Electric vehicle simulation model test control platform and method

Abstract

The invention relates to the technical field of data processing, and discloses an electric vehicle simulation model test control platform and method.The method comprises the steps that firstly, kinetic models of a pure electric vehicle transmission system and a suspension system are established; carrying out adaptive control system modeling; specifically, modeling is carried out through a second-order linear differential equation; model parameter estimation is carried out; designing a self-adaptive controller according to the estimated parameters; under different working conditions, the jitter suppression effect is evaluated, and optimization is carried out by adjusting controller parameters including a positive definite constant or a switching gain; prediction is carried out through the model, and a prediction model of the system is established; at each sampling moment, the optimal control input at the current moment is obtained through solving; and carrying out rolling optimization: only executing the optimal control input at the current moment, repeating the steps at the next sampling moment, and carrying out prediction and optimization based on new measurement information. According to the method, the actual situation can be reflected more accurately, and the simulation result has higher authenticity and reference value.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an electric vehicle simulation model test control platform and method. Background Art

[0002] When building a simulation platform for vibration analysis and active and passive control methods for pure electric vehicles, in addition to simulating the impact and torsion of the motor on the transmission system, the suspension system, the transmission system, the transmission relationship of the rotor-gear system inside the powertrain, and considering the half-shaft as a flexible system, it is also necessary to comprehensively consider the following key aspects to ensure the comprehensiveness and accuracy of the simulation: Motor and electronic control system simulation: Motor model: Establish a mathematical model including electrical characteristics, mechanical characteristics, and magnetic field characteristics to accurately reflect the actual working state of the motor. Electronic control system simulation: Simulate the influence of the electronic control strategy on the motor speed and torque output to ensure the stable operation of the motor under different working conditions. Vehicle dynamics simulation: Vehicle dynamics model: Establish a mathematical model including mass, inertia, suspension characteristics, and tire characteristics to describe the motion state and response characteristics of the vehicle under different driving conditions. Acceleration, braking and steering simulation: Simulate the dynamic performance of the vehicle during acceleration, braking and steering to evaluate the stability and handling of the vehicle.

[0003] Therefore, in response to the above simulation requirements, an electric vehicle simulation model test control platform and method are needed. Summary of the invention

[0004] The purpose of the present invention is to provide an electric vehicle simulation model test control platform and method. The present invention brings many beneficial effects such as flexibility, high efficiency, modular design, collaborative work, reusability, integrated environment, real-time support, low cost and high efficiency, and teaching and research value through the operation method, system module and platform design of AI model training on an embedded platform. These effects jointly promote the development and application of embedded AI technology.

[0005] The present invention is achieved in that:

[0006] The present invention provides an electric vehicle simulation model test control method which is specifically performed according to the following steps:

[0007] S1: Establish the dynamic model of the transmission system and suspension system of the pure electric vehicle; perform system modeling for adaptive control; specifically, perform modeling through a second-order linear differential equation; the modeling through a second-order linear differential equation is as follows:

[0008]

[0009] Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, x is the displacement vector, and F is the external force vector, including the excitation force generated by the motor torque.

[0010] S2: Estimating model parameters, specifically using the recursive least squares method to estimate system parameters in real time; such as motor torque, transmission system stiffness and damping, as shown in the following formula;

[0011]

[0012] in, is the parameter estimate at time k, K k is the gain matrix, y k is the measured output, is the regression vector, λ is the forgetting factor, P k is the covariance matrix.

[0013] S3: According to the estimated parameters, an adaptive controller is designed; the adaptive controller adopts an adaptive sliding mode controller; the sliding mode surface formula is as follows:

[0014]

[0015] Where, e is the error between the system output and the expected output, and λ is a positive definite constant;

[0016] The control law is designed as follows:

[0017] u=u eq +u dis

[0018] Among them, u eq is equivalent control, u dis is a switching control; equivalent control is achieved by The solution is obtained; switching control is used to overcome system uncertainty and interference.

[0019] S4: Simulate the adaptive control algorithm under different working conditions, including different vehicle speeds and road excitations, evaluate its effect in suppressing vibration, and optimize it by adjusting controller parameters including positive constants or switching gains;

[0020] S5: Then make predictions through the model to establish a prediction model of the system; the prediction model includes a prediction model based on the state space model; as shown in the following formula;

[0021] x k+1 =Ax k +Bu k

[0022] y k =Cx k

[0023] Among them, xk is the state vector at time k, u k is the control input, y k is the system output, A, B, C are system matrices. Then set the prediction time domain and control time domain to determine the prediction time domain N p and control time domain N c ;

[0024] Then define the objective function, including minimizing the sum of squares of the error between the system output and the expected output and the change in the control input; as follows:

[0025]

[0026] in, is k+k predicted based on information at time k p Output at any time, is the expected output, λ u is the weight coefficient of the control input change, Δu k =u k -u k-1 .

