Whole vehicle braking stability simulation method of distributed drive-by-wire vehicle and electronic equipment

Abstract

The invention discloses a whole vehicle braking stability simulation method and electronic equipment for a distributed drive-by-wire vehicle, and the method comprises the steps: building a Tricksim whole vehicle controlled object model, and selecting an input parameter and an output parameter of joint simulation; a simulink control model is established, the simulink control model calculates a side slip angle expected value and a yaw velocity expected value in real time according to part of input parameters, and then the optimal additional yaw moment is designed in combination with a quadratic programming algorithm; and the simulink control model calculates the vertical load of a tire in real time according to other input parameters, an optimal distribution algorithm is utilized to be combined with the optimal additional yawing moment to design an optimal braking force distribution value, and output parameters are calculated through the optimal braking force distribution value to control the posture of a vehicle in the Tricksim whole vehicle controlled object model, so that a control closed loop is formed. According to the invention, a simulation platform can be provided for whole-vehicle braking stability performance analysis, control strategy optimization and the like of the distributed drive-by-wire vehicle.

Description

Technical Field

[0001] The present invention relates to the field of distributed wire-controlled vehicle motion control, and in particular to a whole-vehicle braking stability simulation method and electronic equipment for a distributed wire-controlled vehicle. Background Art

[0002] In the field of commercial vehicles, the distributed wire control brake system is a typical overdrive system with redundant actuators. The four wheels have the characteristics of independent braking, which provides conditions for the vehicle to achieve additional yaw torque through differential braking. However, a completely independent braking system also brings about the problem of complex control.

[0003] The direct yaw moment control method is currently the mainstream method in the field of vehicle stability control. The existing additional yaw moment is mostly achieved through a single controller method. There is still little research on the vehicle stability control of the four-wheel independent braking system by jointly calculating the optimal additional yaw moment through two controllers.

[0004] Therefore, it is necessary to develop a distributed wire-controlled commercial vehicle braking stability simulation method based on model prediction and sliding mode control to solve the above problems. Summary of the invention

[0005] The main purpose of the present invention is to overcome the defect of complex whole vehicle braking stability control of distributed wire-controlled commercial vehicles in the prior art, and to propose a whole vehicle braking stability simulation method and electronic equipment for distributed wire-controlled vehicles.

[0006] The present invention adopts the following technical solution:

[0007] A method for simulating the braking stability of a distributed wire-controlled vehicle, comprising:

[0008] Establish the Trucksim vehicle controlled object model and select the input and output parameters of the joint simulation;

[0009] A simulink control model is established, wherein the simulink control model calculates the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity in real time according to some of the input parameters, and then designs the optimal additional yaw moment in combination with a quadratic programming algorithm;

[0010] The Simulink control model calculates the tire vertical load in real time according to the remaining input parameters, and then uses the optimal distribution algorithm in combination with the optimal additional yaw moment to design the optimal braking force distribution value. The output parameter is calculated through the optimal braking force distribution value to control the posture of the vehicle in the Trucksim whole vehicle controlled object model, thereby forming a control closed loop.

[0011] The Trucksim vehicle controlled object model includes a model database, a model solver and simulation post-processing; the model database is used to set the basic parameters, operation scenarios and control inputs of the vehicle; the model solver is used to set simulation parameters and the interface with the simulink control model; the simulation post-processing is used to view the operation results.

[0012] The input parameters of the joint simulation include front wheel turning angle, lateral acceleration, longitudinal acceleration, vehicle speed, center of mass sideslip angle and yaw angular velocity; the output parameters of the joint simulation include left front braking torque, right front braking torque, left rear braking torque and right rear braking torque.

[0013] The Simulink control model includes a vehicle dynamics model, a vehicle braking stability joint controller and a distributed wire-controlled vehicle braking execution system; the vehicle dynamics model calculates part of the transmitted input parameters to obtain the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity at the current moment; the vehicle braking stability joint controller calculates multiple additional yaw moments according to the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity, and adopts a joint coefficient allocation strategy for the multiple additional yaw moments to obtain the optimal additional yaw moment; the distributed wire-controlled vehicle braking execution system transmits the braking torque demand through a bus and executes braking force size control.

[0014] The vehicle braking stability joint controller includes an MPC controller, and the additional yaw moment is calculated by using the MPC controller, which specifically includes the following steps:

[0015] The MPC controller calculates the required additional yaw moment prediction equation according to the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity;

[0016] For the prediction equation, the deviation between the predicted state quantity and the expected input and the constraint on the control quantity are designed, and the optimization objective function is defined, and the system state is close to the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity as the optimization target;

[0017] The optimization objective function is solved by a quadratic programming solution method, and the first element of the obtained optimization sequence is the additional yaw moment.

