Parameter and tire force real-time estimation method and system based on multi-model fusion

Abstract

The invention discloses a parameter and tire force real-time estimation method and system based on multi-model fusion, and relates to the technical field of vehicle parameter identification, and the method comprises the steps: building a three-state monorail vehicle dynamics model and a tire model according to vehicle parameters and dynamics parameters; performing formula transformation on the three-state monorail vehicle dynamics model to respectively obtain formulas of longitudinal, transverse and yaw angular accelerations; dividing parameters in the formula into parameters which can be collected by a low-cost sensor and parameters to be estimated, and respectively updating the formulas of longitudinal, transverse and yaw angular accelerations; constructing a nonlinear optimization problem based on the updated formula; according to the nonlinear optimization problem, based on vehicle parameters and kinetic parameters in the historical driving process, parameter estimation is carried out, and based on a parameter estimation result, real-time state estimation is carried out on tire force. Complete kinetic parameters can be estimated in real time through a low-cost sensor, and the real-time state of the tire is estimated based on the complete kinetic parameters.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle parameter identification, and in particular to a method and system for real-time estimation of parameters and tire forces based on multi-model fusion. Background Art

[0002] In the field of autonomous driving, obtaining accurate dynamic parameters is crucial for the design of vehicle motion controllers. However, obtaining dynamic parameters faces multiple challenges. First, many controller designs assume that the dynamic parameters are known. However, in actual scenarios, they cannot be directly measured by low-cost sensors and can only be estimated based on sensor observation information. Measuring dynamic parameters with dedicated equipment usually requires sending the vehicle to a specific test site, which is a complex, time-consuming and costly process. In addition, many existing parameter estimation methods can only estimate some parameters, while other parameters are assumed to be known, making it impossible to estimate the complete dynamic parameters, thus limiting the effective application of these methods. In addition, most studies ignore the time-varying characteristics of some of the dynamic parameters and are unable to estimate the time-varying parameters in real time, which affects the accuracy of other parameter estimates and the consistency of motion control. Summary of the Invention

[0003] The purpose of this application is to provide a method and system for real-time estimation of parameters and tire forces based on multi-model fusion, which can realize real-time estimation of complete dynamic parameters through low-cost sensors and real-time state estimation of tire lateral, longitudinal and vertical forces based on dynamic parameter estimation.

[0004] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides a method for real-time estimation of parameters and tire forces based on multi-model fusion, comprising: Obtain vehicle parameters of the target vehicle and dynamic parameters during driving; the vehicle parameters include body parameters and wheel parameters.

[0005] According to the vehicle parameters and dynamic parameters, based on the Newton-Euler law, a three-state single-track vehicle dynamics model integrating the wheel model and a simplified magic formula tire model considering the load transfer effect are established respectively.

[0006] The three-state single-track vehicle dynamics model integrated with the wheel model is transformed to obtain the formula expressions of the longitudinal acceleration, lateral acceleration and yaw acceleration respectively.

[0007] The parameters in the formulas for longitudinal acceleration, lateral acceleration, and yaw acceleration are divided into parameters that can be collected by low-cost sensors and parameters to be estimated, and the formulas for longitudinal acceleration, lateral acceleration, and yaw acceleration are updated separately; the parameters to be estimated include the longitudinal position of the vehicle's center of mass, the vehicle's mass, the vehicle's moment of inertia about the yaw axis, and the wheel's moment of inertia.

[0008] Based on the updated formula expressions of longitudinal acceleration, lateral acceleration and yaw acceleration, a nonlinear optimization problem based on longitudinal acceleration, lateral acceleration and yaw acceleration is constructed.

[0009] According to the nonlinear optimization problem, based on the vehicle parameters of the target vehicle and the dynamic parameters in the historical driving process, parameter estimation is performed on the parameters to be estimated to obtain parameter estimation results.

[0010] Based on the parameter estimation results, real-time state estimation of tire forces is performed; the tire forces include tire longitudinal force, tire vertical force and tire lateral force; the tire vertical force is calculated based on a simplified magic formula tire model that considers the load transfer effect.

[0011] Optionally, the formula expression of the three-state single-track vehicle dynamics model integrating the wheel model is: .