[0027] S6: At each sampling moment, the optimal control input u at the current moment is obtained by solving steps S1-S5 k , using the quadratic programming QP method to solve; and performing rolling optimization: only executing the optimal control input at the current moment, at the next sampling moment, repeating steps S1-S5, and performing prediction and optimization based on the new measurement information.

[0028] Furthermore, the present invention provides an electric vehicle simulation model test control platform, which specifically includes a parameter estimation module, which uses a recursive least squares method to estimate system parameters in real time, including motor torque, transmission system stiffness and damping parameters;

[0029] Controller design module: Design the controller according to the system parameters and control objectives; in adaptive control, design an adaptive sliding mode controller to control the system by defining the sliding surface and control law; in predictive control, obtain the optimal control input by solving the optimization problem to achieve predictive control of the system.

[0030] Working condition simulation module: simulates the control algorithm under different working conditions, including different vehicle speeds and road excitations; can simulate various working conditions that pure electric vehicles may encounter in actual operation, and provide a real scenario for evaluating the performance of the control algorithm. Through simulation under different working conditions, the vibration suppression effect of the control algorithm under various conditions is analyzed, so as to optimize the controller parameters.

[0031] Optimization and comparison module: optimizes the control algorithm and improves the control effect by adjusting the controller parameters, including the switching gain in adaptive control and the weight coefficient in predictive control. At the same time, it compares the advantages and disadvantages of different control algorithms (such as adaptive control and predictive control) in terms of vibration suppression, providing a basis for selecting the most suitable active control strategy for pure electric vehicles.

[0032] Furthermore, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the electric vehicle simulation model test control method described above is implemented.

[0033] Furthermore, the present invention provides a computer-storable medium, wherein the computer-readable storage medium includes an embedded processing system and a stored program, and controls the above-mentioned electric vehicle simulation model test control method when the embedded system control program runs.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. Real-time parameter estimation advantage: Recursive least squares (RLS) method is used to estimate system parameters in real time, such as motor torque, transmission system stiffness and damping. In simulation, parameter estimation can be dynamically adjusted according to the real-time operating status of the system to adapt to the changes in system parameters over time and working conditions. For pure electric vehicles, which have complex and changeable operating conditions, it can more accurately reflect the actual situation, making the simulation results more realistic and valuable for reference.

[0036] 2. Ability to cope with uncertainty: The design of the adaptive sliding mode controller can effectively overcome system uncertainty and interference through the sliding mode surface and control law, especially the switching control part. When simulating different working conditions (such as different vehicle speeds and road excitations), the ability of the controller to suppress jitter in complex environments can be verified, providing strong simulation support for improving system stability and robustness in practical applications.

[0037] 3. Flexible parameter optimization: During the simulation process, the controller parameters (such as switching gain, etc.) can be adjusted for optimization. This flexibility makes it possible to find the optimal controller parameter combination for different performance requirements and operating conditions, improve the overall performance of the system, and intuitively observe the impact of parameter changes on system performance, which helps to deeply understand the control characteristics of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It is understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0039] Figure 1 is a flow chart of the method of the present invention;

[0040] Figure 2 It is a system structure diagram of the present invention;

[0041] Figure 3 The cvxpy library of Python is used to solve the code Figure 1 ;

[0042] Figure 4 The cvxpy library of Python is used to solve the code Figure 2 . DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but is only for selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0044] See also Figure 1-Figure 4 , the present invention provides an electric vehicle simulation model test control platform and method;

[0045] S1: Establish the dynamic model of the transmission system and suspension system of the pure electric vehicle; perform system modeling for adaptive control; specifically, perform modeling through a second-order linear differential equation; the modeling through a second-order linear differential equation is as follows:

[0046]

[0047] Where M is the mass matrix, C is the damping matrix, K is the stiffness matrix, x is the displacement vector, and F is the external force vector, including the excitation force generated by the motor torque.

[0048] S2: Estimating model parameters, specifically using the recursive least squares method to estimate system parameters in real time; such as motor torque, transmission system stiffness and damping, as shown in the following formula;

[0049]

[0050] in, is the parameter estimate at time k, K k is the gain matrix, y k is the measured output, is the regression vector, λ is the forgetting factor, P k is the covariance matrix.

