And l r The distance from the center of mass of the four-wheel independent drive electric vehicle to the front axle and the rear axle respectively; m is the mass of the four-wheel independent drive electric vehicle; K f And K r The side stiffness of the front and rear wheels of the four-wheel independent drive electric vehicle.

[0030] The improved model predictive control MPC algorithm in step 3 processes the additional yaw moment of the four-wheel independent drive electric vehicle and outputs the additional yaw moment of the four-wheel independent drive electric vehicle as follows:

[0031]

[0032] M z,min ≤u(t k +iΔτ)≤M z,max ,i=0,1,2...p-1

[0033] Where, J() is the optimization objective function; X k,p And R k,p The prediction sequence and the reference sequence of the improved model predictive control MPC algorithm respectively; Q and W are the first and second weighting coefficients of the improved model predictive control MPC algorithm; u k,p And The additional yaw moment of the four-wheel independent drive electric vehicle before and after the improved model predictive control MPC algorithm; S u Is the prediction sequence X k,pThe coefficient corresponding to the difference Δu term of the inter-step additional yaw moment u; Ψ is the prediction sequence X without the difference Δu term of the inter-step additional yaw moment k,p and the reference sequence R k,p ; M z,max and M z,min are the upper and lower limits of the additional yaw moment of the four-wheel independent drive electric vehicle respectively; t k is the starting control time of the improved model predictive control (MPC) algorithm; p is the prediction time domain; and Δτ is the discrete step length.

[0034] The electronic device of the present application comprises a memory and a processor coupled to each other, wherein the memory stores program data, and the processor invokes the program data to execute the method as described above.

[0035] The computer readable storage medium of the present application has program data stored thereon, and the program data is executed by a processor to implement the method as described above.

[0036] The present application has the following beneficial effects:

[0037] The method of the present application obtains the stability level of the whole vehicle steering through phase plane analysis, obtains the adaptive coefficient in the fuzzy controller, and uses the stability level for quantization to obtain the evaluation index, and finally determines the prediction time domain and the control time domain of the MPC algorithm, so that the additional yaw moment can be minimized, and the stability and energy saving in the steering process can be coordinated according to the stability level and the battery state of the whole vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a flowchart of the method of the present application;

[0039] Figure 2 is a schematic diagram of the two-degree-of-freedom whole vehicle model of the present application;

[0040] Figure 3 is a schematic diagram of the β-β phase plane including the stable region boundary and the saddle point position of the present application;

[0041] Figure 4 is a schematic diagram of the fuzzy controller of the present application. DETAILED DESCRIPTION

[0042] The present application will be further described in detail below in combination with the drawings and specific embodiments.

[0043] As shown in Figure 1 , the adaptive control method for steering stability and energy saving of the four-wheel independent drive electric vehicle of the present application is as follows:

[0044] Step 1, when the four-wheel independent drive electric vehicle is steering, the center of mass side slip angle, yaw rate and battery remaining capacity SOC of the four-wheel independent drive electric vehicle are obtained in real time, and the stable region of the center of mass side slip angle is obtained after phase plane analysis, as follows:

[0045]

[0046]

[0047] wherein β and are the center of mass side slip angle and its derivative of the four-wheel independent drive electric vehicle respectively; B1, B2 and B3 are the first, second and third stable region boundary coefficients respectively; p1, p2, p3, p4, p5, p6, p7, p8 and p9 are the first, second, third, fourth, fifth, sixth, seventh, eighth and ninth stable region characteristic values respectively; μ represents the road adhesion coefficient; v x is the speed of the four-wheel independent drive electric vehicle in the x direction; δ f is the front wheel steering angle of the four-wheel independent drive electric vehicle.

[0048] The stable region considers the influence of the road adhesion coefficient and the vehicle speed on the stable region, wherein the vehicle speed affects the slope of the stable boundary, and the road adhesion coefficient affects the position of the saddle point.

[0049] Step 2, a time domain regulation model of the four-wheel independent drive electric vehicle based on the steering stability energy-saving evaluation parameter is established, as follows:

[0050] p=2[10-round(10ρ)]

[0051] c=p / 2

[0052]

[0053] wherein p and c are the prediction time domain and the control time domain respectively, which can be changed according to the stability of the whole vehicle; round() is the rounding function; ρ is the steering stability energy-saving evaluation parameter, ρ∈[0,1]; I ind is the horizontal parameter of the four-wheel independent drive electric vehicle, I ind = max{I β , I γ}, max{} is the maximum value function, I β and I γ are the center of mass side slip angle horizontal parameter and the yaw rate horizontal parameter respectively; z is the adaptive coefficient.

