Closed-loop automobile crosswind stability dynamic evaluation system based on LQR control algorithm

Abstract

The invention relates to the technical field of intelligent driving test, and discloses a closed-loop automobile crosswind stability dynamic evaluation system based on an LQR control algorithm, and the system comprises a CFD model unit, an active control feedback unit, and an MBD model unit. The CFD model unit is used for acquiring motion parameters of a vehicle and outputting aerodynamic load parameters as input of the MBD model unit according to the motion parameters; the active control feedback unit is used for simulating active control behaviors of the vehicle, and front wheel steering angle parameters of the vehicle are obtained through an LQR module and serve as input of the MBD model unit; and the MBD model unit is used for taking the front wheel steering angle parameter as a control quantity and updating an aerodynamic load parameter, obtaining a dynamic response parameter according to the aerodynamic load parameter, and iterating a motion parameter of the vehicle according to the dynamic response parameter. According to the method, it is effectively guaranteed that almost out-of-control lateral movement does not occur in the model solving process, the actual driving working condition is truly restored, and the evaluation accuracy and reliability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent driving test, and in particular to a closed-loop automobile sidewind stability dynamic evaluation system based on an LQR control algorithm. Background Art

[0002] The crosswind stability of a car refers to the ability of the car to maintain stable and safe driving after being disturbed by the lateral airflow when driving at high speed, passing through a cross-sea bridge, entering and exiting a valley tunnel, or overtaking or meeting another car. This performance is directly related to the driving safety of the vehicle, especially in a strong crosswind environment. Vehicles with poor crosswind stability are prone to swaying, sideslipping, lane departure, and even rollover. Therefore, the simulation test of the crosswind stability of a car has become an important means to improve the safety performance of vehicles in the automotive industry.

[0003] However, in the current crosswind stability simulation test, despite the use of advanced computer simulation software and models, there are still problems such as low test simulation restoration and inaccurate test results. First of all, the air flow around the car becomes extremely complex under the action of crosswind, especially under high wind speed and drastic gust conditions, this complexity is further exacerbated. Traditional simulation methods, such as LBM (Lattice Boltzmann Method), can simulate this flow to a certain extent, but their applicable velocity discrete models and boundary condition methods are still very limited, and it is difficult to fully restore the complex airflow changes in real scenes.

[0004] Secondly, existing simulation tests often ignore the bidirectional coupling relationship between the vehicle's driving posture and the crosswind aerodynamic characteristics. In fact, changes in the vehicle's driving posture will in turn affect the crosswind aerodynamic characteristics, which is not fully reflected in the current simulation model. Therefore, the simulation results often cannot accurately reflect the dynamic response of the vehicle in a real crosswind environment, making it difficult for the test results to achieve the expected accuracy and reliability. Summary of the invention

[0005] The present invention aims to provide a closed-loop automobile sidewind stability dynamic evaluation system based on the LQR control algorithm to solve the problem that the degree of restoration of the automobile sidewind stability simulation test in the prior art to the actual situation is low, resulting in the test results being not accurate and reliable enough.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a closed-loop automobile crosswind stability dynamic evaluation system based on the LQR control algorithm. This scheme considers the influence of lateral active control behavior in actual working conditions, uses the LQR algorithm for lateral control, simulates the feedback behavior of actual driving for lateral deviation, truly reflects the movement of the car in a crosswind environment, improves the degree of simulation restoration, and improves the accuracy of the test results. The evaluation system specifically includes a CFD model unit, an active control feedback unit, and an MBD model unit; the CFD model unit is used to obtain the motion parameters of the vehicle, and outputs aerodynamic load parameters as input of the MBD model unit according to the motion parameters;

[0007] The active control feedback unit is used to simulate the active control behavior of the vehicle, including obtaining the front wheel steering angle parameters of the vehicle through the LQR module according to the motion parameters of the vehicle, and taking them as the input of the MBD model unit;

[0008] The MBD model unit is used to use the front wheel steering angle parameter as a control variable and update the aerodynamic load parameter, obtain the dynamic response parameter according to the aerodynamic load parameter, iterate the vehicle's motion parameters according to the dynamic response parameter, and complete a closed-loop vehicle crosswind stability evaluation.