[0018] The vehicle braking stability joint controller includes a sliding mode controller, and the additional yaw moment is calculated by using the sliding mode controller, which specifically includes the following steps:

[0019] Based on the two-degree-of-freedom vehicle model, the differential equations of the two control state variables, the expected value of the center of mass slip angle and the expected value of the yaw angular velocity, are derived respectively;

[0020] Sliding mode control of the sideslip angle at the center of mass: the yaw rate tracking error and its derivative are calculated according to the differential equation of the sideslip angle at the center of mass, the sliding mode surface is defined according to the yaw rate tracking error and its derivative, the sliding film approach method selects the constant velocity approach law, and the additional yaw moment expression is derived;

[0021] Yaw angular rate sliding mode control: the yaw angular rate tracking error and its derivative are calculated according to the differential equation of the yaw angular rate, the sliding mode surface is defined according to the yaw angular rate tracking error and its derivative, the sliding film approach method selects the constant speed approach law, and the additional yaw moment expression is derived.

[0022] The vehicle braking stability joint controller includes an MPC controller and a sliding mode controller. The MPC controller and the sliding mode controller are used to calculate a plurality of the additional yaw moments, respectively, and the optimal additional yaw moment is obtained by combining the joint coefficient allocation strategy. The specific expression is as follows:

[0023] ΔM=η 1 ΔM smc_wz +η 2 ΔM smc_β +η 3 ΔM mpc_β_wz ;

[0024] Among them, ΔM smc_wz , ΔM smc_β is the additional yaw moment calculated using the sliding mode controller, ΔM mpc_β_wz is the additional yaw moment calculated by the MPC controller; η 1 , η 2 and η 3 is the joint coefficient.

[0025] The simulink control model calculates the tire vertical load in real time according to the remaining input parameters, and then uses the optimal distribution algorithm in combination with the optimal additional yaw moment to design the optimal braking force distribution value, and calculates the output parameter through the optimal braking force distribution value, which specifically includes the following:

[0026] The S function is used to call the sequential quadratic programming algorithm in the fmincon optimization toolbox to solve the problem and obtain the optimal distribution of braking force under different working conditions:

[0027] The vertical load of each wheel is calculated by the input parameters, and for optimal distribution of braking force under different working conditions, the difference between the slip rate of each wheel and the optimal slip rate is used as the optimization target, and an objective function is established to determine the slip rate of each wheel;

[0028] According to the yaw moment generated during braking being equal to the optimal additional yaw moment, the braking force of each wheel should satisfy the relationship;

[0029] According to the vertical load of each wheel, the braking force of each wheel should satisfy the relationship and the Burckhardt tire model The nonlinear relationship gives the relationship between slip ratio and tire longitudinal braking force;

[0030] The tire longitudinal braking force is calculated by bringing the slip rate of each wheel into the relationship between the slip rate and the tire longitudinal braking force, which is the optimal braking force distribution value;

[0031] The optimal braking force distribution value is combined with the tire radius to calculate the output parameter.

[0032] It also includes introducing the drive motor braking energy recovery function to participate in the joint control of the optimal additional yaw torque, that is, the longitudinal braking force of the wheel corresponding to the failed brake is provided by the drive motor energy recovery function, and the braking force of each wheel satisfies the following relationship:

[0033]

[0034] Where c is the equivalent wheelbase, a is the distance from the center of mass to the front wheel, δ is the equivalent front wheel turning angle rad, F xi is the longitudinal braking force of each wheel. When i is 1, 2, 3, and 4, it represents the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel respectively. ΔM is the optimal additional yaw moment. When the brake fails, F xi The braking force is recovered by the braking energy of the driving motor.

[0035] An electronic device, comprising:

[0036] one or more processors;

[0037] a storage device for storing one or more programs,

[0038] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for simulating the braking stability of a distributed wire-controlled vehicle.

[0039] It can be seen from the above description of the present invention that, compared with the prior art, the present invention has the following beneficial effects:

[0040] The method of the present invention establishes a Trucksim whole vehicle controlled object model, takes Trucksim whole vehicle signals (yaw angular velocity, center of mass sideslip angle, etc.) as input, uses MATLAB-simulink to build a control model, and performs joint control by calculating the optimal additional yaw moment through a model predictive controller (MPC) and a sliding mode controller (SMC). The optimal additional yaw moment is used to obtain the optimal braking force distribution value through a quadratic programming algorithm, and the optimal distribution value is input back into the Trucksim whole vehicle controlled object model to form a closed-loop control, thereby providing a simulation platform for the whole vehicle braking stability performance analysis, control strategy optimization, etc. of distributed wire-controlled vehicles.

[0041] The present invention also proposes to utilize the braking energy recovery function of the driving motor to participate in the optimal additional yaw torque joint control when a wheel-end braking system fails, thereby ensuring the stability and safety of the braking condition of the distributed wire-controlled vehicle under normal circumstances and when a wheel-end braking system fails. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a control schematic diagram of the present invention;

[0043] Figure 2 It is a schematic diagram of the two-degree-of-freedom vehicle steering model of the present invention;

[0044] Figure 3 It is a schematic diagram of the final additional yaw moment of the combined control of the present invention;

[0045] Figure 4 This is a schematic diagram of the driving motor braking energy recovery function participating in the optimal additional yaw torque joint control of the present invention.