[0054] The steering stability and energy saving evaluation parameter p is introduced to quantitatively describe the current stability state of the automobile, and the size of the adaptive coefficient z is related to the steering stability and energy saving of the automobile. The smaller the adaptive coefficient z is, the closer the steering stability and energy saving evaluation parameter p is to 1, and the lower the stability and energy saving of the four-wheel independent drive electric automobile is. The steering stability and energy saving evaluation parameter p equal to 0 corresponds to the horizontal parameter I ind The larger the range is, the more intuitive the evaluation result is.

[0055] The adaptive coefficient is obtained by adjusting the expected horizontal parameter of the four-wheel independent drive electric automobile and the remaining capacity SOC of the battery in real time through the fuzzy controller. When the phase plane of the center of mass side slip angle of the four-wheel independent drive electric automobile is in the stable region, the real-time adjusted adaptive coefficient and the horizontal parameter of the four-wheel independent drive electric automobile are input into the time domain optimization model, and the real-time changed prediction time domain and control time domain are output after the time domain optimization model is processed. The expected horizontal parameter I i nd of the four-wheel independent drive electric automobile is the expected value of the horizontal parameter of the four-wheel independent drive electric automobile. i nd β γ ', I β and I γ are the expected center of mass side slip angle horizontal parameter and the expected yaw rate horizontal parameter respectively.

[0056] The center of mass side slip angle horizontal parameter I β and the yaw rate horizontal parameter I γ are as follows:

[0057]

[0058] γ max = μg / v x , γ min = - μg / v x

[0059] wherein β, β max and β min are the center of mass side slip angle and its derivative and upper and lower limits of the four-wheel independent drive electric automobile respectively; γ, γ max and γ min are the yaw rate and its derivative and upper and lower limits of the four-wheel independent drive electric automobile respectively; B1, B2 and B3 are the first, second and third stable region boundary coefficients respectively; μ and g are the road adhesion coefficient and the acceleration of gravity respectively; v x is the vehicle body speed of the four-wheel independent drive electric automobile.

[0060] ​​​​Step 3, the improved model predictive control (MPC) algorithm is obtained by taking the real-time changing prediction time domain and control time domain as the time domain of the model predictive control (MPC) algorithm, and the expected center side slip angle and yaw rate of the four-wheel independent drive electric vehicle are processed by the improved model predictive control (MPC) algorithm to output the additional yaw moment of the four-wheel independent drive electric vehicle, and then the adaptive control of the four-wheel independent drive electric vehicle is carried out.

[0061] The expected center side slip angle γ of the four-wheel independent drive electric vehicle ss and the expected yaw rate β ss Specifically as follows:

[0062]

[0063] κ = m (l f K f -l r K r ) / (L K r K f )

[0064] Wherein, v x is the vehicle body speed of the four-wheel independent drive electric vehicle; L is the distance from the front axle to the rear axle of the four-wheel independent drive electric vehicle; κ is the stability parameter; δ f is the front wheel angle of the four-wheel independent drive electric vehicle; μ and g respectively represent the road adhesion coefficient and the acceleration of gravity; sign() is an indication parameter, which is equal to 1 when the input parameter is greater than 0, equal to -1 when the input parameter is less than 0, and equal to 0 when the input parameter is equal to 0; l f and l r are the distances from the center of mass of the four-wheel independent drive electric vehicle to the front axle and the rear axle respectively; m is the total mass of the four-wheel independent drive electric vehicle; K f and K r are the side stiffness of the front and rear wheels of the four-wheel independent drive electric vehicle.

[0065] The system state space equation of the two-degree-of-freedom vehicle model with only lateral and yaw is as follows:

[0066]

[0067] Wherein, is the derivative of the vehicle body speed v x of the four-wheel independent drive electric vehicle; is the derivative of the center side slip angle β of the four-wheel independent drive electric vehicle; is the derivative of the yaw rate of the four-wheel independent drive electric vehicle; F yf and F yrThe sum of the lateral force of the front wheels and the sum of the lateral force of the rear wheels of the four-wheel independent drive electric vehicle, respectively. z The z-axis moment of inertia of the four-wheel independent drive electric vehicle.

[0068] The boundary condition of the yaw rate satisfies the following equation considering the influence of the road adhesion coefficient μ:

[0069]

[0070] When the vehicle is in a stable state, and are both 0, so the expected yaw rate and the expected center of mass side slip angle can be derived.

