Vehicle cornering stiffness calculation method, device, equipment and readable storage medium

Abstract

The present application relates to a method, apparatus, device and readable storage medium for calculating the cornering stiffness of a vehicle, and relates to the technical field of automotive intelligent driving. It includes constructing a force equation based on deviation and a rotational moment equation based on deviation according to a two-degree-of-freedom model and a vehicle dynamics model based on deviation; constructing a vehicle spatial state equation based on deviation including the functional relationship between the cornering stiffness parameter and the vehicle operating condition parameter according to the force equation based on deviation; constructing a cornering stiffness spatial state equation based on the extended Kalman filter according to the force equation based on deviation, the rotational moment equation based on deviation and the vehicle spatial state equation based on deviation; and solving the cornering stiffness spatial state equation based on the extended Kalman filter algorithm and the obtained vehicle real-time operating condition parameters to obtain the real-time cornering stiffness of the vehicle. Through the present application, only by obtaining the vehicle real-time operating condition parameters and using the extended Kalman filter algorithm can the real-time cornering stiffness of the vehicle be accurately identified.

Description

Technical Field

[0001] This application relates to the technical field of automotive intelligent driving, and particularly to a method, device, equipment, and readable storage medium for calculating the cornering stiffness of a vehicle. Background Art

[0002] With the continuous advancement of the intelligentization process, intelligent driving functions, as the most representative intelligent functions of automobiles, have become the core field of the intelligent development of vehicle manufacturers. The lateral control of a vehicle is an important part of the automotive intelligent driving environment. In particular, the cornering stiffness of a vehicle has an important impact on the lateral control and control stability of the vehicle. Therefore, whether the data of the cornering stiffness of the vehicle body during driving can be obtained quickly and accurately has a significant impact on the effect of the lateral motion control of intelligent vehicles.

[0003] In related technologies, there is no sensor that can be used to directly collect the cornering stiffness data of the vehicle body, and the cornering stiffness of the vehicle body can only be identified through experimental methods or offline. However, the identified cornering stiffness has a large error, so that the accuracy requirement cannot be met. Summary of the Invention

[0004] This application provides a method, device, equipment, and readable storage medium for calculating the cornering stiffness of a vehicle to solve the problem in related technologies that the cornering stiffness of a vehicle cannot be accurately calculated.

[0005] In a first aspect, a method for calculating the cornering stiffness of a vehicle is provided, including the following steps:

[0006] Construct a force equation based on deviation and a rotational moment equation based on deviation according to a two-degree-of-freedom model and a vehicle dynamics model based on deviation;

[0007] Construct a vehicle spatial state equation based on deviation according to the force equation based on deviation, and the vehicle spatial state equation based on deviation includes a functional relationship between the cornering stiffness parameter and the vehicle operating condition parameter;

[0008] Construct a cornering stiffness spatial state equation based on the extended Kalman filter according to the force equation based on deviation, the rotational moment equation based on deviation, and the vehicle spatial state equation based on deviation;

[0009] Solve the cornering stiffness spatial state equation based on the extended Kalman filter algorithm and the obtained vehicle real-time operating condition parameters to obtain the real-time cornering stiffness of the vehicle.

[0010] In some embodiments, the vehicle spatial state equation based on deviation is:

[0011]

[0012] In the formula, Caf represents the cornering stiffness of the front wheels of the vehicle, C ar represents the cornering stiffness of the rear wheels of the vehicle, represents the lateral speed deviation of the vehicle, and e2 represents the deviation between the vehicle direction and the road direction, represents the yaw rate deviation of the vehicle in the lateral direction, represents the desired yaw rate deviation of the vehicle in the lateral direction, m represents the vehicle mass, V x represents the longitudinal vehicle speed, l f represents the distance from the front wheels of the vehicle to the vehicle's center of mass, l r represents the distance from the rear wheels of the vehicle to the vehicle's center of mass.

[0013] In some embodiments, constructing a cornering stiffness space state equation based on the extended Kalman filter according to the deviation-based force equation, the deviation-based turning moment equation, and the deviation-based vehicle space state equation includes:

[0014] Creating a system state equation of the extended Kalman filter according to the deviation-based vehicle space state equation;

[0015] Creating a system measurement equation of the extended Kalman filter according to the deviation-based force equation and the deviation-based turning moment equation;

[0016] Creating a system observation equation of the extended Kalman filter based on the system measurement equation and system measurement noise, and creating a transformation matrix based on the system measurement equation;

[0017] Creating a state transition matrix based on the system state equation, and creating an estimation error covariance matrix based on the system state equation, the process excitation noise covariance matrix, the state transition matrix, and its corresponding transposed matrix;

[0018] Creating a state gain matrix according to the estimation error covariance matrix, the observation noise covariance matrix, the transformation matrix, and its corresponding transposed matrix;

[0019] Constructing a cornering stiffness space state equation based on the extended Kalman filter according to the system state equation, the state gain matrix, the system observation equation, and the system measurement equation.