[0009] The principles and advantages of this solution are:

[0010] The study of the crosswind stability of automobiles involves the intersection of multiple disciplines such as automobile aerodynamics and automobile multi-body dynamics. In order to systematically evaluate the crosswind stability of a vehicle, it is necessary to consider the mutual coupling of factors such as aerodynamic turbulence, the vehicle's driving path, and changes in the vehicle's body posture, and simulate the dynamic relationship between the six aerodynamic forces and the vehicle's body posture, that is, the dynamic evaluation of the vehicle's crosswind stability. Through this evaluation method, numerical simulation methods can be used to more realistically obtain the flow field information and movement conditions of the vehicle in a crosswind environment. For example, the invention application with the patent number "CN117829029A" discloses a method for testing the crosswind stability of a vehicle using models of automobile aerodynamics and automobile multi-body dynamics.

[0011] However, when the crosswind speed is high, the simulated vehicle often has a significant lateral deviation due to the large and long duration of the lateral velocity obtained, or even directly drives out of the calculation domain, which is obviously inconsistent with the actual situation, resulting in inaccurate test results. After analysis, we found that this is because the existing technology still ignores one point: the driver's correction behavior during driving and the role of auxiliary systems such as ESP. Even if the numerical simulation method is used to restore the movement of the car in a crosswind environment without correction behavior through the coupling of vehicle aerodynamics and vehicle multi-body dynamics, it ignores the active control of the direction of real driving when deviation occurs, which has certain limitations, resulting in a low degree of restoration of the existing evaluation method, affecting the reliability of the final evaluation results.

[0012] At the same time, even though the existing technology refers to the coupling effect of vehicle aerodynamics and vehicle multi-body dynamics, it does not take into account the factors of active control during actual driving and the influence relationship between them, which leads to large errors in the established dynamic model and large deviations in the constructed coupling relationship, resulting in inaccurate evaluation results.

[0013] Therefore, this scheme is to truly restore the movement of the car in the crosswind environment and reflect the impact of active control behavior on crosswind stability when lateral deviation occurs. Considering the feedback effect of active control behavior on crosswind interference, a closed-loop dynamic evaluation system for automobile crosswind stability based on the LQR control algorithm is proposed. Based on the bidirectional dynamic coupling of automobile aerodynamics and automobile multi-body dynamics (CFD-MBD), a whole vehicle CFD model and MBD model for coupling calculation are established respectively. The front wheel angle parameters are calculated by the LQR algorithm according to the heading error and lateral error of the current time step, and loaded into the dynamic solution together with the six aerodynamic components. After that, the body posture parameters of the dynamic response are transferred to the aerodynamics for the next time step iteration. The LQR algorithm and the bidirectional data exchange are calculated synchronously, and the state quantity of each solution is fed back, thereby realizing a closed-loop automobile crosswind stability evaluation.

[0014] The implementation of this program has the following beneficial effects:

[0015] Compared with the existing processing methods, it is more consistent with the actual situation. In the absence of crosswind stability test conditions, it can truly reproduce the driving state of the car in a complex wind environment. It can effectively ensure that the model does not move sideways almost out of control during the solution, truly restore the actual driving conditions, improve the degree of restoration, and improve the accuracy and reliability of the evaluation results.

[0016] Furthermore, the active control feedback unit includes a body posture parameter module, an LQR calculation module, a structural parameter module and a path module; the body posture parameter module is used to obtain the motion parameters of the vehicle at the current time step; the structural parameter module is used to obtain the body structure parameters of the vehicle; the path module is used to obtain the planned path information of the vehicle; the LQR calculation module is used to calculate the front wheel steering angle parameters of the current time step based on the motion parameters and the structural parameters.