[0046] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. DETAILED DESCRIPTION

[0047] The present invention is further described below through specific implementation modes.

[0048] See also Figures 1 to 4 A method for simulating the braking stability of a distributed wire-controlled vehicle comprises the following steps:

[0049] S1 establishes the Trucksim vehicle controlled object model and selects the input parameters and output parameters of the joint simulation.

[0050] The Trucksim vehicle controlled object model uses a graphical operation interface, which mainly includes three parts: model database, model solver and simulation post-processing. To establish the Trucksim vehicle controlled object model, it is necessary to complete the settings of the above three parts. The model database is used to set the basic parameters, operation scenarios, control inputs, etc. of the vehicle; the model solver is used to set simulation parameters and the interface with the simulink control model; the simulation post-processing part is used to view the operation results, including two modules: animation demonstration and graphic drawing;

[0051] The selected joint simulation input parameters and output parameters are as follows: front wheel angle, lateral acceleration, longitudinal acceleration, vehicle speed, sideslip angle of center of mass, and yaw angular velocity are selected as output parameters of the simulation; the left front, right front, left rear, and right rear braking torques are selected as input parameters of the simulation.

[0052] S2 establishes a simulink control model. The simulink control model calculates the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity in real time according to some input parameters, and then combines the quadratic programming algorithm to design the optimal additional yaw moment.

[0053] Through the model interface, the input parameters of the Trucksim vehicle controlled object model, including the front wheel angle, lateral acceleration, longitudinal acceleration, vehicle speed, sideslip angle of the center of mass, and yaw angular velocity, are transmitted to the Simulink control model through joint simulation. The Simulink control model receives the parameters through the model interface to design the control system.

[0054] The simulink control model includes a vehicle dynamics model, a vehicle braking stability joint controller, and a distributed wire-controlled vehicle braking execution system; the vehicle dynamics model uses a two-degree-of-freedom vehicle steering model, which calculates some of the input parameters (front wheel angle, vehicle speed, center of mass slip angle, and yaw rate) to obtain the expected value of the center of mass slip angle and the expected value of the yaw rate at the current moment; the vehicle braking stability joint controller calculates multiple additional yaw moments based on the expected value of the center of mass slip angle and the expected value of the yaw rate, and uses a joint coefficient allocation strategy for multiple additional yaw moments to obtain the optimal additional yaw moment. The distributed wire-controlled vehicle braking execution system is used to make the four wheel ends of the vehicle completely independent, transmit the braking torque requirements through the bus, and execute the braking force size control.

[0055] Specifically, the vehicle braking stability joint controller includes an MPC controller, which calculates the required additional yaw moment prediction equation according to the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity; for the prediction equation, the deviation between the predicted state quantity and the expected input and the constraint on the control quantity are designed, and the optimization objective function is defined, and the system state is approximated to the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity as the optimization target; the optimization objective function is solved by the quadratic programming solution method, and the first element of the obtained optimization sequence is the additional yaw moment, which is as follows:

[0056] 1) Based on the two-degree-of-freedom car model, the nonlinear state space equation of the system is obtained as follows

[0057] The sum of the resultant force along the y-axis and the moment around the center of mass of the two-degree-of-freedom car is:

[0058]

[0059] Among them, m is the vehicle mass Kg; is the lateral acceleration of the vehicle m / s 2 ; u is the longitudinal speed of the vehicle km / h; ω z is the vehicle yaw angular velocity rad / s; F y1 is the lateral reaction force of the front wheel on the ground N; δ 1 is the front wheel turning angle rad; F y2 is the lateral reaction force of the rear wheel on the ground N; I z The moment of inertia of the vehicle is kg.m 2 ; is the vehicle yaw angular acceleration rad / s 2 ; l 1 is the distance from the center of mass of the vehicle to the front wheel m; l 2 ΔM is the distance from the center of mass of the vehicle to the rear wheel in m; z The additional yaw moment Nm is the vehicle's center of mass around the Z axis.

[0060] The nonlinear state space equation of the system can be obtained:

[0061]

[0062] in, is the mass center sideslip angular velocity rad / s, is the vehicle yaw angular acceleration rad / s 2 .

[0063] 2) Linearize the nonlinear state space equation of the system and expand it into the standard state space equation form to obtain the reference model after local linearization, as follows:

[0064] Linearize the system's nonlinear state-space equations:

[0065]

[0066] Where x = [β, ω z ] T , x is the sideslip angle β and the yaw rate ω z Matrix, u = ΔM z , u is the additional yaw moment of the vehicle center of mass around the Z axis Nm, To solve the sign of the partial derivative, f(0,0) is the initial state of the linearization.