[0020] In some embodiments, the cornering stiffness space state equation based on the extended Kalman filter is:

[0021]

[0022] In the formula, represents the system state equation, K represents the state gain matrix, represents the system observation equation, Represents the system measurement equation, C af Represents the cornering stiffness of the vehicle's front wheels, C ar Represents the cornering stiffness of the vehicle's rear wheels, Represents the lateral velocity deviation of the vehicle.

[0023] In a second aspect, a vehicle cornering stiffness calculation device is provided, including:

[0024] A first construction unit configured to construct a deviation-based force equation and a deviation-based rotational moment equation according to a two-degree-of-freedom model and a deviation-based vehicle dynamics model;

[0025] A second construction unit configured to construct a deviation-based vehicle space state equation according to the deviation-based force equation, where the deviation-based vehicle space state equation includes a functional relationship between a cornering stiffness parameter and a vehicle operating condition parameter;

[0026] A third construction unit configured to construct a cornering stiffness space state equation based on the extended Kalman filter according to the deviation-based force equation, the deviation-based rotational moment equation, and the deviation-based vehicle space state equation;

[0027] A calculation unit configured to solve the cornering stiffness space state equation based on the extended Kalman filter algorithm and the acquired real-time vehicle operating condition parameters to obtain the real-time cornering stiffness of the vehicle.

[0028] In some embodiments, the deviation-based vehicle space state equation is:

[0029]

[0030] In the formula, C af Represents the cornering stiffness of the vehicle's front wheels, C ar Represents the cornering stiffness of the vehicle's rear wheels, Represents the lateral velocity deviation of the vehicle, e2 represents the deviation between the vehicle direction and the road direction, Represents the lateral yaw rate deviation of the vehicle, Represents the desired lateral yaw rate deviation of the vehicle, m represents the vehicle mass, V x Represents the longitudinal vehicle speed, l f Represents the distance from the vehicle's front wheels to the vehicle's center of mass, l r Represents the distance from the vehicle's rear wheels to the vehicle's center of mass.

[0031] In some embodiments, the third construction unit is specifically configured to:

[0032] Create a system state equation of the extended Kalman filter according to the deviation-based vehicle space state equation;

[0033] Create a system measurement equation for the extended Kalman filter according to the deviation-based force equation and the deviation-based rotational torque equation;

[0034] Create a system observation equation for the extended Kalman filter based on the system measurement equation and system measurement noise, and create a transformation matrix based on the system measurement equation;

[0035] Create a state transition matrix based on the system state equation, and create an estimation error covariance matrix based on the system state equation, the process excitation noise covariance matrix, the state transition matrix and its corresponding transposed matrix;

[0036] Create a state gain matrix according to the estimation error covariance matrix, the observation noise covariance matrix, the transformation matrix and its corresponding transposed matrix;

[0037] Construct a sideslip stiffness space state equation based on the extended Kalman filter according to the system state equation, the state gain matrix, the system observation equation and the system measurement equation.

[0038] In some embodiments, the sideslip stiffness space state equation based on the extended Kalman filter is:

[0039]

[0040] In the formula, represents the system state equation, K represents the state gain matrix, represents the system observation equation, represents the system measurement equation, C af represents the front wheel sideslip stiffness of the vehicle, C ar represents the rear wheel sideslip stiffness of the vehicle, represents the speed deviation in the lateral direction of the vehicle.

[0041] In a third aspect, there is provided a vehicle sideslip stiffness calculation device, including: a memory and a processor, where at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the foregoing vehicle sideslip stiffness calculation method.

[0042] In a fourth aspect, there is provided a computer-readable storage medium, where the computer storage medium stores a computer program, and when the computer program is executed by a processor, the foregoing vehicle sideslip stiffness calculation method is implemented.

[0043] The beneficial effects brought by the technical solution provided by this application include: being able to accurately calculate the real-time sideslip stiffness of a vehicle.