[0012] in, is the distance between the vehicle's center of mass and the front axle, is the distance between the vehicle's center of mass and the rear axle, is the vehicle mass, is the moment of inertia about the yaw axis, is the rear wheel lateral force, is the lateral force on the front wheel, is the yaw angular velocity, is the yaw angular acceleration, is the longitudinal velocity of the center of mass, is the lateral velocity of the center of mass, is the front wheel turning angle, is the rear wheel drive torque, is the moment of inertia of the front and rear wheels, is the rear wheel rotation angular acceleration, are the radii of the front and rear wheels, is the longitudinal acceleration of the center of mass, is the lateral acceleration of the center of mass.

[0013] Alternatively, a simplified magic formula tire model that takes into account the load transfer effect can be expressed as: .

[0014] .

[0015] in, is the height of the vehicle's center of mass, is the acceleration due to gravity, 、 、 、 、 are tire parameters, l For distance.

[0016] Optionally, the three-state single-track vehicle dynamics model integrated with the wheel model is transformed to obtain formula expressions for longitudinal acceleration, lateral acceleration, and yaw acceleration, respectively, including: The three equations in the three-state single-track vehicle dynamics model integrating the wheel model are combined in pairs to form three sets of equations.

[0017] Perform matrix transformation on the three sets of equations to obtain the matrix form of the three sets of equations.

[0018] Substituting the matrix forms of the three sets of equations into the three-state single-track vehicle dynamics model integrated with the wheel model, the formula expressions for the longitudinal acceleration, lateral acceleration and yaw acceleration are obtained.

[0019] Optionally, the three sets of equations are specifically: .

[0020] .

[0021] .

[0022] Optionally, the matrix forms of the three sets of equations are specifically: .

[0023] .

[0024] .

[0025] Optionally, the formula expressions for the longitudinal acceleration, lateral acceleration, and yaw acceleration are specifically: ; .

[0026] .

[0027] Optionally, the nonlinear optimization problem is expressed as follows: .

[0028] in, For the The predicted lateral acceleration at time , For the The predicted longitudinal acceleration at time , For the The predicted yaw acceleration at time , For the The lateral acceleration at time For the The longitudinal acceleration at time For the Yaw acceleration at the moment; is the weight coefficient of the lateral acceleration cost term, is the weight coefficient of the longitudinal acceleration cost term, is the weight coefficient of the yaw acceleration cost term, is the sliding window length, k It is the sampling point index corresponding to the moment after the right boundary of the sliding window.

[0029] Optionally, based on the parameter estimation results, real-time state estimation of the tire force is performed, specifically including: According to the formula , to estimate the longitudinal force of the tire; where, is the rear wheel longitudinal force.

[0030] According to the formula , to estimate the vertical force; where, is the vertical force on the front wheel, is the vertical force on the rear wheel.

[0031] The front wheel lateral force and the rear wheel lateral force are estimated based on the formula expression of the simplified magic formula tire model considering the load transfer effect.

[0032] In a second aspect, the present application provides a method for real-time estimation of parameters and tire forces based on multi-model fusion, comprising: The parameter acquisition module is used to obtain the vehicle parameters of the target vehicle and the dynamic parameters during driving; the vehicle parameters include body parameters and wheel parameters.

[0033] The model building module is used to establish a three-state single-track vehicle dynamics model integrating a wheel model and a simplified magic formula tire model considering load transfer effect according to the vehicle parameters and dynamic parameters and based on the Newton-Euler law.

[0034] The formula transformation module is used to transform the three-state single-track vehicle dynamics model that integrates the wheel model into a formula to obtain the formula expressions of the longitudinal acceleration, lateral acceleration and yaw angle acceleration respectively.

[0035] A formula update module is used to divide the parameters in the formula expressions of longitudinal acceleration, lateral acceleration and yaw acceleration into parameters that can be collected by low-cost sensors and parameters to be estimated, and to update the formula expressions of longitudinal acceleration, lateral acceleration and yaw acceleration respectively; the parameters to be estimated include the longitudinal position of the vehicle's center of mass, the vehicle's mass, the vehicle's moment of inertia about the yaw axis, and the wheel's moment of inertia.

[0036] The optimization problem building module is used to build a nonlinear optimization problem based on the longitudinal acceleration, the lateral acceleration and the yaw angle acceleration based on the updated formula expressions of the longitudinal acceleration, the lateral acceleration and the yaw angle acceleration.

[0037] The parameter estimation module is used to perform parameter estimation on the parameters to be estimated based on the vehicle parameters of the target vehicle and the dynamic parameters during the historical driving process according to the nonlinear optimization problem, and obtain the parameter estimation results: The tire force estimation module is used to perform real-time state estimation of tire forces based on parameter estimation results; the tire forces include tire longitudinal force, tire vertical force and tire lateral force; the tire vertical force is calculated based on a simplified magic formula tire model that considers load transfer effects.

