Adaptive control method for steering stability and energy saving performance of four-wheel independent drive electric automobile

Abstract

The invention discloses an adaptive control method for steering stability and energy conservation of a four-wheel independent drive electric automobile. The method comprises the steps that a time domain regulation and control model based on steering stability energy-saving evaluation parameters is established, and an expected level parameter and the residual capacity of a battery are adjusted in real time through a fuzzy controller to obtain an adaptive coefficient; when the phase plane of the side slip angle is in a stable area, the adaptive coefficient and the horizontal parameter are input into a model to be processed, then a prediction time domain and a control time domain which change in real time are output and serve as time domains of an MPC algorithm, and after the expected side slip angle and the yaw velocity are processed through the improved MPC algorithm, additional yaw moment is output; and adaptive control is carried out on the electric vehicle. According to the method, inherent parameters and real-time parameters of the whole vehicle are utilized, phase plane analysis and a fuzzy controller are combined, and variable time domain control of the MPC algorithm is achieved, so that the stability and the energy saving performance are coordinated, the minimum additional yawing moment can be obtained, and the purpose of coordinating the stability and the energy saving performance is achieved.

Description

Technical Field

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

[0002] Steering stability is an unavoidable issue for vehicle driving safety, and maximum mileage is also a key indicator. Therefore, coordinating stability and energy saving is the key point of current research. Among the many electric vehicle power system layouts, four-wheel independent drive electric vehicles can achieve complex dynamic control and have outstanding advantages in stability and energy saving control.

[0003] In steering stability control, upper and lower hierarchical control is usually used to reduce the dimension of the optimization strategy. The upper controller uses direct yaw moment control to obtain additional yaw moment by tracking the ideal state, and the lower controller uses the optimal torque distribution algorithm to ensure that the vehicle achieves the expected steering control. How to output the appropriate additional yaw moment through the upper controller becomes the key to coordinating stability and energy saving. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the present invention provides an adaptive control method for steering stability and energy saving of a four-wheel independent drive electric vehicle. The method utilizes the inherent parameters and real-time parameters of the whole vehicle, combines phase plane analysis and fuzzy controller, realizes the variable time domain control of the MPC strategy, thereby coordinating stability and energy saving, and can obtain the minimized additional yaw moment, so as to achieve the purpose of coordinating stability and energy saving.

[0005] The technical solution adopted by the present invention is:

[0006] The adaptive control method for steering stability and energy saving of a four-wheel independent drive electric vehicle of the present invention comprises:

[0007] Step 1, when the four-wheel independent drive electric vehicle is turning, the center of mass sideslip angle, yaw angular velocity and battery remaining capacity SOC (State of Charge) of the four-wheel independent drive electric vehicle are obtained in real time, and a phase plane analysis is performed to obtain a stable area of ​​the phase plane of the center of mass sideslip angle.

[0008] Step 2, establish a time domain control model of the four-wheel independent drive electric vehicle based on the steering stability and energy-saving evaluation parameters, and obtain the adaptive coefficient after adjusting the expected horizontal parameters and the battery remaining capacity SOC of the four-wheel independent drive electric vehicle in real time through the fuzzy controller; when the phase plane of the center of mass sideslip angle of the four-wheel independent drive electric vehicle is in a stable area, the real-time adjusted adaptive coefficient and the horizontal parameters of the four-wheel independent drive electric vehicle are input into the time domain optimization model, and the time domain optimization model outputs the real-time changing prediction time domain and control time domain after processing.

[0009] Step 3, using the real-time changing prediction time domain and control time domain as the time domain of the model predictive control MPC (Model Predictive Control) algorithm to obtain an improved model predictive control MPC algorithm, and processing the expected center of mass sideslip angle and yaw angular velocity of the four-wheel independent drive electric vehicle through the improved model predictive control MPC algorithm to output the additional yaw torque of the four-wheel independent drive electric vehicle, thereby adaptively controlling the four-wheel independent drive electric vehicle.