[0044] The present application provides a method, apparatus, device and readable storage medium for calculating the cornering stiffness of a vehicle, including constructing a force equation based on deviation and a rotational moment equation based on deviation according to a two-degree-of-freedom model and a vehicle dynamics model based on deviation; constructing a vehicle spatial state equation based on deviation according to the force equation based on deviation, where the vehicle spatial state equation based on deviation includes a functional relationship between the cornering stiffness parameter and the vehicle operating condition parameter; constructing a cornering stiffness spatial state equation based on the extended Kalman filter according to the force equation based on deviation, the rotational moment equation based on deviation and the vehicle spatial state equation based on deviation; and solving the cornering stiffness spatial state equation based on the extended Kalman filter algorithm and the obtained real-time vehicle operating condition parameters to obtain the real-time cornering stiffness of the vehicle. Through the present application, only by obtaining the real-time vehicle operating condition parameters and using the extended Kalman filter algorithm can the real-time cornering stiffness of the vehicle be accurately identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0046] Figure 1 It is a schematic flowchart of a method for calculating the cornering stiffness of a vehicle provided by an embodiment of the present application;

[0047] Figure 2 It is a schematic diagram of a two-degree-of-freedom vehicle model provided by an embodiment of the present application;

[0048] Figure 3 It is a schematic diagram of the angular relationship between the X-axis of the tire and the vehicle body coordinate system provided by an embodiment of the present application;

[0049] Figure 4 It is a schematic diagram of a single-vehicle model provided by an embodiment of the present application;

[0050] Figure 5 It is a schematic diagram of the structure of a device for calculating the cornering stiffness of a vehicle provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present application belong to the scope of protection of the present application.

[0052] The embodiments of the present application provide a method, device, equipment and readable storage medium for calculating the cornering stiffness of a vehicle, which can solve the problem in the related art that the cornering stiffness of the vehicle cannot be accurately calculated.

[0053] Figure 1 A method for calculating the cornering stiffness of a vehicle provided by an embodiment of the present application includes the following steps:

[0054] Step S10: Construct a deviation-based force equation and a deviation-based rotational moment equation according to the two-degree-of-freedom model and the vehicle dynamics model based on deviation;

[0055] Exemplarily, in this embodiment, since the assumption that "the vehicle speed of each wheel is along the wheel direction" no longer holds in the case of high speed, this embodiment considers introducing a dynamic model, that is, establishing a dynamics model of the vehicle's lateral movement based on the vehicle two-degree-of-freedom model. Specifically, the two degrees of freedom in this embodiment refer to the lateral position of the vehicle and the direction angle of the vehicle in the earth coordinate system (see Figure 2 shown); therefore, considering the movement in the y direction, the force equation is:

[0056] ma y =F yf +F yr (1)

[0057] In the formula, m represents the vehicle mass, a y represents the acceleration of the vehicle in the y direction, F yf and F yr respectively represent the lateral forces received by the front axle and the rear axle of the vehicle body;

[0058] Considering the acceleration in the y direction, then:

[0059]

[0060] In the formula, s represents the distance from the vehicle center of mass to the center line of the lane, represents the acceleration of the vehicle to the center line of the lane, V x represents the longitudinal vehicle speed, ψ represents the actual yaw angle of the vehicle, represents the actual yaw angular velocity of the vehicle;

[0061] Substituting Equation (2) into Equation (1), we can get:

[0062]

[0063] Considering the rotation direction around the z axis, the equation of the rotational moment is:

[0064]

[0065] In the formula, I z represents the moment of inertia of the vehicle, represents the actual yaw angular acceleration of the vehicle, l f , l r respectively represent the distances from the front and rear wheels to the vehicle's center of mass.

[0066] To further describe the lateral force on the tire, the concept of tire slip angle is introduced in this embodiment. Among them, referring to Figure 3 as shown, δ represents the angle between the wheel direction and the X-axis of the vehicle body coordinate system, δ f represents the angle between the front wheel direction and the X-axis of the vehicle body coordinate system, δ r represents the angle between the rear wheel direction and the X-axis of the vehicle body coordinate system. However, in this embodiment, the front-wheel steering system is taken as an example to explain the identification of the cornering stiffness. Therefore, δ r is set to 0, that is, δ in the following text is equivalent to δ f . Similarly, if the rear-wheel steering system is taken as an example to explain the identification of the cornering stiffness, then δ f can be set to 0, and the corresponding δ in the following text will be equivalent to δ r ; θ V represents the angle between the wheel speed direction and the X-axis of the vehicle body coordinate system, and θ Vf represents the angle between the front wheel speed direction and the X-axis of the vehicle body coordinate system, θ Vr represents the angle between the rear wheel speed direction and the X-axis of the vehicle body coordinate system.