[0017] Furthermore, the CFD model unit includes an update module, an output module and a determination module; the update module includes a boundary condition submodule and a dynamic mesh submodule; the boundary condition submodule is used to obtain motion parameters and update the motion parameters as boundary conditions, and the dynamic mesh submodule is used to update the dynamic mesh model according to the boundary conditions and obtain the aerodynamic load parameters through the dynamic mesh model; the output module is used to output the solved aerodynamic load parameters; the determination module is used to determine whether the simulation is completed.

[0018] Furthermore, the MBD model unit includes a control quantity module, a motion state module, a response module and a judgment module; the control quantity module is used to obtain the control quantity and update the aerodynamic load parameters; the motion state module is used to update the motion state by solving the DAE equation according to the aerodynamic load parameters; the response module is used to obtain the dynamic response parameters according to the motion state and output them to the active control feedback unit; the judgment module is used to determine whether the simulation is completed.

[0019] Furthermore, the active control feedback unit also includes an error module and a feedforward interference module; the error module is used to obtain error parameters between the actual path of the vehicle and the planned path; the feedforward interference module is used to generate interference according to the error parameters, which is used as a feedforward control input to participate in the calculation in the LQR calculation module.

[0020] Furthermore, the state equation of the front wheel steering angle is constructed through the LQR calculation module to control the error change of the front wheel steering angle. The state equation of the front wheel steering angle is expressed by the vehicle dynamics equation in the natural coordinate system as follows:

[0021]

[0022] In the formula, e d , Respectively represent the lateral error and heading error between the planned path and the actual path in the natural coordinate system, represents the first derivative of the lateral error, represents the first derivative of the lateral heading error, represents the second-order derivative of the lateral error, represents the second-order derivative of the lateral heading error, m represents the total mass of the vehicle, u represents the longitudinal speed at the center of mass, δ represents the wheel angle, I represents the moment of inertia, a and b represent the front and rear wheelbases, and K1 and K2 represent the cornering stiffness of the front and rear tires.

[0023] Furthermore, a cost function for reducing the error is constructed based on the state equation of the front wheel angle:

[0024]

[0025] In the formula, the error u=δ, Q and R are weight matrices of the LQR algorithm.

[0026] Furthermore, the interference amount is expressed as:

[0027]

[0028] In the formula, k r represents the curvature of the path at this point, k3 represents the third element in the K vector calculated by the LQR algorithm, and L represents the wheelbase.

[0029] Furthermore, the vehicle dynamics equation in the natural coordinate system is obtained by constructing the vehicle dynamics equation in the vehicle body coordinate system, wherein the vehicle dynamics equation in the vehicle body coordinate system is:

[0030]

[0031] In the formula, represents the lateral acceleration, is the yaw angular acceleration, represents the longitudinal speed in the vehicle body coordinate system, Represents the vehicle's yaw rate.

[0032] Furthermore, the relationship between the motion parameters in the vehicle body coordinate system and the motion parameters in the natural coordinate system is:

[0033]

[0034] In the formula, Represents the first-order derivative of the actual front wheel steering angle when considering the side slip. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 The present invention is a schematic structural diagram of a closed-loop automobile sidewind stability dynamic evaluation system based on an LQR control algorithm.

[0036] Figure 2 The present invention is a schematic diagram of the calculation flow of a closed-loop automobile sidewind stability dynamic evaluation system based on the LQR control algorithm.

[0037] Figure 3 The present invention is a feedforward control flow chart of a closed-loop automobile sidewind stability dynamic evaluation system based on an LQR control algorithm. DETAILED DESCRIPTION

[0038] The following is further described in detail through specific implementation methods:

[0039] Example

[0040] As attached Figure 1As shown, this scheme addresses the problems that the dynamic evaluation method for automobile crosswind stability will have large lateral displacement during simulation and cannot fully consider the driver's control of direction and the role of the body electronic control system. It considers the feedback effect of active control behavior on crosswind interference, uses LQR algorithm for lateral control, simulates the feedback behavior of actual driving for lateral deviation, and truly reflects the movement of the car in a crosswind environment. Under this system, the large lateral deviation that often occurs in numerical simulation is suppressed, and the driver's active correction behavior and the role of the electronic assistance system are also restored, thereby improving the degree of restoration of the numerical simulation method to the actual situation. In this embodiment, the closed-loop automobile crosswind stability dynamic evaluation system mainly includes a CFD model unit, an active control feedback unit, and an MBD model unit.