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

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

[0012]

[0013] Among them, β and are the sideslip angle of the center of mass of the four-wheel independent drive electric vehicle and its derivative; B1, B2 and B3 are the boundary coefficients of the first, second and third stable regions respectively; p1, p2, p3, p4, p5, p6, p7, p8 and p9 are the special values ​​of the first, second, third, fourth, fifth, sixth, seventh, eighth and ninth stable regions respectively; μ represents the road adhesion coefficient; v x is the speed of the body of the four-wheel independent drive electric vehicle in the x direction; δ f It is the front wheel turning angle of the four-wheel independent drive electric vehicle.

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

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

[0016] p = 2[10-round(10ρ)]

[0017] c=p / 2

[0018]

[0019] Among them, p and c are the prediction time domain and control time domain respectively, which can be changed according to the stability of the 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, Iind =max{I β ,I γ}, max{} is the maximum value function, I β and I γ are the horizontal parameters of the sideslip angle at the center of mass and the yaw rate; z is the adaptive coefficient.

[0020] The steering stability and energy-saving evaluation parameter ρ 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 performance of the vehicle. The smaller the adaptive coefficient z is, the closer the steering stability and energy-saving evaluation parameter ρ is to 1, and the lower the vehicle stability and energy-saving performance of the four-wheel independent drive electric vehicle are. The level parameter I corresponding to the steering stability and energy-saving evaluation parameter ρ being equal to 0 is ind The larger the range, the more precise the evaluation results will be.

[0021] Expected level parameters of four-wheel independent drive electric vehicles I i ' nd is the expected value of the horizontal parameter of the four-wheel independent drive electric vehicle, I i ' nd =max{I' β ,I γ '}, I' β and I γ ' are the expected center of mass sideslip angle horizontal parameters and the expected yaw rate horizontal parameters respectively.

[0022] The horizontal parameter I of the center of mass sideslip angle β and the yaw rate level parameter I γ The details are as follows:

[0023]

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

[0025] Among them, β, β max and β min are the sideslip angle of the center of mass of the four-wheel independent drive electric vehicle and its derivative and upper and lower limits; γ, 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 vehicle; B1, B2 and B3 are the boundary coefficients of the first, second and third stable regions respectively; μ and g represent the road adhesion coefficient and gravity acceleration respectively; v x It is the body speed of the four-wheel independent drive electric vehicle.

[0026] In step 3, the desired center of mass sideslip angle γ of the four-wheel independent drive electric vehicle is ss and the desired yaw rate β ss The details are as follows:

[0027]

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

[0029] Among them, v x is 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 is the front wheel turning angle of the four-wheel independent drive electric vehicle; μ and g represent the road adhesion coefficient and gravity acceleration respectively; sign() is the indicator parameter. If the input parameter is greater than 0, the indicator parameter is equal to 1; if the input parameter is less than 0, the indicator parameter is equal to -1; if the input parameter is equal to 0, the indicator 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 vehicle mass of the four-wheel independent drive electric vehicle; K f and K r It is the lateral stiffness of the front and rear wheels of the four-wheel independent drive electric vehicle.

[0030] In the step 3, the additional yaw moment of the four-wheel independent drive electric vehicle is output after processing by the improved model predictive control MPC algorithm as follows:

[0031]

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

[0033] Among them, J() is the optimization objective function; X k,p and R k,p are the prediction sequence and reference sequence of the improved model predictive control MPC algorithm respectively; Q and W are the first and second weighted coefficients of the improved model predictive control MPC algorithm respectively; u k,p and The improved model predictive control MPC algorithm is used to process the additional yaw moment of the front and rear four-wheel independent drive electric vehicle; S u To predict the sequence X k,pThe coefficient corresponding to the additional yaw moment u difference Δu between the steps; Ψ is the prediction sequence X without the additional yaw moment difference Δu between the steps k,p and the reference sequence R k,p The difference between 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; Δτ is the discrete step length.