[0067] Specifically, since the lateral force in the wheel direction is generated by the lateral offset of the wheel, therefore, in the small-angle working condition, the lateral force of a single tire is proportional to the tire slip angle, that is:

[0068] F yf = k f α f (5)

[0069] F yr = k r α r (6)

[0070] In the formula, k f and k r respectively represent the cornering stiffness of the front and rear wheels, α f and α r respectively represent the slip angles of the front and rear wheels; since k f and k r are negative values, for the convenience of calculation, it can be agreed that:

[0071] C af = -k f (7)

[0072] C ar = -k r (8)

[0073] Wherein, C af and C ar represent the positive cornering stiffness of the front and rear wheels, and:

[0074] α f = δ - θ Vf (9)

[0075] α r = -θ Vr (10)

[0076] Next, solve for θ Vf and θ Vr That is:

[0077]

[0078]

[0079] Wherein, V y represents the lateral vehicle speed;

[0080] See Figure 4 shown. Using the assumption of small-angle linearity, based on Equations (11) and (12), we can obtain:

[0081]

[0082]

[0083] Wherein, represents the speed of the vehicle to the center line of the lane;

[0084] Therefore, substituting Equations (5), (6), (9), and (10) into Equations (3) and (4), the lateral spatial state equation of the vehicle can be obtained as:

[0085]

[0086] In this embodiment, in order to more accurately identify the cornering stiffness, a dynamics model based on deviation is adopted. That is, in order to eliminate the deviation between the vehicle and the target line when controlling the vehicle movement, the dynamics equation of the deviation is considered, and then the error state quantities are introduced: the deviation e1 of the vehicle center of mass to the road center line and the deviation e2 of the vehicle direction and the road direction;

[0087] Therefore, considering the longitudinal vehicle speed V x and the lane radius R, the yaw angular velocity of the vehicle is:

[0088]

[0089] The desired vehicle lateral acceleration is:

[0090]

[0091] Furthermore, and e2 can be respectively defined as:

[0092]

[0093] e2 = ψ - ψ des (19)

[0094] In the formula, represents the lateral acceleration deviation, ψ des represents the desired yaw angle, represents the desired yaw angular velocity;

[0095] Integrating gives:

[0096]

[0097] In the formula, represents the lateral velocity deviation;

[0098] Substituting the above formulas into formulas (3) and (4) and simplifying, the force equation based on deviation can be obtained:

[0099]

[0100] and the rotational moment equation based on deviation:

[0101]

[0102] In the formula, represents the lateral yaw angular velocity deviation, represents the lateral yaw angular acceleration deviation, represents the desired yaw angular acceleration.

[0103] Step S20: Construct a vehicle spatial state equation based on the force equation based on deviation, and the vehicle spatial state equation based on deviation includes the functional relationship between the cornering stiffness parameter and the vehicle operating condition parameter;

[0104] Furthermore, the vehicle spatial state equation based on deviation is:

[0105]

[0106] In the formula, C af represents the cornering stiffness of the vehicle front wheels, C ar represents the cornering stiffness of the vehicle rear wheels, represents the lateral speed deviation of the vehicle, and e2 represents the deviation between the vehicle direction and the road direction. represents the yaw rate deviation of the vehicle in the lateral direction. represents the desired yaw rate deviation of the vehicle in the lateral direction, m represents the vehicle mass, and V x represents the longitudinal vehicle speed, and l f represents the distance from the front wheel of the vehicle to the vehicle's center of mass, and l r represents the distance from the rear wheel of the vehicle to the vehicle's center of mass.

[0107] Exemplarily, in this embodiment, according to Equation (21), it can be obtained that:

[0108]

[0109] Based on Equation (23), it can be obtained that:

[0110]

[0111] By further transforming Equation (23), the vehicle space state equation based on the deviation can be obtained:

[0112]

[0113] In this embodiment, by replacing the cornering stiffness with the reciprocal of the cornering stiffness, the Kalman filter error can be minimized.

[0114] Step S30: Construct a cornering stiffness space state equation based on the extended Kalman filter according to the force equation based on the deviation, the rotational torque equation based on the deviation, and the vehicle space state equation based on the deviation;

[0115] Further, the constructing a cornering stiffness space state equation based on the extended Kalman filter according to the force equation based on the deviation, the rotational torque equation based on the deviation, and the vehicle space state equation based on the deviation includes:

[0116] Create a system state equation of the extended Kalman filter according to the vehicle space state equation based on the deviation;

[0117] Create a system measurement equation of the extended Kalman filter according to the force equation based on the deviation and the rotational torque equation based on the deviation;

[0118] Create a system observation equation of the extended Kalman filter based on the system measurement equation and system measurement noise, and create a transformation matrix based on the system measurement equation;

[0119] Create a state transition matrix based on the system state equation, and create an estimated error covariance matrix based on the system state equation, the process excitation noise covariance matrix, the state transition matrix, and its corresponding transposed matrix;

[0120] Create a state gain matrix according to the estimated error covariance matrix, the observation noise covariance matrix, the conversion matrix, and its corresponding transposed matrix;

[0121] Construct a cornering stiffness space state equation based on the extended Kalman filter according to the system state equation, the state gain matrix, the system observation equation, and the system measurement equation.