[0041] The CFD model unit is a model of the aerodynamic part of the automobile, which is used to obtain the motion parameters of the vehicle and output the aerodynamic load parameters as the input of the MBD model unit according to the motion parameters. In this embodiment, the aerodynamic load parameters are the aerodynamic six-component force parameters.

[0042] The active control feedback unit is used to simulate the active control behavior of the vehicle, including obtaining the front wheel steering angle parameters of the vehicle through the LQR module according to the vehicle's motion parameters, and using them as the input of the MBD model unit.

[0043] The MBD model unit is a partial model of the automobile aerodynamics. It is used to take the front wheel steering angle parameters as the control quantity and update the aerodynamic load parameters, that is, to update the aerodynamic six-component force parameters, obtain the dynamic response parameters according to the aerodynamic load parameters, and iterate the vehicle's motion parameters according to the dynamic response parameters to complete a closed-loop evaluation of the automobile's crosswind stability.

[0044] Combined with Figure 2 As shown, attached Figure 2 This is a schematic diagram of the operation process of the closed-loop evaluation system based on the LQR control algorithm, which is built on the dynamic coupling platform of automobile aerodynamics and automobile multi-body dynamics. The aerodynamic part is calculated by the CFD solver Star CCM+, and the multi-body dynamics part is simulated by the MBD solver Carsim. The real-time calculation data of the fluid and multi-body system is obtained and transmitted by scanning the shared database, which is realized by Matlab / Simulink software.

[0045] In this embodiment, before coupling, Star CCM+ and Carsim need to solve separately for a period of time, and scan and transfer data after the flow field and vehicle motion reach a stable state. During this process, the LQR algorithm calculates as the data is loaded, but does not produce actual control effects.

[0046] Specifically, the CFD model unit includes an update module, an output module, and a determination module. The update module includes a boundary condition submodule and a dynamic mesh submodule. When coupling starts, the boundary condition submodule is used to scan and read the motion parameters in the specified shared database through the udf program, and combined with the dynamic mesh model technology, the motion parameters of the current time step are solved and updated as boundary conditions. The dynamic mesh submodule is used to update the dynamic mesh model according to the boundary conditions, and obtain the aerodynamic load parameters by solving the NS (Navier-Stokes) equations. In the rectangular coordinate system, the vector form of the NS equation can be expressed as Where ρ is the fluid density, p is the pressure, u(u,v,w) is the velocity vector, and F(X,Y,Z) is the body force acting on unit mass of fluid. is the Hamiltonian operator, Δ is the Laplace operator, which is a prior art and will not be elaborated here. The output module is used to output the aerodynamic load parameters obtained by the solution, i.e., the aerodynamic six-component force parameters. The determination module is used to determine whether the simulation is finished. If the simulation is finished, the simulation calculation of the CFD model unit is terminated. If not, it returns to the update module to continue iterating until the simulation result meets the requirements.

[0047] The active control feedback unit includes a body posture parameter module, an LQR calculation module, a structural parameter module and a path module. The body posture parameter module is used to obtain the motion parameters of the vehicle at the current time step as real-time data in the shared database. The structural parameter module is used to obtain the body structure parameters of the vehicle. In this embodiment, the body structure parameters include the front and rear wheel side slip angles, the vehicle center of mass position, the vehicle total mass, the front and rear wheelbase, etc. The path module is used to obtain the planned path information of the vehicle and the actual driving path information of the current time step. The LQR calculation module is used to calculate the front wheel steering angle parameters of the current time step based on the motion parameters and the structural parameters.

[0048] The active control feedback unit also includes an error module and a feedforward interference module. The error module is used to obtain the error parameters between the actual path of the vehicle and the planned path. The feedforward interference module is used to generate interference according to the error parameters, which is used as the feedforward control input to participate in the calculation in the LQR calculation module.