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

[0035] The computer-readable storage medium of the present invention stores program data thereon, and when the program data is executed by a processor, the method described above is implemented.

[0036] The beneficial effects of the present invention are:

[0037] The method of the present invention obtains the steering stability level of the whole vehicle through phase plane analysis, obtains the adaptive coefficient in the fuzzy controller, and uses it to quantify the stability level to obtain the evaluation index, and finally determines the prediction time domain and control time domain of the MPC algorithm, which can minimize the additional yaw moment, coordinate the stability and energy saving in the steering process according to the stability level of the whole vehicle and the battery status. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a flow chart of the method of the present invention;

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

[0040] Figure 3 The β-β phase plane of the present invention includes a schematic diagram of the stable region boundary and the saddle point position;

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

[0042] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

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

[0044] Step 1, when the four-wheel independent drive electric vehicle is turning, the center of mass sideslip angle, yaw angular velocity and battery remaining capacity SOC of the four-wheel independent drive electric vehicle are obtained in real time and the stable area of ​​the phase plane of the center of mass sideslip angle is obtained after performing phase plane analysis, as follows:

[0045]

[0046]

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

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

[0049] Step 2, establish a time domain control model of a four-wheel independent drive electric vehicle based on steering stability energy-saving evaluation parameters, as follows:

[0050] p = 2[10-round(10ρ)]

[0051] c=p / 2

[0052]

[0053] Among them, p and c are the prediction time domain and control time domain respectively, which can be changed according to the stability of the 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 horizontal parameters of the sideslip angle at the center of mass and the yaw rate, respectively; z is the adaptive coefficient.

[0054] The steering stability and energy-saving evaluation parameter ρ 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 performance of the vehicle. The smaller the adaptive coefficient z is, the closer the steering stability and energy-saving evaluation parameter ρ is to 1, and the lower the vehicle stability and energy-saving performance of the four-wheel independent drive electric vehicle are. The level parameter I corresponding to the steering stability and energy-saving evaluation parameter ρ being equal to 0 is ind The larger the range, the more precise the evaluation results will be.

[0055] The desired horizontal parameters of the four-wheel independent drive electric vehicle and the remaining battery capacity SOC are adjusted in real time by the fuzzy controller to obtain the adaptive coefficient; when the phase plane of the center of mass side slip angle of the four-wheel independent drive electric vehicle is in a stable area, the real-time adjusted adaptive coefficient and the horizontal parameters 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 the real-time changing prediction time domain and control time domain. The desired horizontal parameters of the four-wheel independent drive electric vehicle I i ' nd is the expected value of the horizontal parameter of the four-wheel independent drive electric vehicle, I i ' nd =max{I' β ,I γ '}, I' β and I γ ' are the expected center of mass sideslip angle horizontal parameters and the expected yaw rate horizontal parameters respectively.

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

[0057]

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

[0059] Among them, β, β max and β min are the sideslip angle of the center of mass of the four-wheel independent drive electric vehicle and its derivative and upper and lower limits; 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 vehicle; B1, B2 and B3 are the boundary coefficients of the first, second and third stable regions respectively; μ and g represent the road adhesion coefficient and gravity acceleration respectively; v x It is the body speed of the four-wheel independent drive electric vehicle.

[0060] Step 3, using the real-time changing prediction time domain and control time domain as the time domain of the model predictive control MPC algorithm to obtain an improved model predictive control MPC algorithm, and processing the expected center of mass sideslip angle and yaw angular velocity of the four-wheel independent drive electric vehicle through the improved model predictive control MPC algorithm to output the additional yaw torque of the four-wheel independent drive electric vehicle, thereby adaptively controlling the four-wheel independent drive electric vehicle.