[0067] Expanded into standard state-space equation form:

[0068]

[0069] Among them, the A matrix is ​​the partial derivative matrix of the linearized function f(x,u) with respect to x; the B matrix is ​​the partial derivative matrix of the linearized function f(x,u) with respect to u; the C matrix is ​​the two-dimensional unit matrix; the E matrix is ​​the linearized initial state, k 1 is the front wheel cornering stiffness N / rad; the reference model after local linearization is:

[0070]

[0071] in, is the local linearization control A matrix element, and the other parameters are the same as above.

[0072] 3) Discretize the obtained reference model to obtain a discrete state space equation; change the input variable of the discrete state space equation into an incremental form, and define a new state variable to obtain a new discrete state space equation. The details are as follows:

[0073] Discrete state-space equations:

[0074] x(k+1)=G(T)x(k)+H(T)u(k)+B d d;

[0075] Where G(T), H(T), and Bd are:

[0076]

[0077] Among them, G(T) is the discretized state control matrix, H(T) is the discretized input control matrix, B d is the discretized disturbance control matrix, d is the disturbance, x(k) is the state variable at time k, u(k) is the input variable at time k, k is the current calculation time step, and T is the period; A is the local linearization control matrix, I is the unit matrix, B is the local linearization input control matrix, B cdis the local linearized disturbance control matrix.

[0078] By changing the input u of the system into the incremental form Δu, we can obtain:

[0079] x(k+1)=Gx(k)+Hu(k-1)+HΔu(k)+B d d

[0080] u(k)=u(k-1)+Δu(k);

[0081] Among them, x(k+1) is the state variable at time k+1 after discretization, G is the state control matrix after changing to incremental form, H is the input control matrix after changing to incremental form, u(k-1) is the input variable at time k-1 after discretization, and Δu(k) is the input increment at time k.

[0082] Define a new discrete system state variable ξ:

[0083] ξ(k)=[x(k),u(k-1)] T ;

[0084] Among them, ξ(k) is the new discrete system state variable matrix.

[0085] The discrete state space equation with ξ as the state variable is:

[0086]

[0087] in They are:

[0088]

[0089] Among them, ξ(k+1) is the discrete system state variable matrix at time k+1, is the new discrete system state control matrix, Enter the control matrix for the new discrete system, is the disturbance control matrix for the new discrete system.

[0090] The output variable y is:

[0091]

[0092] in for:

[0093]

[0094] Among them, y(k) is the output variable matrix at time k, Output control matrix at time k.

[0095] 4) The prediction equation is obtained according to the new discrete state space equation, and the reference yaw rate expected value and the reference center of mass sideslip angle expected value are used as expected value reference inputs.

[0096] Specifically, the prediction equation with a prediction time domain of p steps and a control time domain of m steps can be obtained:

[0097] Y(k+1|k)=S ξ ξ(k)+S u ΔU(k)+S d d;

[0098]

[0099] Among them, Y(k+1|k) is the output matrix of time k predicting time k+1, S ξ is the state control matrix of the prediction equation, S u Input control matrix for prediction equation, S d is the disturbance control matrix of the prediction equation.

[0100] The expected value of yaw rate and the expected value of reference center of mass sideslip angle are used as reference inputs, that is, r = [β r ,ω r ] T , define the reference value sequence:

[0101] R k =[r(k+1|k) T ,r(k+2|k) T ,r(k+3|k) T ...,r(k+p|k) T ] T ;

[0102] Among them, R k is the reference value sequence matrix at time k, r(k+1|k) T The reference input matrix for predicting time k+1 from time k.

[0103] 5) For the prediction equation, design the deviation between the predicted state quantity and the reference input and the constraints on the control quantity, and define the optimization objective function, taking the system state approaching the expected trajectory as the optimization goal.

[0104] In order to make the system state follow the expected reference input, the deviation between the predicted state and the reference value and the constraint on the control quantity are designed, and the optimization objective function is defined:

[0105]

[0106] Among them, J(U k ) is the optimization objective function, X kis the input state variable matrix at time k, R k is the reference value sequence matrix at time k, U k is the control variable matrix at time k, Q and W are weight coefficient matrices, Q = diag(q 1 ,q 2 ,...,q 2p )、W=diag(w 1 ,w 2 ,...,w p ).

[0107] The optimization goal is to make the system state approach the desired trajectory, at which point the above objective function value is minimized. Therefore, the optimization goal can be described as:

[0108] minJ(U k )st|u(k+i|k)|≤u max ,i=0,1,2,...,p-1;

[0109] Among them, u(k+i|k) is the control variable predicted at time k+1, and u max is the maximum value of the control variable at all times;

[0110] Arrange the optimization objective function:

[0111]

[0112] Among them, D k is the disturbance variable matrix at time k.

[0113] Let G = S ξ x(k)-R k +S d D k , the above formula is rearranged to get:

[0114]

[0115] The last item G T QG is a constant term and has no effect on finding the optimal sequence; therefore, it can be omitted and written as the standard form of quadratic programming:

[0116]

[0117] 6) The optimization objective function is solved by the quadratic programming method, and the first element of the obtained optimization sequence is the additional yaw moment. That is, the additional yaw moment required for vehicle lateral stability control is obtained by using the model predictive control algorithm.