[0049] In this embodiment, the LQR control algorithm in the LQR calculation module is based on the linear two-degree-of-freedom model of the vehicle. However, since this solution considers the active control behavior during actual driving, the vehicle operation is assumed and simplified, and only the lateral and yaw degrees of freedom are retained, and the tire cornering stiffness is within the linear range. According to Newton's second law, the linear two-degree-of-freedom vehicle model satisfies:

[0050]

[0051] In the formula, F Y 、FY1 、F Y2 represents the lateral force on the vehicle and the front and rear wheels, δ represents the wheel angle, m represents the total mass, a y represents the lateral acceleration, M Z represents the yaw moment, a and b represent the front and rear wheelbases, and I represents the moment of inertia. Represents angular acceleration.

[0052] In this embodiment, in order to better reflect the research object of the lateral and yaw degrees of freedom, after transforming formula (1), the following formula is obtained:

[0053]

[0054] Where K1 and K2 represent the cornering stiffness of the front and rear tires, and α1 and α2 represent the front and rear wheel slip angles, respectively. Under the assumption of a small slip angle for the bicycle model, the front and rear wheel slip angles are generally no greater than 4° to 5°.

[0055] Due to the limitation of the degrees of freedom of the bicycle model, in this embodiment, the control object of the LQR algorithm can only be the wheel angle, and the state quantity based on which the control is based is the parameter reflecting the longitudinal and lateral motion conditions, and the state equation of the control system needs to be obtained by transforming the vehicle dynamics equation of the linear two-degree-of-freedom model in the geodetic coordinate system, that is, equation (2). At the same time, in this embodiment, the control idea adopted is to minimize the error between the actual path and the planned path. Therefore, for the convenience of calculation, in this embodiment, the vehicle dynamics equation in the geodetic coordinate system is obtained by transforming the vehicle dynamics equation in the natural coordinate system, because the natural coordinate system avoids the deviation between the longitudinal velocity direction caused by the sideslip angle of the center of mass and the horizontal axis of the vehicle body coordinate system, which facilitates the decoupling of the lateral and longitudinal motions and simplifies the analysis process. In this embodiment, for the sideslip angles of the front and rear wheels and the lateral acceleration a y and yaw angular acceleration The various parameters in the vehicle body coordinate system can be expressed as follows:

[0056]

[0057] In the formula, represents the longitudinal speed in the vehicle body coordinate system, u and υ represent the longitudinal and lateral speeds at the center of mass, respectively. Represents the yaw rate of the vehicle. Substituting the parameter relationship in equation (3) into equation (2), the vehicle dynamics equation in the body coordinate system can be obtained:

[0058]

[0059] In the formula, represents the lateral acceleration, is the yaw angular acceleration, represents the longitudinal speed in the vehicle body coordinate system, Represents the vehicle's yaw rate.

[0060] The motion parameters in the vehicle body coordinate system and the motion parameters in the natural coordinate system have the following relationship:

[0061]

[0062] In the formula, e d , Respectively represent the lateral error and heading error between the planned path and the actual path in the natural coordinate system, represents the first derivative of the lateral error, represents the first derivative of the lateral heading error, represents the second-order derivative of the lateral error, Represents the second-order derivative of the lateral heading error, θ r represents the heading angle, Represents the first derivative of the heading angle.

[0063] Substituting the parameter relationship in equation (5) into equation (4), we can obtain the vehicle dynamics equation in the natural coordinate system:

[0064]

[0065] That is, the state equation of the controller is obtained (6), which is also the constraint condition of the LQR algorithm. The state quantity is the lateral and heading errors and their first-order derivatives in the natural coordinate system, and the control quantity is the front wheel angle. The state equation of the front wheel angle is constructed through the LQR calculation module to control the error change of the front wheel angle, so as to minimize the change of the control quantity to ensure comfort. rr Minimize the control purpose.