[0071] The improved model predictive control MPC algorithm for processing the additional yaw moment of the four-wheel independent drive electric vehicle is as follows:

[0072]

[0073] M z,min ≤u(t k +iΔτ)≤M z,max , i = 0, 1, 2…p-1

[0074] where J() is the optimization objective function; X k,p and R k,p are the prediction sequence and the reference sequence of the improved model predictive control MPC algorithm, respectively; Q and W are the first and second weighting coefficients of the improved model predictive control MPC algorithm, respectively; u k,p and are the additional yaw moments of the four-wheel independent drive electric vehicle before and after processing by the improved model predictive control MPC algorithm, respectively; S u is the coefficient corresponding to the difference Δu term of the additional yaw moment u between steps in the prediction sequence X k,p ; Ψ is the difference between the prediction sequence X k,p and the reference sequence R k,p after removing the difference Δu term of the additional yaw moment; M z,max and M z,min are the upper and lower limits of the additional yaw moment of the four-wheel independent drive electric vehicle; t k is the starting control time of the improved model predictive control MPC algorithm; p is the prediction time domain; and Δτ is the discrete step size.

[0075] In the improved model predictive control MPC algorithm, a nonlinear two-degree-of-freedom model considering the additional yaw moment is constructed as a prediction model, the prediction model is linearized and discretized to obtain a prediction equation. The deviation between the prediction state quantity and the reference value is used as an optimization variable, and finally an optimization objective function is defined.

[0076] The nonlinear two-degree-of-freedom model state space equation is:

[0077]

[0078] where M z is the additional yaw moment; since the center of mass side slip angle β satisfies β = arctan(v y / v x ) and v y is the speed of the body of the four-wheel independent drive electric vehicle in the y direction, so β can be simplified as β ≈ v y / v x ; let x = [β, γ] T , u = M z , and according to the linear relationship between the center of mass side slip angle and the lateral force, the nonlinear state space equation of the system is linearized as:

[0079]

[0080] where is the derivative of x, and f() is the linear state space equation of the system.

[0081] The coefficient matrixes of [β, γ] T , M z and δ f are defined as A, B and C respectively, and the state space equation of the system is rewritten as:

[0082]

[0083] After discretization processing, the discretization step is set as Δτ, and only the first order term is retained. δ f at the same t k is regarded as a fixed value d, and the discrete state space equation is obtained as:

[0084]

[0085] Δu(t k ) = u(t k ) - u(t k - Δτ)

[0086] ζ(t k ) = [x(t k ), u(t k - Δτ)] T

[0087]

[0088] where ζ is a new variable combining x and u. and are the variations of the discrete last three coefficient matrices A, B and C, respectively; Δu is the difference of u between each step; I is a diagonal matrix, and 0 is a zero matrix.

[0089] The prediction equation can be written as:

[0090]

[0091] X k,p = [x T (t k + Δτt k ), x T (t k + 2Δτt k ),..., x T (t k + pΔτt k )] T

[0092] = S c [ζ(t k )] + S u Δu(t k ) + S d d

[0093] Φ = [I, 0]

[0094] where S c is the coefficient matrix corresponding to ζ in the prediction sequence X k,p , and S d is the coefficient matrix corresponding to d in the prediction sequence X k,p .

[0095] Taking the desired centroid side slip angle β ss and the yaw rate γ ss as the reference input, i.e. r = [β ss , γ ss ] T , the reference sequence is obtained as:

[0096] R k,p = [r T (t k + Δτt k ), r T (t k + 2Δτt k ),..., r T (t k + pΔτt k )] T

[0097] To make the system follow the desired centroid side slip angle and yaw rate, the deviation of the predicted sequence from the reference sequence is taken as an optimization variable, and the optimization objective function is defined as:

[0098]

[0099] The additional yaw moment u must satisfy the upper and lower boundaries of the additional yaw moment. c [ζ(t k )]+S d d-R k,p . Finally, we get:

[0100]

[0101] Since in the model predictive control (MPC) algorithm, the size of the prediction time domain and the control time domain will affect the final output of the additional yaw moment, the relationship between the two is: the larger the time domain, the smaller the additional yaw moment, which is beneficial to energy saving; the smaller the time domain, the larger the additional yaw moment, which is beneficial to stability. The method of the present application can minimize the additional yaw moment output by the upper controller, and coordinate the stability and energy saving during the steering process according to the stability level of the whole vehicle and the battery state.