[0038] According to the specific embodiments provided in this application, this application discloses the following technical effects: This application provides a real-time parameter and tire force estimation method and system based on multi-model fusion. This method first obtains the target vehicle's vehicle parameters and dynamic parameters during driving. Secondly, based on the Newton-Euler law, a three-state single-track vehicle dynamics model that incorporates the wheel model and a simplified magic formula tire model that considers load transfer effects are established. The three-state single-track vehicle dynamics model describes the vehicle's motion in the longitudinal, lateral, and yaw directions, while the simplified magic formula tire model describes the forces acting on the tire in the vertical, longitudinal, and lateral directions. Next, the three-state single-track vehicle dynamics model that incorporates the wheel model is transformed to obtain formulas for longitudinal acceleration, lateral acceleration, and yaw acceleration. The parameters in these formulas are then divided into parameters that can be acquired with low-cost sensors and parameters to be estimated. Low-cost sensor-acquired parameters include those directly measured by sensors, while parameters to be estimated include those that cannot be directly measured by sensors and require algorithmic estimation, such as the longitudinal position of the vehicle's center of mass, vehicle mass, vehicle moment of inertia about the yaw axis, and wheel moment of inertia. Next, based on the updated formulas for longitudinal acceleration, lateral acceleration, and yaw acceleration, a nonlinear optimization problem based on longitudinal acceleration, lateral acceleration, and yaw acceleration is constructed to find the optimal solution for the parameters to be estimated. Based on the nonlinear optimization problem, the parameters to be estimated are estimated based on the target vehicle's vehicle parameters and historical driving dynamic parameters. These estimation results are used to subsequently estimate the real-time state of tire forces. Finally, based on the parameter estimation results, real-time state estimation of tire forces is performed. Tire forces include longitudinal force, vertical force, and lateral force. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0040] Figure 1 A flowchart of a method for real-time parameter and tire force estimation based on multi-model fusion provided in one embodiment of the present application.

[0041] Figure 2 Schematic diagram of a three-state monorail vehicle dynamics model provided in an embodiment of the present application Figure 3 A schematic diagram of a wheel model provided in one embodiment of the present application.

[0042] Figure 4A schematic diagram of a vehicle longitudinal dynamics model provided in one embodiment of the present application.

[0043] Figure 5 A schematic diagram of the functional modules of a real-time system for parameters and tire forces based on multi-model fusion provided in one embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0045] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0046] Example 1 like Figure 1 As shown, this embodiment provides a method for real-time estimation of parameters and tire forces based on multi-model fusion, including: Step 101: Obtain vehicle parameters of a target vehicle and dynamic parameters during driving; the vehicle parameters include body parameters and wheel parameters.

[0047] Step 102: Based on the vehicle parameters and dynamic parameters and the Newton-Euler law, a three-state single-track vehicle dynamics model integrating a wheel model and a simplified magic formula tire model considering load transfer effects are established.

[0048] Step 103: Perform formula transformation on the three-state single-track vehicle dynamics model integrated with the wheel model to obtain formula expressions for longitudinal acceleration, lateral acceleration, and yaw acceleration, respectively.

[0049] Step 104: Divide the parameters in the formula expressions for the longitudinal acceleration, lateral acceleration, and yaw acceleration into parameters that can be collected by low-cost sensors and parameters to be estimated, and update the formula expressions for the longitudinal acceleration, lateral acceleration, and yaw acceleration respectively; the parameters to be estimated include the longitudinal position of the vehicle's center of mass, the vehicle's mass, the vehicle's moment of inertia about the yaw axis, and the wheel's moment of inertia.

[0050] Step 105: Based on the updated expressions of the longitudinal acceleration, the lateral acceleration, and the yaw acceleration, a nonlinear optimization problem based on the longitudinal acceleration, the lateral acceleration, and the yaw acceleration is constructed.

[0051] Step 106: According to the nonlinear optimization problem, based on the vehicle parameters of the target vehicle and the dynamic parameters during the historical driving process, parameter estimation is performed on the parameters to be estimated to obtain parameter estimation results.

[0052] Step 107: Based on the parameter estimation results, perform real-time state estimation of tire forces; the tire forces include tire longitudinal force, tire vertical force, and tire lateral force; the tire vertical force is calculated based on a simplified magic formula tire model that considers load transfer effects.