[0118] In this embodiment, the vehicle braking stability joint controller includes a sliding mode controller, and the additional yaw moment is calculated by using the sliding mode controller, which specifically includes the following:

[0119] 1) According to the two-degree-of-freedom vehicle model, the differential equations of the two control state variables, the sideslip angle of the center of mass and the yaw rate, are derived respectively. Based on the two-degree-of-freedom vehicle model, the sliding mode controller is designed starting from the two control state variables, the sideslip angle of the center of mass and the yaw rate: The original two-degree-of-freedom model differential equation can be rewritten as:

[0120]

[0121] Among them, k f is the total cornering stiffness of the front wheel N / rad, k r is the total cornering stiffness of the rear wheel N / rad, v x is the longitudinal speed of the vehicle km / h, L f is the distance from the center of mass to the front axle, m, L r is the distance from the center of mass to the rear axle m, δ f is the front wheel steering angle rad, ΔM wz is the additional yaw moment.

[0122] 2) Sliding mode control of center of mass sideslip angle: Calculate the yaw rate tracking error and its derivative according to the differential equation of the center of mass sideslip angle, define the sliding mode surface according to the yaw rate tracking error and its derivative, select the constant velocity approach law as the sliding film approach method, and derive the additional yaw moment expression. The details are as follows:

[0123] Define the yaw rate tracking error and its derivative:

[0124] e β =β-β z_exp

[0125]

[0126] Among them, e β is the center of mass sideslip angle tracking error, β z_exp Ideal center of mass sideslip angle rad, β Actual center of mass sideslip angle rad, is the center of mass sideslip angular velocity tracking error, Ideal center of mass sideslip angular velocity rad / s, Actual center of mass sideslip angular velocity rad / s.

[0127] Define the sliding surface:

[0128]

[0129] Among them, s β is the sliding surface parameter of the centroid slip angle, c β is the relative weight coefficient between the error and the error change rate, which is greater than 0. The other parameters are the same as described above and will not be repeated here;

[0130] The synovial approach method selects the isokinetic approach law, that is, but:

[0131]

[0132] The additional yaw moment is

[0133]

[0134] in, is the sliding surface derivative of the sideslip angle at the center of mass, K β is the sliding mode constant velocity approach coefficient of the sideslip angle at the center of mass, sign(s β ) is the sign function of the sideslip angle at the center of mass; is the second derivative of the center of mass sideslip angle tracking error, Front wheel angular velocity rad / s, Ideal center of mass sideslip angle acceleration rad / s^ 2 ,ΔM smc_β is the attachment yaw moment caused by the sideslip angle at the center of mass.

[0135] 3) Yaw angular velocity sliding mode control: Calculate the yaw angular velocity tracking error and its derivatives according to the differential equation of the yaw angular velocity, define the sliding mode surface according to the yaw angular velocity tracking error and its derivatives, select the constant velocity approach law as the sliding film approach method, and derive the additional yaw torque expression. The details are as follows:

[0136] Define the yaw rate tracking error and its derivative:

[0137] e ωz =ω z -ω z_exp

[0138]

[0139] Among them, e ωz is the yaw rate tracking error, ω z_exp Ideal yaw rate rad / s, ω z Actual yaw rate rad / s, is the yaw angular acceleration tracking error, Ideal yaw angular acceleration rad / s^ 2 , Actual yaw angular acceleration rad / s^ 2 ;

[0140] Define the sliding surface:

[0141]

[0142] Among them, s ωzis the sliding surface parameter of yaw rate, c ωz is the relative weight coefficient between the error and the error change rate, which is greater than 0.

[0143] The synovial approach method selects the isokinetic approach law, that is, but:

[0144]

[0145] The additional yaw moment required for pushout is:

[0146]

[0147] in, is the sliding surface derivative of the yaw rate, K ω is the sliding mode constant velocity approach coefficient of yaw angular velocity, sign(s ωz ) is the sign function of the yaw rate; is the second derivative of the yaw rate tracking error, is the ideal yaw angular acceleration rad / s^ 2 ,ΔM smc_wz is the attachment yaw moment resulting from the yaw angle.

[0148] 4) The vehicle braking stability joint controller uses the MPC controller and the sliding mode controller to calculate multiple additional yaw moments, and combines the joint coefficient allocation strategy to obtain the optimal additional yaw moment.

[0149] Since the yaw rate and the sideslip angle are coupled, it is impossible to achieve a better control effect by controlling a single variable. Therefore, a coordinated control strategy is needed.

[0150] Define the system's current center of mass sideslip angle stable state as I β , the yaw angular velocity is in a stable state. ω The stable state expression and the corresponding value range are as follows. Its physical meaning indicates the deviation distance between the corresponding indicator and the stable state:

[0151]

[0152] When I β >1, β exceeds the saddle point and is in the unstable region; when I ω >1, ω exceeds the limit and is in the unstable region. Therefore, only when the stable state I β ≤1 and I ω When ≤1, the vehicle is in the stable area. ind =max(I ω ,I β ).