[0122] Among them, the cornering stiffness space state equation based on the extended Kalman filter is:

[0123]

[0124] In the formula, represents the system state equation, K represents the state gain matrix, represents the system observation equation, represents the system measurement equation, C af represents the cornering stiffness of the front wheels of the vehicle, C ar represents the cornering stiffness of the rear wheels of the vehicle, represents the speed deviation in the lateral direction of the vehicle.

[0125] Exemplarily, in this embodiment, the cornering stiffness of the front and rear wheels of the vehicle is used as the parameter to be estimated to obtain the space state equation for the cornering stiffness. Among them, the extended Kalman filter is applicable to solving linear and non-linear state equations, and it includes a prediction formula and an update formula.

[0126] Prediction formula:

[0127]

[0128] P k = F k P k-1 F k T + Q k (27)

[0129] Update formula:

[0130] K = P k H k T (H k P k H k T + R k ) -1 (28)

[0131]

[0132] P k+1 = P k - K T H k P k (30)

[0133] In the formula, represents the predicted state vector; f(x k-1 ) represents the system nonlinear state function; P k represents the covariance matrix of the estimated error of the state vector at time k, which represents the relationship between each element in the state vector; F k represents the state transition matrix, that is, transferring the state vector at time k - 1 to the state vector at time k; P k-1 represents the covariance matrix of the estimated error of the state vector at time k - 1; F k T represents the transpose matrix of F k ; Q k represents the process excitation noise covariance matrix, that is, the covariance matrix of the Gaussian noise of the predicted state, which is used to measure the accuracy of the model. The smaller its value, the more accurate the model; K represents the state gain matrix; H k represents the transformation matrix, which maps the state vector x k to the vector space where the measurement value is located; H k T represents the transpose matrix of H k ; R k represents the observation noise covariance matrix, that is, the covariance matrix of the Gaussian noise of the measurement value, which represents the error of the sensor measurement; represents the observation vector, that is, the state vector of the sensor measurement value, that is, the measurement result of the sensor; represents the measurement equation; represents the updated state vector; P k+1 represents the covariance matrix of the estimated error of the state vector at time k + 1; K T represents the transpose matrix of K.

[0134] In this embodiment, each equation based on the extended Kalman filter is created. Specifically:

[0135] According to the vehicle space state equation based on the deviation (i.e., Equation (25)), the system state equation of the extended Kalman filter is created. Then the system state equation of the extended Kalman filter is:

[0136]

[0137] Based on the deviation-based force equation (i.e., Equation (21)) and the deviation-based rotational moment equation (i.e., Equation (22)), the system measurement equation of the extended Kalman filter is created. Then, the system measurement equation of the extended Kalman filter is:

[0138]

[0139] Based on the system measurement equation and the system measurement noise (i.e., vk, which follows a Gaussian distribution), the system observation equation of the extended Kalman filter is created. Then, the system observation equation is:

[0140]

[0141] In this embodiment, a transformation matrix is created based on the system measurement equation, that is, by finding the Jacobian matrix to obtain the transformation matrix H k :

[0142]

[0143] In this embodiment, a state transition matrix is created based on the system state equation, that is, by (i.e., f(x k-1 )) finding the Jacobian matrix to obtain the state transition matrix F k :

[0144]

[0145] Based on the system state equation, the process excitation noise covariance matrix (i.e., Q k ), the state transition matrix and its corresponding transpose matrix, an estimated error covariance matrix is created. Then, the estimated error covariance matrix P k is:

[0146]

[0147]

[0148] Based on the estimated error covariance matrix, the observation noise covariance matrix (i.e., R k ), the transformation matrix and its corresponding transpose matrix, a state gain matrix is created. Then, the state gain matrix is:

[0149] In this embodiment, according to the system state equation, the state gain matrix, the system observation equation and the system measurement equation, a lateral stiffness space state equation based on the extended Kalman filter is constructed, that is, substituting Equations (31) to (37) into Equations (27) to (30), the lateral stiffness space state equation based on deviation can be obtained as:

[0150]

[0151] Step S40: Solve the cornering stiffness space state equation based on the extended Kalman filter algorithm and the obtained vehicle real-time operating condition parameters to obtain the vehicle's real-time cornering stiffness.