[0053] In some embodiments, when executing step 101, the specific steps may be as follows: Among the parameters obtained, the longitudinal position of the vehicle's center of mass, vehicle mass, vehicle moment of inertia about the yaw axis, wheel moment of inertia, tire parameters, and vehicle center of mass height are unknown parameters, while the effective radius r of the front and rear wheels is a known parameter, which is measured using a length measuring tool such as a tape measure.

[0054] Among them, the dynamic parameters during driving include yaw rate, longitudinal velocity of center of mass, front wheel angle, etc.

[0055] In some embodiments, when executing step 102, the specific steps may be as follows: First, a three-state single-track vehicle dynamics model is established, such as Figure 2 As shown. In the modeling process, it is assumed that the rear wheels are driven and the front wheels are steered, so the longitudinal force of the front wheels is = 0. Along axis, The force balance equation of the axis and the moment balance equation around the yaw axis can be obtained: (1).

[0056] (2).

[0057] (3).

[0058] in, is the distance between the vehicle's center of mass and the front axle, is the distance between the vehicle's center of mass and the rear axle, is the vehicle mass, is the moment of inertia about the yaw axis, is the rear wheel lateral force, is the lateral force on the front wheel, is the yaw angular velocity, is the yaw angular acceleration, is the longitudinal velocity of the center of mass, is the lateral velocity of the center of mass, is the front wheel turning angle, is the longitudinal acceleration of the center of mass, is the lateral acceleration of the center of mass.

[0059] Then, create Figure 3 Wheel model for a rear-wheel drive vehicle shown: (4).

[0060] in, is the moment of inertia of the front and rear wheels, is the rear wheel rotation angular acceleration, are the effective radii of the front and rear wheels, is the rear wheel drive torque.

[0061] Substituting formula (4) into formula (2) yields: (5).

[0062] By combining equations (1), (3) and (5), we can obtain the three-state monorail vehicle dynamics model integrating the wheel model as follows: (6).

[0063] Among them, for the tire lateral force and , a simplified magic formula tire model considering the nonlinear characteristics of the tire is established as follows: (7).

[0064] (8).

[0065] in, 、 、 is the magic formula coefficient of the front wheel, 、 、 is the magic formula coefficient for the rear wheel. is the front wheel slip angle, is the rear wheel slip angle. The magic formula coefficient and slip angle can be further expressed as: (9).

[0066] (10).

[0067] (11).

[0068] in, is the vertical force on the front wheel, is the vertical force on the rear wheel. 、 、 、 、 is the tire parameter. In order to calculate the load transfer effect and , establish the vehicle longitudinal dynamics model as Figure 4 As shown, the front and rear wheels are respectively around the contact points with the ground The balance of shaft torque can be obtained: (12).

[0069] in, is the height of the vehicle's center of mass, is the acceleration due to gravity.

[0070] Substituting Equations (9) to (12) into Equations (7) to (8) yields the simplified magic formula tire model that considers the load transfer effect: (13).

[0071] (14).

[0072] In some embodiments, when executing step 103, the specific steps may be as follows: The three-state single-track vehicle dynamics model integrated with the wheel model is transformed to obtain the formula expressions of longitudinal acceleration, lateral acceleration and yaw acceleration, including: The three equations in the three-state single-track vehicle dynamics model integrating the wheel model are combined in pairs to form three sets of equations.

[0073] Perform matrix transformation on the three sets of equations to obtain the matrix form of the three sets of equations.

[0074] Substituting the matrix forms of the three sets of equations into the three-state single-track vehicle dynamics model integrated with the wheel model, the formula expressions for the longitudinal acceleration, lateral acceleration and yaw acceleration are obtained.

[0075] Specifically, based on the three-state single-track vehicle dynamics model integrated with the wheel model established in step 102, the three equations in equation (6) are combined in pairs to form the following three sets of equations: (15).

[0076] (16).

[0077] (17).

[0078] By writing equations (15) to (17) in matrix form and sorting them out, we can obtain: (18).

[0079] (19).

[0080] (20).

[0081] Formulas (1), (3) and (5) are written in matrix form and sorted out to obtain: (twenty one).

[0082] (twenty two).

[0083] (twenty three).

[0084] Substituting Equation (18) into Equation (23), Equation (19) into Equation (22), and Equation (20) into Equation (21), we can obtain the expressions of longitudinal acceleration, lateral acceleration, and yaw acceleration as follows: (twenty four).

[0085] (25).

[0086] (26).