[0153] The final additional yaw moment of the joint control is:

[0154] ΔM=η 1 ΔM smc_wz +η 2 ΔM smc_β +η 3 ΔM mpc_β_wz ;

[0155] Among them, ΔM smc_wz , ΔM smc_β is the additional yaw moment calculated using the sliding mode controller, ΔM mpc_β_wz is the additional yaw moment calculated by the MPC controller; η 1 , η 2 and η 3 is the joint coefficient. Among them:

[0156]

[0157] The S3simulink control model calculates the tire vertical load in real time according to the input parameters, and then uses the optimal distribution algorithm combined with the optimal additional yaw moment to design the optimal braking force distribution value. The output parameters are calculated through the optimal braking force distribution value to control the vehicle posture in the Trucksim vehicle controlled object model, thus forming a control closed loop.

[0158] The details include:

[0159] 1) Use the S function to call the sequential quadratic programming algorithm in the fmincon optimization toolbox to solve the problem and obtain the optimal distribution of braking force under different working conditions; convert the problem to be solved into the following fmincon format basic form:

[0160]

[0161] In the formula,

[0162] C(x),C eq (x) is the constraint function of the nonlinear constraint condition;

[0163] A eq ,b eq is the coefficient matrix of the linear equality constraints;

[0164] A, b are the coefficient matrices of linear inequality constraints;

[0165] lb, ub are the upper and lower bounds of the optimization variables.

[0166] The specific function calling format of the fmincon command is as follows:

[0167] [x] = fmincon(fun,x0 ,A,b,A eq ,b eq ,lb,ub,nonlcon,options)

[0168] Where fun is the optimization objective function; x 0 are the initial values ​​of the optimization variables; nonlcon is the nonlinear constraint; option is other parameter setting options, such as algorithm, maximum number of iterations, etc.

[0169] 2) Calculate the vertical load of each wheel through the remaining input parameters as follows:

[0170]

[0171] Where Fz1, Fz2, Fz3, and Fz4 are the vertical loads of the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively; a and b are the distances from the center of mass of the vehicle to the front axle and rear axle respectively; c is the wheelbase; h is the wheelbase of the vehicle. g is the height of the vehicle's center of mass; a x is the lateral acceleration of the vehicle's center of mass; a y is the longitudinal acceleration of the vehicle's center of mass; L is the vehicle's wheelbase.

[0172] For the optimal distribution of braking force under different working conditions, the difference between the slip rate of each wheel and the optimal slip rate is taken as the optimization target, and an objective function is established to determine the slip rate of each wheel. The objective function is as follows:

[0173] min J=∑(λ i -λ p ) 2 i=1,2,3,4

[0174] Where λ i is the slip rate of each wheel. When i is 1, 2, 3, and 4, it represents the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel respectively; λ p is the optimal slip rate under the current road conditions.

[0175] The braking torque that each brake can provide is constrained by physical conditions such as its motor power and transmission mechanism size. Therefore, the braking torque of each wheel must meet the following requirements:

[0176] T i =F xi r≤T max i=1,2,3,4

[0177] Where T i is the braking torque of each wheel (Nm); r is the tire rolling radius; T maxis the maximum braking torque that each brake can provide (Nm). To ensure that each wheel can generate a large longitudinal braking force and that the tire has good lateral adhesion during braking, the wheel slip ratio should meet the following requirements:

[0178] 0≤λ i ≤0.3 i=1,2,3,4

[0179] 3) According to the yaw moment generated during braking being equal to the optimal additional yaw moment, the braking force of each wheel should satisfy the relationship; the braking force of each wheel should satisfy the following relationship:

[0180]

[0181] Where c is the equivalent wheelbase m, a is the distance from the center of mass to the front wheel, δ is the equivalent front wheel turning angle rad, F xi is the longitudinal braking force of each wheel. When i is 1, 2, 3, and 4, it represents the left front wheel, right front wheel, left rear wheel, and right rear wheel (N). ΔM is the additional yaw moment required to ensure the stability of the vehicle.

[0182] 4) According to the vertical load of each wheel, the braking force of each wheel should satisfy the relationship and Burckhardt tire model The nonlinear relationship gives the relationship between slip ratio and tire longitudinal braking force; it is as follows:

[0183]

[0184] In the formula, λ i is the slip rate, is the slip rate λ i Corresponding longitudinal adhesion coefficient; F zi are the vertical loads of the corresponding wheels (N), and when i is 1, 2, 3, and 4, they represent the left front wheel, right front wheel, left rear wheel, and right rear wheel (N), respectively; c 1 , c 2 , c 3 are the empirical coefficients respectively.

[0185] 5) Substituting the obtained slip rate of each wheel into the relationship between the slip rate and the tire longitudinal braking force to calculate the tire longitudinal braking force is the optimal braking force distribution value.