[0051] S3: According to the estimated parameters, an adaptive controller is designed; the adaptive controller adopts an adaptive sliding mode controller; the sliding mode surface formula is as follows:

[0052]

[0053] Where, e is the error between the system output and the expected output, and λ is a positive definite constant;

[0054] The control law is designed as follows:

[0055] u=u eq +u dis

[0056] Among them, u eq is equivalent control, u dis is a switching control; equivalent control is achieved by The solution is obtained; switching control is used to overcome system uncertainty and interference.

[0057] S4: Simulate the adaptive control algorithm under different working conditions, including different vehicle speeds and road excitations, evaluate its effect in suppressing vibration, and optimize it by adjusting controller parameters including positive constants or switching gains;

[0058] S5: Then make predictions through the model to establish a prediction model of the system; the prediction model includes a prediction model based on the state space model; as shown in the following formula;

[0059] x k+1 =Ax k +Bu k

[0060] y k =Cx k

[0061] Among them, x k is the state vector at time k, u k is the control input, y kis the system output, A, B, C are system matrices. Then set the prediction time domain and control time domain to determine the prediction time domain N p and control time domain N c ;

[0062] Then define the objective function, including minimizing the sum of squares of the error between the system output and the expected output and the change in the control input; as follows:

[0063]

[0064] in, is k+k predicted based on information at time k p Output at any time, is the expected output, λ u is the weight coefficient of the control input change, Δu k =u k -u k-1 .

[0065] S6: At each sampling moment, the optimal control input u at the current moment is obtained by solving steps S1-S5 k , using the quadratic programming QP method to solve; and performing rolling optimization: only executing the optimal control input at the current moment, at the next sampling moment, repeating steps S1-S5, and performing prediction and optimization based on the new measurement information.

[0066] In this embodiment, the present invention provides an electric vehicle simulation model test control platform, which specifically includes a parameter estimation module, which uses a recursive least squares method to estimate system parameters in real time, including motor torque, transmission system stiffness and damping parameters;

[0067] Controller design module: Design the controller according to the system parameters and control objectives; in adaptive control, design an adaptive sliding mode controller to control the system by defining the sliding surface and control law; in predictive control, obtain the optimal control input by solving the optimization problem to achieve predictive control of the system.

[0068] Working condition simulation module: simulates the control algorithm under different working conditions, including different vehicle speeds and road excitations; can simulate various working conditions that pure electric vehicles may encounter in actual operation, and provide a real scenario for evaluating the performance of the control algorithm. Through simulation under different working conditions, the vibration suppression effect of the control algorithm under various conditions is analyzed, so as to optimize the controller parameters.

[0069] Optimization and comparison module: optimizes the control algorithm and improves the control effect by adjusting the controller parameters, including the switching gain in adaptive control and the weight coefficient in predictive control. At the same time, it compares the advantages and disadvantages of different control algorithms (such as adaptive control and predictive control) in terms of vibration suppression, providing a basis for selecting the most suitable active control strategy for pure electric vehicles.

[0070] In this embodiment, the above steps are calculated using the following example data:

[0071] For example, the transmission system of a pure electric vehicle is a single degree of freedom system. In this case, the mass matrix M = [m], the damping matrix C = [c], the stiffness matrix K = [k], the displacement vector x = [x], and the external force vector F = [T / r] (T is the motor torque, r is the transmission radius). Assume m = 1000kg, c = 500Ns / m, k = 20000N / m, and the transmission radius r = 0.5m.

[0072] Perform parameter estimation (RLS algorithm);

[0073] Set the initial parameter estimates The covariance matrix P(0)=1000I (here I is the identity matrix, and since it is a single parameter estimation I=[1],), and the forgetting factor λ=0.95.

[0074] Assuming that the measured output y(k) is the displacement x(k), the regression vector

[0075] At time k = 1, assuming that the measured displacement x(1) = 0.01 m, the velocity Motor torque T(1) = 1000 Nm.

[0076] The regression vector is as follows;

[0077]

[0078] The gain matrix is ​​as follows:

[0079]

[0080]

[0081] Parameter estimates

[0082]

[0083] Covariance matrix

[0084] Controller Design (Adaptive Sliding Mode Controller)

[0085] Let the expected displacement x d=0.02m, error e=xx d , positive definite constant λ s =10.

[0086] Sliding surface

[0087] Equivalent control u eq By making Solve, switch control u sw =K s sgn(s), assuming K s =100.

[0088] Then carry out simulation and optimization;

[0089] Simulate under different vehicle speeds, road excitations and other working conditions, for example, set different motor torque change rules to simulate different vehicle speeds. s and K s Parameters such as the system displacement and the expected displacement are observed to evaluate the effect of jitter suppression.