[0102] The present application realizes that the time domain of the improved MPC algorithm changes in real time with the stability level of the whole vehicle and the battery state by giving the whole vehicle driving conditions and whole vehicle parameters. As shown in the figure, Figure 2 in the two-degree-of-freedom whole vehicle model used in the specific implementation of the present application, the structural parameters of the whole vehicle include: the mass of the whole vehicle m, the distance of the centroid from the front axle l f , the distance of the centroid from the rear axle l r , the distance between the front axle and the rear axle L, the z-axis moment of inertia I z , the front wheel cornering stiffness K f and the rear wheel cornering stiffness K r , the vehicle body speed v x and v y , the sum of the front wheel lateral force F yf , the sum of the rear wheel lateral force F yr , the front wheel yaw rate γ f , the rear wheel yaw rate γ r , the centroid side slip angle β, the front wheel cornering angle α f , and the rear wheel cornering angle α r . The given driving condition is to move at a constant road surface adhesion coefficient μ and a constant speed. The relationship between the wheel angle and the steering wheel angle is δ f = δ / I t . δ is the steering wheel angle, and I t is the steering wheel transmission ratio. In the case where the above parameters are known, the desired centroid side slip angle γ ss and the desired yaw rate β ss can be determined.

[0103] As shown in Figure 3 , the red solid line is the boundary of the phase plane stable region, the green and blue solid lines represent the upper and lower boundaries of the yaw rate, the stable region boundary coefficient is affected by many conditions such as vehicle parameters, the influence of road adhesion coefficient and vehicle speed on the stable region is considered, among which the vehicle speed affects the slope of the stable boundary, and the road adhesion coefficient affects the position of the saddle point. Calculate the slope at four or more different speeds, calculate multiple saddle point positions, get p1-p9 through fitting, and get B1, B2 and B3. According to the expected centroid side slip angle and yaw rate, I i ' nd can be integrated. According to the size of I i ' nd and SOC, the fuzzy controller outputs the adaptive coefficient z, as shown in Figure 4 , which is a schematic of the fuzzy controller, and finally the time domain size of the MPC algorithm can be determined.

[0104] The current centroid side slip angle, yaw rate, wheel angle and vehicle speed are obtained from the data acquisition module, and the expected centroid side slip angle and yaw rate are obtained. The linearized state space equation coefficient matrices A, B and C are obtained. The discretization process is performed to obtain the discretized state space equation coefficient matrices and After determining the prediction sequence X k,p and the reference sequence R k,p , the deviation of the prediction sequence and the reference sequence is taken as the optimization variable, and the optimization objective function is defined. Then, multiple control variables are converted into a single control variable, and quadratic programming is used for solving to obtain the minimum additional yaw moment.

[0105] The content described in the embodiments of the present specification is only a list of implementation forms of the inventive concept, and the protection scope of the present application should not be regarded as limited to the specific forms described in the embodiments, and the protection scope of the present application also extends to equivalent technical means of using fuzzy control to obtain adaptive coefficients and the quantization method after combining adaptive coefficients.

Claims

1. A method for adaptive control of steering stability and energy saving of a four-wheel independent drive electric vehicle, characterized in that, The application relates to a four-wheel independent drive electric vehicle adaptive control method, which comprises the following steps: Step 1: when the four-wheel independent drive electric vehicle is steering, the center of mass side slip angle, the yaw angular velocity and the remaining capacity SOC of the battery of the four-wheel independent drive electric vehicle are acquired in real time, the phase plane of the center of mass side slip angle is analyzed in a phase plane, and a stable region of the phase plane is obtained; Step 2: a time domain regulation model of the four-wheel independent drive electric vehicle based on a steering stability and energy saving evaluation parameter is established, the expected horizontal parameter and the remaining capacity SOC of the battery of the four-wheel independent drive electric vehicle are adjusted in real time through a fuzzy controller, and an adaptive coefficient is obtained; when the phase plane of the center of mass side slip angle of the four-wheel independent drive electric vehicle is in the stable region, the adaptive coefficient adjusted in real time and the horizontal parameter of the four-wheel independent drive electric vehicle are input into the time domain optimization model, and the time domain optimization model is processed to output a real-time changing prediction time domain and a control time domain; Step 3: the real-time changing prediction time domain and the control time domain are taken as a time domain of an improved model predictive control MPC algorithm, the expected center of mass side slip angle and the expected yaw angular velocity of the four-wheel independent drive electric vehicle are processed through the improved model predictive control MPC algorithm, and an additional yaw moment of the four-wheel independent drive electric vehicle is output, so that adaptive control is performed on the four-wheel independent drive electric vehicle.