[0186] 6) Combine the optimal braking force distribution value with the tire radius to calculate the output parameters, including the left front braking torque, right front braking torque, left rear braking torque and right rear braking torque, for closed-loop control, thereby ensuring the stability and safety of the braking condition of the distributed wire-controlled vehicle under normal circumstances.

[0187] The calculated tire longitudinal braking force is input back into the Trucksim vehicle controlled object model to form a closed-loop control, thereby ensuring the stability and safety of the distributed wire-controlled vehicle under normal vehicle braking conditions.

[0188] In this embodiment, the drive motor braking energy recovery function is also introduced to participate in the optimal additional yaw torque joint control, so as to ensure the stability and safety of the braking condition of the distributed wire-controlled vehicle when a wheel-end braking system fails. From the perspective of the whole vehicle, during the emergency braking process when a single wheel braking force is lost, the whole vehicle controller transmits the single wheel braking force loss signal and the single wheel drive motor braking energy recovery torque value to the sliding mode controller and the MPC controller. The sliding mode controller and the MPC controller recalculate the braking yaw torque required for the other three wheels based on the information obtained, which should meet the requirements of the additional yaw torque required to maintain the stability of the whole vehicle. The control diagram is shown in the figure. Figure 4 .

[0189] When the brake of a wheel fails, the controller transmits a control signal to the drive motor, and the drive motor then starts the braking energy recovery function (i.e., uses the drive motor as a generator to absorb the rotational kinetic energy of the tire, thereby forming a braking force). As mentioned above, when the brake of a wheel fails, the drive motor can be used as a backup for the braking force.

[0190] Therefore, the braking force of each wheel should satisfy the following relationship, and also include the introduction of the drive motor braking energy recovery function to participate in the joint control of the optimal additional yaw moment. The braking force of each wheel satisfies the following relationship:

[0191]

[0192] Where c is the equivalent wheelbase, a is the distance from the center of mass to the front wheel, δ is the equivalent front wheel turning angle rad, F xi is the braking force of the drive motor when the brake fails. When i is 1, 2, 3, and 4, it represents the left front wheel, right front wheel, left rear wheel, and right rear wheel respectively. ΔM is the optimal additional yaw moment. The longitudinal braking force of the wheel corresponding to the failed brake is provided by the energy recovery function of the drive motor.

[0193] The simulation method of this embodiment solves the complex problem of vehicle braking stability control of distributed wire-controlled commercial vehicles and improves the vehicle braking stability when a single wheel braking force is lost.

[0194] Based on this, the present invention further proposes an electronic device, comprising:

[0195] one or more processors;

[0196] a storage device for storing one or more programs,

[0197] When one or more programs are executed by one or more processors, the one or more processors implement the above-mentioned method for simulating the braking stability of a distributed wire-controlled vehicle.

[0198] The above is only a specific implementation of the present invention, but the design concept of the present invention is not limited to this. Any non-substantial changes to the present invention using this concept shall be deemed as an infringement of the protection scope of the present invention.

Claims

1. A method for simulating the braking stability of a distributed wire-controlled vehicle, characterized in that: include Establish the Trucksim vehicle controlled object model and select the input and output parameters of the joint simulation; A simulink control model is established, wherein the simulink control model calculates the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity in real time according to some of the input parameters, and then designs the optimal additional yaw moment in combination with a quadratic programming algorithm; The Simulink control model calculates the tire vertical load in real time according to the remaining input parameters, and then uses the optimal distribution algorithm in combination with the optimal additional yaw moment to design the optimal braking force distribution value. The output parameter is calculated through the optimal braking force distribution value to control the posture of the vehicle in the Trucksim whole vehicle controlled object model, thereby forming a control closed loop.

2. The method for simulating the braking stability of a distributed wire-controlled vehicle according to claim 1, characterized in that: The Trucksim vehicle controlled object model includes a model database, a model solver and simulation post-processing; the model database is used to set the basic parameters, operation scenarios and control inputs of the vehicle; the model solver is used to set simulation parameters and the interface with the simulink control model; the simulation post-processing is used to view the operation results.

3. The method for simulating the braking stability of a distributed wire-controlled vehicle according to claim 1, characterized in that: The input parameters of the joint simulation include front wheel turning angle, lateral acceleration, longitudinal acceleration, vehicle speed, center of mass sideslip angle and yaw angular velocity; the output parameters of the joint simulation include left front braking torque, right front braking torque, left rear braking torque and right rear braking torque.

4. The method for simulating the braking stability of a distributed wire-controlled vehicle according to claim 1, characterized in that: The Simulink control model includes a vehicle dynamics model, a vehicle braking stability joint controller and a distributed wire-controlled vehicle braking execution system; the vehicle dynamics model calculates part of the transmitted input parameters to obtain the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity at the current moment; the vehicle braking stability joint controller calculates multiple additional yaw moments according to the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity, and adopts a joint coefficient allocation strategy for the multiple additional yaw moments to obtain the optimal additional yaw moment; the distributed wire-controlled vehicle braking execution system transmits the braking torque demand through a bus and executes braking force size control.