[0152] Exemplarily, in this embodiment, it is only necessary to obtain in real time the vehicle real-time operating condition parameters including vehicle mass, vehicle longitudinal speed, distances from the front and rear wheels to the vehicle's center of mass, the angle between the wheel direction and the X-axis of the vehicle body coordinate system, the desired yaw angular velocity, and the desired yaw angular acceleration, etc., substitute them into Equation (38), and finally solve Equation (38) through the extended Kalman filter algorithm to obtain the vehicle's real-time cornering stiffness, so as to realize the online identification of the cornering stiffness.

[0153] In the process of performing the extended Kalman filter in this embodiment, it is found that when directly performing the extended Kalman filter on the vehicle space state equation, the results will have large fluctuations; therefore, after improving the vehicle space state equation in this embodiment, a vehicle dynamics model based on deviation is obtained. In the subsequent steps of the extended Kalman filter, compared with the former, the vehicle parameter - cornering stiffness obtained has the characteristics of fast convergence and good convergence effect. It can be seen that the space state equation based on deviation adopted in this embodiment can directly obtain the corresponding parameters during the real vehicle movement process, thereby performing the online identification of the vehicle cornering stiffness, greatly improving the accuracy and timeliness of the cornering stiffness identification, and providing excellent parameters for the vehicle's autonomous driving environment.

[0154] In summary, in order to improve the control performance of intelligent driving vehicles, this embodiment provides a method for identifying the cornering stiffness of a vehicle based on the extended Kalman filter algorithm, which is applicable to the operating conditions where the tires are in the linear range under vehicle dynamics, has strong operability and can be identified in real time. Its space state equation based on deviation can effectively improve the identification accuracy of the cornering stiffness of commercial vehicles. In addition, in this embodiment, the extended Kalman filter is used for simulation, and the obtained experimental results have high accuracy and strong anti-interference ability; moreover, since the operating condition data required in this embodiment can be easily obtained from the vehicle, the feasibility of this embodiment is strong.

[0155] The embodiment of the present application also provides a vehicle cornering stiffness calculation device, including:

[0156] A first construction unit, which is used to construct a force equation based on deviation and a rotational moment equation based on deviation according to the two-degree-of-freedom model and the vehicle dynamics model based on deviation;

[0157] A second construction unit, which is used to construct a deviation-based vehicle spatial state equation according to the deviation-based force equation, and the deviation-based vehicle spatial state equation includes a functional relationship between a cornering stiffness parameter and a vehicle operating condition parameter;

[0158] A third construction unit, which is used to construct a cornering stiffness spatial state equation based on the extended Kalman filter according to the deviation-based force equation, the deviation-based rotational torque equation, and the deviation-based vehicle spatial state equation;

[0159] A calculation unit, which is used to solve the cornering stiffness spatial state equation based on the extended Kalman filter algorithm and the obtained real-time vehicle operating condition parameters to obtain the real-time cornering stiffness of the vehicle.

[0160] Further, the deviation-based vehicle spatial state equation is:

[0161]

[0162] In the formula, C af represents the cornering stiffness of the front wheels of the vehicle, C ar represents the cornering stiffness of the rear wheels of the vehicle, represents the speed deviation in the lateral direction of the vehicle, e2 represents the deviation between the vehicle direction and the road direction, represents the yaw rate deviation in the lateral direction of the vehicle, represents the desired yaw rate deviation in the lateral direction of the vehicle, m represents the vehicle mass, V x represents the longitudinal vehicle speed, l f represents the distance from the front wheels of the vehicle to the vehicle's center of mass, l r represents the distance from the rear wheels of the vehicle to the vehicle's center of mass.

[0163] Further, the third construction unit is specifically used for:

[0164] Create a system state equation of the extended Kalman filter according to the deviation-based vehicle spatial state equation;

[0165] Create a system measurement equation of the extended Kalman filter according to the deviation-based force equation and the deviation-based rotational torque equation;

[0166] Create a system observation equation of the extended Kalman filter based on the system measurement equation and system measurement noise, and create a transformation matrix based on the system measurement equation;

[0167] Create a state transition matrix based on the system state equation, and create an estimated error covariance matrix based on the system state equation, the process excitation noise covariance matrix, the state transition matrix, and its corresponding transposed matrix;

[0168] Create a state gain matrix according to the estimated error covariance matrix, the observation noise covariance matrix, the conversion matrix, and its corresponding transposed matrix;

[0169] Construct a cornering stiffness space state equation based on the extended Kalman filter according to the system state equation, the state gain matrix, the system observation equation, and the system measurement equation.