2. The adaptive control method for steering stability and energy saving of a four-wheel independent drive electric vehicle according to claim 1, characterized in that: The step 1 of the centroid side slip angle The stable region of the phase plane is specifically as follows: wherein, and are the center of mass side slip angle and its derivative of the four-wheel independent drive electric vehicle, respectively; , and are the first, second and third stability region boundary coefficients, respectively; , , , , , , , and are the first, second, third, fourth, fifth, sixth, seventh, eighth and ninth stability region characteristic values, respectively; represents the road surface adhesion coefficient; is the vehicle body speed in the x direction of the four-wheel independent drive electric vehicle; is the front wheel steering angle of the four-wheel independent drive electric vehicle.

3. The adaptive control method for steering stability and energy saving of a four-wheel independent drive electric vehicle according to claim 1, characterized in that: In the step 2, the time domain regulation model of the four-wheel independent drive electric vehicle is as follows: wherein, and are a prediction horizon and a control horizon, respectively; is a rounding function; is a cornering stability and energy saving evaluation parameter, ; is a level parameter of a four-wheel independent drive electric vehicle, , is a maximum value function, and are a center of mass side slip angle level parameter and a yaw rate level parameter, respectively; is an adaptive coefficient; Adaptive coefficients The smaller the turning stability and energy saving evaluation parameter The closer to 1, the lower the overall stability of the four-wheel independent drive electric vehicle, and the lower the energy saving Desired level parameters for a four-wheel independent drive electric vehicle are desired values for level parameters for a four-wheel independent drive electric vehicle, , and are a desired level parameter for a yaw rate and a desired level parameter for a side slip angle, respectively.

4. The adaptive control method for steering stability and energy saving of a four-wheel independent drive electric vehicle according to claim 3, characterized in that: The center of mass side slip angle level parameter And the yaw angular velocity level parameter Specific as follows: ， ， wherein, , , and are the upper and lower limits of the side slip angle and its derivative of the four-wheel independent drive electric vehicle, respectively; , , and are the upper and lower limits of the yaw rate and its derivative of the four-wheel independent drive electric vehicle, respectively; , and are the first, second and third stability region boundary coefficients, respectively; and denote the road adhesion coefficient and the gravitational acceleration, respectively; is the body speed of the four-wheel independent drive electric vehicle.

5. The adaptive control method for stability and energy saving of four-wheel independent drive electric vehicle steering according to claim 1, characterized in that: In step 3, the desired yaw rate of the four-wheel independent drive electric vehicle and the desired center of mass side slip angle Specifically as follows: wherein, is the vehicle body speed of the four-wheel independent drive electric vehicle; is the distance from the front axle to the rear axle of the four-wheel independent drive electric vehicle; is the stability parameter; is the front wheel steering angle of the four-wheel independent drive electric vehicle; and respectively represent the road adhesion coefficient and the gravitational acceleration; is the indication parameter, if the input parameter is greater than 0, the indication parameter is equal to 1, if the input parameter is less than 0, the indication parameter is equal to -1, and if the input parameter is equal to 0, the indication parameter is equal to 0; and respectively are the distances from the center of mass of the four-wheel independent drive electric vehicle to the front axle and the rear axle; is the total mass of the four-wheel independent drive electric vehicle; and are the front wheel and rear wheel cornering stiffness of the four-wheel independent drive electric vehicle.

6. The adaptive control method for stability and energy saving of four-wheel independent drive electric vehicle steering according to claim 1, characterized in that: In the step 3, the additional yaw moment of the four-wheel independent drive electric vehicle output through the improved model predictive control MPC algorithm is as follows: wherein, is an optimization objective function; and are a prediction sequence and a reference sequence of the improved model predictive control (MPC) algorithm, respectively; and are first and second weighting coefficients of the improved model predictive control (MPC) algorithm, respectively; and are additional yaw moments of the four-wheel independent drive electric vehicle before and after being processed by the improved model predictive control (MPC) algorithm, respectively; is the prediction sequence is the additional yaw moment between steps is the difference is the coefficient corresponding to the term is the prediction sequence is the difference between the prediction sequence and the reference sequence and are upper and lower limits of the additional yaw moment of the four-wheel independent drive electric vehicle, respectively; is a starting control time of the improved model predictive control (MPC) algorithm; is a prediction time domain; is a discrete step.​ 7. An electronic device, comprising: The application also relates to a computer readable storage medium, which comprises: The program data is executed by the processor to realize the method in any one of claims 1-6.

8. A computer readable storage medium having stored thereon program data, wherein, The program data is executed by the processor to realize the method in any one of claims 1-6.

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