5. The method for simulating the braking stability of a distributed wire-controlled vehicle according to claim 4, characterized in that: The vehicle braking stability joint controller includes an MPC controller, and the additional yaw moment is calculated by using the MPC controller, which specifically includes the following steps: The MPC controller calculates the required additional yaw moment prediction equation according to the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity; For the prediction equation, the deviation between the predicted state quantity and the expected input and the constraint on the control quantity are designed, and the optimization objective function is defined, and the system state is close to the expected value of the center of mass sideslip angle and the expected value of the yaw angular velocity as the optimization target; The optimization objective function is solved by a quadratic programming solution method, and the first element of the obtained optimization sequence is the additional yaw moment.

6. The method for simulating the braking stability of a distributed wire-controlled vehicle according to claim 4, characterized in that: The vehicle braking stability joint controller includes a sliding mode controller, and the additional yaw moment is calculated by using the sliding mode controller, which specifically includes the following steps: Based on the two-degree-of-freedom vehicle model, the differential equations of the two control state variables, the expected value of the center of mass slip angle and the expected value of the yaw angular velocity, are derived respectively; Sliding mode control of the sideslip angle at the center of mass: the yaw rate tracking error and its derivative are calculated according to the differential equation of the sideslip angle at the center of mass, the sliding mode surface is defined according to the yaw rate tracking error and its derivative, the sliding film approach method selects the constant velocity approach law, and the additional yaw moment expression is derived; Yaw angular rate sliding mode control: the yaw angular rate tracking error and its derivative are calculated according to the differential equation of the yaw angular rate, the sliding mode surface is defined according to the yaw angular rate tracking error and its derivative, the sliding film approach method selects the constant speed approach law, and the additional yaw moment expression is derived.

7. The method for simulating the braking stability of a distributed wire-controlled vehicle according to claim 4, characterized in that: The vehicle braking stability joint controller includes an MPC controller and a sliding mode controller. The MPC controller and the sliding mode controller are used to calculate a plurality of the additional yaw moments, respectively, and the optimal additional yaw moment is obtained by combining the joint coefficient allocation strategy. The specific expression is as follows: ΔM=η1ΔM smc_wz +η2ΔM smc_β +η3ΔM mpc_β_wz Among them, ΔM smc_wz , ΔM smc_β is the additional yaw moment calculated using the sliding mode controller, ΔM mpc_β_wz is the additional yaw moment calculated by the MPC controller; η1, η2 and η3 are the joint coefficients.

8. The method for simulating the braking stability of a distributed wire-controlled vehicle according to claim 1, characterized in that: The simulink control model calculates the tire vertical load in real time according to the remaining input parameters, and then uses the optimal distribution algorithm in combination with the optimal additional yaw moment to design the optimal braking force distribution value, and calculates the output parameter through the optimal braking force distribution value, which specifically includes the following: The S function is used to call the sequential quadratic programming algorithm in the fmincon optimization toolbox to solve the problem and obtain the optimal distribution of braking force under different working conditions: The vertical load of each wheel is calculated by the input parameters, and for optimal distribution of braking force under different working conditions, the difference between the slip rate of each wheel and the optimal slip rate is used as the optimization target, and an objective function is established to determine the slip rate of each wheel; According to the yaw moment generated during braking being equal to the optimal additional yaw moment, the braking force of each wheel should satisfy the relationship; According to the vertical load of each wheel, the braking force of each wheel should satisfy the relationship and the Burckhardt tire model The nonlinear relationship gives the relationship between slip ratio and tire longitudinal braking force; The tire longitudinal braking force is calculated by bringing the slip rate of each wheel into the relationship between the slip rate and the tire longitudinal braking force, which is the optimal braking force distribution value; The optimal braking force distribution value is combined with the tire radius to calculate the output parameter.

9. The method for simulating the braking stability of a distributed wire-controlled vehicle according to claim 1, characterized in that: It also includes introducing the drive motor braking energy recovery function to participate in the joint control of the optimal additional yaw torque, that is, the longitudinal braking force of the wheel corresponding to the failed brake is provided by the drive motor energy recovery function, and the braking force of each wheel satisfies the following relationship: Where c is the equivalent wheelbase, a is the distance from the center of mass to the front wheel, δ is the equivalent front wheel turning angle rad, F xi is the longitudinal braking force of each wheel. When i is 1, 2, 3, and 4, it represents the left front wheel, the right front wheel, the left rear wheel, and the right rear wheel respectively. ΔM is the optimal additional yaw moment. When the brake fails, F xi The braking force is recovered by the braking energy of the driving motor.

10. An electronic device, comprising: one or more processors; a storage device for storing one or more programs, It is characterized in that when the one or more programs are executed by the one or more processors, the one or more processors implement a vehicle braking stability simulation method for a distributed wire-controlled vehicle as described in any one of claims 1-9.

Images