[0170] Further, the cornering stiffness space state equation based on the extended Kalman filter is:

[0171]

[0172] In the formula, represents the system state equation, K represents the state gain matrix, represents the system observation equation, represents the system measurement equation, C af represents the cornering stiffness of the vehicle's front wheels, C ar represents the cornering stiffness of the vehicle's rear wheels, represents the speed deviation in the lateral direction of the vehicle.

[0173] It should be noted that those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described device and each unit can refer to the corresponding processes in the foregoing embodiments of the vehicle cornering stiffness calculation method, and will not be elaborated herein.

[0174] The vehicle cornering stiffness calculation device provided in the above embodiment can be implemented in the form of a computer program, and this computer program can run on a vehicle cornering stiffness calculation device as shown in Figure 5 .

[0175] The embodiment of the present application also provides a vehicle cornering stiffness calculation device, including: a memory, a processor, and a network interface connected through a system bus. At least one instruction is stored in the memory, and at least one instruction is loaded and executed by the processor to implement all or part of the steps of the foregoing vehicle cornering stiffness calculation method.

[0176] Among them, the network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 5 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0177] The processor can be a CPU, or other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor, etc. The processor is the control center of the computer device and connects all parts of the computer device using various interfaces and circuits.

[0178] The memory can be used to store computer programs and / or modules. By running or executing the computer programs and / or modules stored in the memory, and calling the data stored in the memory, the processor realizes various functions of the computer device. The memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function (such as video playback function, image playback function, etc.); the data storage area can store data created according to the use of the mobile phone (such as video data, image data, etc.). In addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, memory, plug-in hard disks, smart media cards (SMCs), secure digital (SD) cards, flash cards, at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0179] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, all or part of the steps of the foregoing vehicle cornering stiffness calculation method are realized.

[0180] The embodiments of the present application implement all or part of the foregoing processes, and may also be completed by instructing relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the foregoing various methods may be implemented. Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0181] Those skilled in the art should understand that the embodiments of the present application may be provided as a method, system, server, or computer program product. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0182] It should be noted that in this text, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or system. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article, or system including that element.

[0183] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate for implementation in the processFigure 1 means for the functions specified in one process or a plurality of processes and / or boxes Figure 1 or a plurality of boxes.

[0184] The above are only specific embodiments of the present application, which enable those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for calculating the cornering stiffness of a vehicle, characterized in that Including the following steps: Construct a deviation-based force equation and a deviation-based rotational moment equation according to a two-degree-of-freedom model and a deviation-based vehicle dynamics model; Construct a deviation-based vehicle spatial state equation according to the deviation-based force equation, and the deviation-based vehicle spatial state equation includes a functional relationship between a cornering stiffness parameter and a vehicle operating condition parameter; Construct a cornering stiffness spatial state equation based on the extended Kalman filter according to the deviation-based force equation, the deviation-based rotational moment equation, and the deviation-based vehicle spatial state equation; Solve the cornering stiffness spatial state equation based on the extended Kalman filter algorithm and the obtained vehicle real-time operating condition parameters to obtain the real-time cornering stiffness of the vehicle.

2. The vehicle cornering stiffness calculation method according to claim 1, characterized in that, The deviation-based vehicle spatial state equation is: where C af represents the cornering stiffness of the vehicle's front wheels, C ar represents the cornering stiffness of the vehicle's rear wheels, represents the speed deviation in the lateral direction of the vehicle, e2 represents the deviation between the vehicle direction and the road direction, represents the yaw rate deviation in the lateral direction of the vehicle, represents the desired yaw rate deviation in the lateral direction of the vehicle, m represents the vehicle mass, V x represents the longitudinal vehicle speed, l f represents the distance from the vehicle's front wheels to the vehicle's center of mass, l r represents the distance from the vehicle's rear wheels to the vehicle's center of mass, δ represents the angle between the wheel direction and the X-axis of the vehicle body coordinate system.