[0090] Predictive control, model prediction

[0091] Assume that the state space model of the system is

[0092] set up C=[1 0],initial state

[0093] The prediction time domain and control time domain are set, and the prediction time domain P = 5 and the control time domain M = 3;

[0094] Objective function definition

[0095] Expected output y d (k) = 0.1, the weight coefficient of the control input change R = 0.1;

[0096] Objective Function

[0097]

[0098] To solve the optimization, at time k = 0, predict the output y(k+i|k) of the next P steps based on the state space model, and then use the quadratic programming (QP) method to solve the control input sequence [u(k), u(k+1), ..., u(k+M-1)] that minimizes the objective function J. For example, use Python's cvxpy library to solve it, as follows Figure 3-Figure 4 .

[0099] In this embodiment, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an electric vehicle simulation model test control method described above is implemented.

[0100] In this embodiment, the present invention provides a computer readable storage medium, wherein the computer readable storage medium includes an embedded processing system and a stored program, and controls the above-mentioned electric vehicle simulation model test control method when the embedded system control program runs.

[0101] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention has various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A test control method for an electric vehicle simulation model, characterized in that: Please follow the steps below: S1: Establish the dynamic model of the transmission system and suspension system of the pure electric vehicle; perform system modeling for adaptive control; specifically, perform modeling through second-order linear differential equations; S2: Estimating model parameters, specifically using the recursive least squares method to estimate system parameters in real time; S3: Design an adaptive controller based on the estimated parameters; S4: Simulate the adaptive control algorithm under different working conditions, including different vehicle speeds and road excitations, evaluate its effect in suppressing vibration, and optimize it by adjusting controller parameters including positive constants or switching gains; S5: Make predictions through the model and establish a systematic prediction model; S6: At each sampling moment, the optimal control input u at the current moment is obtained by solving steps S1-S5 k , using the quadratic programming QP method to solve; and performing rolling optimization: only executing the optimal control input at the current moment, at the next sampling moment, repeating steps S1-S5, and performing prediction and optimization based on the new measurement information.

2. The electric vehicle simulation model test control method according to claim 1, characterized in that: In step S1, the second-order linear differential equation is modeled as follows:

3. Among them, M is the mass matrix, C is the damping matrix, K is the stiffness matrix, x is the displacement vector, and F is the external force vector, including the excitation force generated by the motor torque.

4. The electric vehicle simulation model test control method according to claim 1, characterized in that: In step S2, the motor torque, transmission system stiffness and damping are as follows: in, is the parameter estimate at time k, K k is the gain matrix, y k is the measured output, is the regression vector, λ is the forgetting factor, P k is the covariance matrix.

5. The electric vehicle simulation model test control method according to claim 1, characterized in that: In step S3, the adaptive controller adopts an adaptive sliding mode controller; the sliding mode surface formula is as follows: Where, e is the error between the system output and the expected output, and λ is a positive definite constant; The control law is designed as follows: in=in eq +in dis Among them, u eq is equivalent control, v dis is a switching control; equivalent control is achieved by The solution is obtained.

6. The electric vehicle simulation model test control method according to claim 1, characterized in that: In step S5, the prediction model includes a prediction model based on a state space model; as shown in the following formula: x k+1 =Ax k +Bu k y k =Cx k Among them, x k is the state vector at time k, u k is the control input, y k is the system output, and A, B, C are the system matrices.

7. The electric vehicle simulation model test control method according to claim 6, characterized in that: Then set the prediction time domain and control time domain to determine the prediction time domain N p and control time domain N c ; Then define the objective function, including minimizing the sum of squares of the error between the system output and the expected output and the change in the control input; as follows: in, is k+k predicted based on information at time k p Output at any time, y ref , +k p |k is the expected output, λ u is the weight coefficient that controls the input change, Δu k =u k -u k-1 。 8. An electric vehicle simulation model test control platform, characterized in that: Specifically, it includes a parameter estimation module, which uses the recursive least squares method to estimate system parameters in real time, including motor torque, transmission system stiffness and damping parameters; Controller design module: Design the controller according to system parameters and control objectives; Working condition simulation module: simulates the control algorithm under different working conditions including different vehicle speeds and road excitations; Optimization and comparison module: Optimize the control algorithm and improve the control effect by adjusting the controller parameters, including the switching gain in adaptive control and the weight coefficient in predictive control.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, an electric vehicle simulation model test control method as described in any one of claims 1-7 is implemented.

10. A computer storable medium, characterized in that: The computer-readable storage medium includes an embedded processing system and a stored program, and when the embedded system control program runs, the electric vehicle simulation model test control method described in claims 1-7 is controlled.

Images