[0066] Therefore, according to the state equation of the front wheel angle, the error parameter between the actual path and the planned path of the vehicle is obtained through the error module, and a cost function for reducing the error is constructed:

[0067]

[0068] In the formula, the error u=δ, Q and R are the weight matrices of the LQR algorithm. Thus, we can obtain the following Figure 3 The control block diagram under the LQR algorithm shown in the figure generates interference quantity according to the changes of error parameters and structural parameters through the feedforward interference module outside the system control block diagram, forming feedforward control to participate in the front wheel angle calculation of the LQR calculation module to prevent the occurrence of "algebraic loop" problem, thereby realizing the simulation of active control behavior.

[0069] In this embodiment, the interference amount δ fIt is expressed as:

[0070]

[0071] In the formula, k r represents the curvature of the path at this point, k3 represents the third element in the K vector calculated by the LQR algorithm, and L represents the wheelbase. The front wheel steering angle parameter is obtained as the input of the MBD model unit.

[0072] The MBD model unit includes a control quantity module, a motion state module, a response module and a judgment module. The control quantity module is used to obtain the control quantity and update the aerodynamic load parameters. In this embodiment, it is scanned and read by Matlab / Simulink, and then the control quantity front wheel angle parameter calculated according to the current state is added and sent to Carsim through the interface program.

[0073] The motion state module is used to solve the DAE equation and update the vehicle motion state according to the aerodynamic load parameters, and write it into the shared database. The response module is used to obtain the dynamic response parameters according to the motion state, and output them to the body posture parameter module in the active control feedback unit to update the motion parameters. Finally, by outputting data such as the six-component aerodynamic force and vehicle motion parameters based on the time series, the closed-loop dynamic evaluation of the vehicle's crosswind aerodynamic characteristics and stability is realized. The judgment module is used to determine whether the simulation is over. If the simulation is over, the simulation calculation of the MBD model unit is terminated. If it is not over, it returns to the update module to continue iterating until the simulation results meet the requirements.

[0074] In this embodiment, a closed-loop system for evaluating the crosswind stability of a vehicle is proposed based on the CFD-MBD dynamic coupling method. At present, in the study of the aerodynamic characteristics or crosswind stability of a vehicle, the dynamic coupling method is used to solve the separation of aerodynamics and multi-body dynamics caused by unidirectional coupling, and the evaluation of the crosswind stability of a vehicle without a feedback mechanism is realized with high authenticity. However, the simple two-way coupling method often results in a large lateral offset that is inconsistent with reality in the numerical simulation because it does not take into account the active correction behavior during actual driving, which greatly restricts the evaluation effect of the two-way coupling mechanism.

[0075] This solution proposes a closed-loop evaluation method that can simulate the active correction behavior of the driver or electronic assistance system in a crosswind environment. It can effectively ensure that the model does not move sideways in a nearly uncontrolled manner during solution, and truly restore the actual driving conditions. Compared with existing processing methods, it is more consistent with the actual situation. In the absence of crosswind stability test conditions, it truly reproduces the driving state of the car in a complex wind environment, and quickly and effectively guides the forward development of the vehicle's aerodynamics and chassis.

[0076] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A closed-loop vehicle sidewind stability dynamic evaluation system based on LQR control algorithm, characterized by: It includes a CFD model unit, an active control feedback unit and an MBD model unit; the CFD model unit is used to obtain the motion parameters of the vehicle, and output aerodynamic load parameters according to the motion parameters as input of the MBD model unit; The active control feedback unit is used to simulate the active control behavior of the vehicle, including obtaining the front wheel steering angle parameters of the vehicle through the LQR module according to the motion parameters of the vehicle, and taking them as the input of the MBD model unit; The MBD model unit is used to use the front wheel steering angle parameter as a control variable and update the aerodynamic load parameter, obtain the dynamic response parameter according to the aerodynamic load parameter, iterate the vehicle's motion parameters according to the dynamic response parameter, and complete a closed-loop vehicle crosswind stability evaluation.