[0061] Expected sideslip angle of center of mass of four-wheel independent drive electric vehicle γ ss and the desired yaw rate β ss The details are as follows:

[0062]

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

[0064] Among them, v x is 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 is the front wheel turning angle of the four-wheel independent drive electric vehicle; μ and g represent the road adhesion coefficient and gravity acceleration respectively; sign() is the indicator parameter. If the input parameter is greater than 0, the indicator parameter is equal to 1; if the input parameter is less than 0, the indicator parameter is equal to -1; if the input parameter is equal to 0, the indicator 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 vehicle mass of the four-wheel independent drive electric vehicle; K f and K r It is the lateral stiffness of the front and rear wheels of the four-wheel independent drive electric vehicle.

[0065] The system state space equations for the vehicle model with only lateral and yaw degrees of freedom are as follows:

[0066]

[0067] in, is the body speed v of the four-wheel independent drive electric vehicle x The derivative of is the derivative of the sideslip angle β of the center of mass 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 yrare the sum of the front wheel lateral force and the sum of the rear wheel lateral force of the four-wheel independent drive electric vehicle; I z is the z-axis moment of inertia of the four-wheel independent drive electric vehicle.

[0068] Considering the influence of the road adhesion coefficient μ, the boundary conditions of the yaw rate are as follows:

[0069]

[0070] When the vehicle is in a stable state, and are all 0, from which the expected yaw rate and the expected sideslip angle of the center of mass can be derived.

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

[0072]

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

[0074] Among them, J() is the optimization objective function; X k,p and R k,p are the prediction sequence and reference sequence of the improved model predictive control MPC algorithm respectively; Q and W are the first and second weighted coefficients of the improved model predictive control MPC algorithm respectively; u k,p and The improved model predictive control MPC algorithm is used to process the additional yaw moment of the front and rear four-wheel independent drive electric vehicle; S u To predict the sequence X k,p The coefficient corresponding to the additional yaw moment u difference Δu between the steps; Ψ is the prediction sequence X without the additional yaw moment difference Δu between the steps k,p and the reference sequence R k,p The difference between 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; Δτ is the discrete step length.

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

[0076] After the additional yaw moment is introduced, the state space equation of the nonlinear two-degree-of-freedom model is:

[0077]

[0078] Among them, M z is the additional yaw moment; since the sideslip angle β satisfies β=arctan(v y / v x )and v y is the velocity of the body of the four-wheel independent drive electric vehicle in the y direction, so β ​​can be simplified to β≈v y / v x ; Let x = [β,γ] T 、u=M z , according to the linear relationship between the sideslip angle and the lateral force, the nonlinear state space equation of the system is linearized as follows:

[0079]

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

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

[0082]

[0083] Then discretize it, set the discrete step length to Δτ, and keep only the first-order term. k The lower delta f Considering d as a fixed value, the discrete state space equation is:

[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] Among them, ζ is a new variable combining x and u; and are the variants of the three coefficient matrices A, B and C after discretization; Δu is the difference between u in 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] Among them, S c To predict the sequence X k,p The coefficient matrix corresponding to ζ in S d To predict the sequence X k,p The coefficient matrix corresponding to d in .

[0095] The expected sideslip angle β ss and the yaw rate γ ss As the reference input, that is, r = [β ss ,γ ss ] T , and obtain the reference sequence:

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

[0097] In order to make the system follow the expected sideslip angle and yaw rate of the center of mass, the deviation between the predicted sequence and the reference sequence is used as the optimization variable, and the optimization objective function is defined as:

[0098]

[0099] u must satisfy the upper and lower limits of the additional yaw moment. Ψ=S c [ζ(t k )]+S d dR k,p . Finally we get:

[0100]

[0101] In the model predictive control MPC algorithm, the size of the prediction time domain and the control time domain will affect the additional yaw moment output in the end, and 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 invention 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 status.