3. The vehicle cornering stiffness calculation method according to claim 2, characterized in that, The constructing the cornering stiffness spatial state equation based on the extended Kalman filter according to the deviation-based force equation, the deviation-based rotational moment equation, and the deviation-based vehicle spatial state equation includes: Create a system state equation of the extended Kalman filter according to the deviation-based vehicle spatial state equation; Create a system measurement equation of the extended Kalman filter according to the deviation-based force equation and the deviation-based rotational moment equation; Create a system observation equation of the extended Kalman filter based on the system measurement equation and system measurement noise, and create a transformation matrix based on the system measurement equation; Create a state transition matrix based on the system state equation, and create an estimation error covariance matrix based on the system state equation, the process excitation noise covariance matrix, the state transition matrix, and its corresponding transpose matrix; Create a state gain matrix according to the estimation error covariance matrix, the observation noise covariance matrix, the transformation matrix, and its corresponding transpose matrix; Construct a cornering stiffness spatial state equation based on the extended Kalman filter according to the system state equation, the state gain matrix, the system observation equation, and the system measurement equation.

4. The vehicle cornering stiffness calculation method according to claim 3, characterized in that, The cornering stiffness spatial state equation based on the extended Kalman filter is: In the formula, represents the system state equation, and K represents the state gain matrix. represents the system observation equation. represents the system measurement equation, and C af represents the cornering stiffness of the front wheels of the vehicle, and C ar represents the cornering stiffness of the rear wheels of the vehicle. represents the lateral speed deviation of the vehicle.

5. A vehicle cornering stiffness calculation device, characterized in that, Including: A first construction unit for constructing a deviation-based force equation and a deviation-based rotational moment equation according to a two-degree-of-freedom model and a deviation-based vehicle dynamics model; A second construction unit for constructing a deviation-based vehicle spatial state equation according to the deviation-based force equation, and the deviation-based vehicle spatial state equation includes a functional relationship between a cornering stiffness parameter and a vehicle operating condition parameter; A third construction unit for constructing a cornering stiffness spatial state equation based on the extended Kalman filter according to the deviation-based force equation, the deviation-based rotational moment equation, and the deviation-based vehicle spatial state equation; A calculation unit for solving the cornering stiffness spatial state equation based on the extended Kalman filter algorithm and the obtained vehicle real-time operating condition parameters to obtain the real-time cornering stiffness of the vehicle.

6. The vehicle cornering stiffness calculation device according to claim 5, wherein The deviation-based vehicle spatial state equation is: Where, C af represents the cornering stiffness of the front wheels of the vehicle, C ar represents the cornering stiffness of the rear wheels of the vehicle, represents the speed deviation in the lateral direction of the vehicle, e2 represents the deviation between the vehicle direction and the road direction, represents the yaw rate deviation in the lateral direction of the vehicle, represents the desired yaw rate deviation in the lateral direction of the vehicle, m represents the mass of the vehicle, V x represents the longitudinal vehicle speed, l f represents the distance from the front wheels of the vehicle to the center of mass of the vehicle, l r represents the distance from the rear wheels of the vehicle to the center of mass of the vehicle, δ represents the angle between the wheel direction and the X-axis of the vehicle body coordinate system.

7. The vehicle cornering stiffness calculation device according to claim 6, characterized in that, The third construction unit is specifically used for: Create a system state equation of the extended Kalman filter according to the deviation-based vehicle spatial state equation; Create the system measurement equation of the extended Kalman filter according to the deviation-based force equation and the deviation-based rotational torque equation; Create the system observation equation of the extended Kalman filter based on the system measurement equation and the system measurement noise, and create a transformation matrix based on the system measurement equation; Create a state transition matrix based on the system state equation, and create an estimated error covariance matrix based on the system state equation, the process excitation noise covariance matrix, the state transition matrix and its corresponding transposed matrix; Create a state gain matrix according to the estimated error covariance matrix, the observation noise covariance matrix, the transformation matrix and its corresponding transposed matrix; Construct the cornering stiffness space state equation based on the extended Kalman filter according to the system state equation, the state gain matrix, the system observation equation and the system measurement equation.

8. The vehicle cornering stiffness calculation device according to claim 7, characterized in that, The cornering stiffness space state equation based on the extended Kalman filter is: In the formula, represents the system state equation, K represents the state gain matrix, represents the system observation equation, represents the system measurement equation, C af represents the cornering stiffness of the front wheels of the vehicle, C ar represents the cornering stiffness of the rear wheels of the vehicle, represents the speed deviation in the lateral direction of the vehicle.

9. A vehicle cornering stiffness calculation device, characterized in that, Including: A memory and a processor, wherein at least one instruction is stored in the memory, and the at least one instruction is loaded and executed by the processor to implement the vehicle cornering stiffness calculation method according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the vehicle cornering stiffness calculation method according to any one of claims 1 to 4.

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