2. A closed-loop automobile sidewind stability dynamic evaluation system based on the LQR control algorithm according to claim 1, characterized in that: The active control feedback unit includes a body posture parameter module, an LQR calculation module, a structural parameter module and a path module; the body posture parameter module is used to obtain the motion parameters of the vehicle at the current time step; the structural parameter module is used to obtain the body structure parameters of the vehicle; the path module is used to obtain the planned path information of the vehicle; the LQR calculation module is used to calculate the front wheel steering angle parameters of the current time step based on the motion parameters and the structural parameters.

3. A closed-loop automobile sidewind stability dynamic evaluation system based on the LQR control algorithm according to claim 1, characterized in that: The CFD model unit includes an update module, an output module and a determination module; the update module includes a boundary condition submodule and a dynamic mesh submodule; the boundary condition submodule is used to obtain motion parameters and update the motion parameters as boundary conditions, and the dynamic mesh submodule is used to update the dynamic mesh model according to the boundary conditions and obtain the aerodynamic load parameters through the dynamic mesh model; the output module is used to output the solved aerodynamic load parameters; the determination module is used to determine whether the simulation is completed.

4. A closed-loop automobile sidewind stability dynamic evaluation system based on LQR control algorithm according to claim 1, characterized in that: The MBD model unit includes a control quantity module, a motion state module, a response module and a judgment module; the control quantity module is used to obtain the control quantity and update the aerodynamic load parameters; the motion state module is used to update the motion state by solving the DAE equation according to the aerodynamic load parameters; the response module is used to obtain the dynamic response parameters according to the motion state and output them to the active control feedback unit; the judgment module is used to determine whether the simulation is completed.

5. A closed-loop vehicle sidewind stability dynamic evaluation system based on LQR control algorithm according to claim 2, characterized in that: The active control feedback unit also includes an error module and a feedforward interference module; the error module is used to obtain the error parameters between the actual path of the vehicle and the planned path; the feedforward interference module is used to generate interference according to the error parameters and structural parameters, which is used as a feedforward control input to participate in the calculation in the LQR calculation module.

6. A closed-loop automobile sidewind stability dynamic evaluation system based on LQR control algorithm according to claim 5, characterized in that: The state equation of the front wheel angle is constructed through the LQR calculation module to control the error change of the front wheel angle. The state equation of the front wheel angle is expressed by the vehicle dynamics equation in the natural coordinate system as follows: In the formula, e d , Respectively represent the lateral error and heading error between the planned path and the actual path in the natural coordinate system, represents the first derivative of the lateral error, represents the first derivative of the lateral heading error, represents the second-order derivative of the lateral error, represents the second-order derivative of the lateral heading error, m represents the total mass of the vehicle, u represents the longitudinal speed at the center of mass, δ represents the wheel angle, I represents the moment of inertia, a and b represent the front and rear wheelbases, and K1 and K2 represent the cornering stiffness of the front and rear tires.

7. A closed-loop automobile sidewind stability dynamic evaluation system based on LQR control algorithm according to claim 6, characterized in that: The cost function used to reduce the error is constructed based on the state equation of the front wheel angle: In the formula, the error u=δ, Q and R are weight matrices of the LQR algorithm.

8. A closed-loop automobile sidewind stability dynamic evaluation system based on LQR control algorithm according to claim 7, characterized in that: The interference amount is expressed as: In the formula, k r represents the curvature of the path at this point, k3 represents the third element in the K vector calculated by the LQR algorithm, and L represents the wheelbase.

9. The closed-loop vehicle sidewind stability dynamic evaluation system based on the LQR control algorithm according to claim 6, characterized in that: The vehicle dynamics equation in the natural coordinate system is obtained by constructing the vehicle dynamics equation in the body coordinate system, where the vehicle dynamics equation in the body coordinate system is: In the formula, represents the lateral acceleration, is the yaw angular acceleration, represents the longitudinal speed in the vehicle body coordinate system, Represents the vehicle's yaw rate.

10. A closed-loop automobile sidewind stability dynamic evaluation system based on LQR control algorithm according to claim 9, characterized in that: The relationship between the motion parameters in the vehicle body coordinate system and the motion parameters in the natural coordinate system is: In the formula, It represents the first derivative of the actual steering angle of the front wheel when considering the side slip.

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