[0102] The present invention realizes the real-time change of the time domain of the improved MPC algorithm with the stability level of the vehicle and the battery status by giving the vehicle driving conditions and vehicle parameters. Figure 2 As shown, in the two-degree-of-freedom vehicle model used in the specific implementation of the present invention, the structural parameters of the vehicle include: vehicle mass m, distance from the center of mass to the front axle l f , the distance between the center of mass and the rear axle is l r , distance from front axle to rear axle L, z-axis moment of inertia I z , front wheel cornering stiffness K f and rear wheel cornering stiffness K r 、V x and v y The sum of the lateral forces of the front wheels is F yf The sum of the lateral forces of the rear wheels is F yr , front wheel yaw rate γ f , rear wheel yaw rate γ r , center of mass sideslip angle β, front wheel sideslip angle α f 、Rear wheel slip angle α r The given driving condition is moving at a constant road 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, I t is the steering wheel transmission ratio. When some of the above parameters are known, the desired center of mass sideslip angle γ can be determined ss and the desired yaw rate β ss .

[0103] like Figure 3 As shown, the red solid line is the stable region boundary of the phase plane, and 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. Considering the influence of the road adhesion coefficient and vehicle speed on the stable region, the vehicle speed affects the slope of the stable boundary, and the road adhesion coefficient affects the position of the saddle point. Calculate the slopes at four or more different vehicle speeds, calculate the positions of multiple saddle points, and obtain p1-p9 by fitting, thereby obtaining B1, B2 and B3. According to the expected sideslip angle of the center of mass and the yaw rate, I can be integrated to obtain i ' nd According to I i ' nd and the size of SOC, the fuzzy controller will output the adaptive coefficient z, such as Figure 4 As shown, it is a schematic diagram of the fuzzy controller, which can ultimately determine the time domain size of the MPC algorithm.

[0104] Obtain the current center of mass slip angle, yaw rate, wheel angle and vehicle speed from the data acquisition module, and obtain the desired center of mass slip angle and yaw rate. Obtain the linearized state space equation coefficient matrices A, B and C. Perform the discretization process to obtain the discretized state space equation coefficient matrix and Determine the predicted sequence X k,p and the reference sequence R k,p Finally, the deviation between the predicted sequence and the reference sequence is used as the optimization variable to define the optimization objective function. Then, multiple control variables are transformed into a single control variable and solved using quadratic programming to obtain the minimized additional yaw moment.

[0105] The contents described in the implementation mode of this specification are merely an enumeration of the implementation forms of the inventive concept. The protection scope of the present invention should not be regarded as limited to the specific forms described in the implementation mode. The protection scope of the present invention also extends to the quantization method after combining the adaptive coefficient and the equivalent technical means of using fuzzy control to obtain the adaptive coefficient.

Claims

1. A method for adaptively controlling steering stability and energy saving of a four-wheel independent drive electric vehicle, characterized in that: include: Step 1, when the four-wheel independent drive electric vehicle is turning, the center of mass sideslip angle, yaw angular velocity and battery remaining capacity SOC of the four-wheel independent drive electric vehicle are obtained in real time, and a phase plane analysis is performed to obtain a stable region of the phase plane of the center of mass sideslip angle; Step 2, establish a time domain control model of the four-wheel independent drive electric vehicle based on the steering stability energy-saving evaluation parameters, and obtain an adaptive coefficient after adjusting the expected horizontal parameters and the battery remaining capacity SOC of the four-wheel independent drive electric vehicle in real time through a fuzzy controller; when the phase plane of the center of mass sideslip angle of the four-wheel independent drive electric vehicle is in a stable area, input the real-time adjusted adaptive coefficient and the horizontal parameters of the four-wheel independent drive electric vehicle into the time domain optimization model, and the time domain optimization model outputs the real-time changing prediction time domain and control time domain after processing; Step 3, using the real-time changing prediction time domain and control time domain as the time domain of the model predictive control MPC algorithm to obtain an improved model predictive control MPC algorithm, and processing the expected center of mass sideslip angle and yaw angular velocity of the four-wheel independent drive electric vehicle through the improved model predictive control MPC algorithm to output the additional yaw torque of the four-wheel independent drive electric vehicle, thereby adaptively controlling 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: In step 1, the center of mass side slip angle The stable region of the phase plane is as follows: Among them, β and are the sideslip angle of the center of mass of the four-wheel independent drive electric vehicle and its derivative; B1, B2 and B3 are the boundary coefficients of the first, second and third stable regions respectively; p1, p2, p3, p4, p5, p6, p7, p8 and p9 are the special values ​​of the first, second, third, fourth, fifth, sixth, seventh, eighth and ninth stable regions respectively; μ represents the road adhesion coefficient; v x is the speed of the body of the four-wheel independent drive electric vehicle in the x direction; δ f It is the front wheel turning 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 step 2, the time domain control model of the four-wheel independent drive electric vehicle is specifically as follows: p = 2[10-round(10ρ)] c=p / 2 Among them, p and c are prediction time domain and control time domain respectively; 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 horizontal parameters of the sideslip angle of the center of mass and the horizontal parameters of the yaw rate; z is the adaptive coefficient; The smaller the adaptive coefficient z is, the closer the steering stability energy-saving evaluation parameter ρ is to 1, and the lower the vehicle stability and energy-saving performance of the four-wheel independent drive electric vehicle are; Expected level parameters of four-wheel independent drive electric vehicles I i ' nd is the expected value of the horizontal parameter of the four-wheel independent drive electric vehicle, I i ' nd =max{I' β ,I γ '}, I' β and I γ ' are the expected center of mass sideslip angle horizontal parameters and the expected yaw rate horizontal parameters 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 horizontal parameter I of the center of mass sideslip angle β and the yaw rate level parameter I γ The details are as follows: c max =μg / v x ,c min =-μg / v x Among them, β, β max and β min are the sideslip angle of the center of mass of the four-wheel independent drive electric vehicle and its derivative and upper and lower limits; 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 vehicle; B1, B2 and B3 are the boundary coefficients of the first, second and third stable regions respectively; μ and g represent the road adhesion coefficient and gravity acceleration respectively; v x It is the body speed of the four-wheel independent drive electric vehicle.

5. 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 step 3, the desired center of mass sideslip angle γ of the four-wheel independent drive electric vehicle is ss and the desired yaw rate β ss The details are as follows: κ=m(l f K f -l r K r ) / (LK r K f ) Among them, v x is 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 is the front wheel turning angle of the four-wheel independent drive electric vehicle; μ and g represent the road adhesion coefficient and gravity acceleration respectively; sign() is the indicator parameter. If the input parameter is greater than 0, the indicator parameter is equal to 1; if the input parameter is less than 0, the indicator parameter is equal to -1; if the input parameter is equal to 0, the indicator 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 vehicle mass of the four-wheel independent drive electric vehicle; K f and K r It is the lateral stiffness of the front and rear wheels of the four-wheel independent drive electric vehicle.

6. 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 3, the additional yaw moment of the four-wheel independent drive electric vehicle is output after processing by the improved model predictive control MPC algorithm as follows: M z,min ≤u(t k +iΔτ)≤M z,max ,i=0,1,2...p-1 Among them, J() is the optimization objective function; X k,p and R k,p are respectively the prediction sequence and reference sequence of the improved model predictive control MPC algorithm; Q and W are respectively the first and second weighted coefficients of the improved model predictive control MPC algorithm; u k,p and They are respectively the additional yaw moment of the front and rear four-wheel independent drive electric vehicle processed by the improved model predictive control MPC algorithm; S u To predict the sequence X k,p The coefficient corresponding to the additional yaw moment u difference Δu between the steps; Ψ is the prediction sequence X without the additional yaw moment difference Δu between the steps k,p and the reference sequence R k,p The difference between 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; Δτ is the discrete step length.

7. An electronic device, characterized in that: include: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1 to 6.

8. A computer-readable storage medium having program data stored thereon, characterized in that: When the program data is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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