[0087] In some embodiments, when executing step 104, the specific steps may be as follows: The historical driving data collected by low-cost sensors is 、 、 、 、 、 、 、 、 , and define the state vector as . Define the known parameter vector as , define the parameter vector to be estimated as .

[0088] Substituting the defined parameters to be estimated into equations (24) to (26) and sorting them out, we can obtain: (27).

[0089] (28).

[0090] (29).

[0091] in, is the lateral acceleration predicted based on historical driving data, is the longitudinal acceleration predicted based on historical driving data, is the yaw acceleration predicted based on historical driving data.

[0092] In some embodiments, when executing step 105, the specific steps may be as follows: Equations (27) to (29) can be used to construct a nonlinear optimization problem based on the historical driving data of the current sliding window: (30).

[0093] in, For the The predicted lateral acceleration at time , For the The predicted longitudinal acceleration at time , For the The predicted yaw acceleration at time , For the The lateral acceleration at time For the The longitudinal acceleration at time For the Yaw acceleration at the moment; is the weight coefficient of the lateral acceleration cost term, is the weight coefficient of the longitudinal acceleration cost term, is the weight coefficient of the yaw acceleration cost term, is the sliding window length, k is the sampling point index corresponding to the moment after the right boundary of the sliding window, and the time step interval covered by the sliding window itself is , a total of n sampling points.

[0094] In the nonlinear optimization problem constructed by formula (30), by selecting appropriate parameters to be estimated To minimize the cost function based on the current sliding window , thereby achieving real-time estimation of the longitudinal position of the vehicle's center of mass, vehicle mass, vehicle moment of inertia around the yaw axis, and wheel moment of inertia.

[0095] In some embodiments, when executing step 106, the specific steps may be as follows: An optimization problem based on three equivalent cost terms of tire lateral force is constructed and solved using historical driving data in the current sliding window to achieve real-time estimation of tire parameters and vehicle center of gravity height.

[0096] Based on the simplified magic formula tire model that considers the load transfer effect and the estimated results of the longitudinal position of the vehicle center of mass, vehicle mass, vehicle moment of inertia about the yaw axis and wheel moment of inertia, the tire parameters and vehicle center of mass height can be further estimated in real time.

[0097] From equations (18) to (20), three equivalent calculation formulas for tire lateral force can be obtained: (31).

[0098] (32).

[0099] (33).

[0100] in, and ( ) indicates the The tire lateral force is calculated using these methods. The state quantities on the right side of equations (31) to (33) can all be collected by low-cost sensors, and the unknown parameters have been estimated in step 104. Therefore, the three calculation methods mentioned above can all achieve the true value estimation of the tire lateral force. The predicted value of the tire lateral force can be obtained from equations (13) to (14): (34).

[0101] (35).

[0102] in, is the front wheel lateral force predicted based on historical driving data, is the rear wheel lateral force predicted based on historical driving data. Furthermore, Equations (34) to (35) can also be expressed in the following mathematical form: (36).

[0103] (37).

[0104] in, is the parameter vector to be estimated in step 104. According to equations (31) to (37) and using the historical driving data of the current sliding window, the three equivalent cost optimization problems based on the tire lateral force can be constructed as follows: (38).

[0105] in, For the Predicted front wheel lateral force at time, For the Predicted rear wheel lateral force at moment t. For the Time passes The front wheel lateral force calculated in this way is For the Time passes The rear wheel lateral force is calculated in this way. It is The front wheel lateral force cost term under this method is: It is The rear wheel lateral force cost term under this method.

[0106] In the optimization problem of three equivalent cost terms based on tire lateral force constructed in formula (38), by selecting appropriate parameters to be estimated To minimize the cost function based on the current sliding window , thus achieving real-time estimation of tire parameters and vehicle center of mass height.

[0107] In some embodiments, when executing step 107, the specific steps may be as follows: Based on the established wheel model, vehicle longitudinal dynamics model and simplified magic formula tire model considering load transfer effect, and based on the real-time parameter estimation results, real-time state estimation of tire longitudinal force, vertical force and lateral force can be performed.

[0108] From formula (4), the expression of tire longitudinal force can be obtained as: (39).

[0109] in, and It can be directly measured by low-cost sensors. are known parameters, It is estimated in step 104. Therefore, the tire longitudinal force Real-time estimation is achieved.

[0110] Formula (12) is the expression of the tire vertical force. It can be directly measured by low-cost sensors. are known parameters, 、 and In step 104, it is estimated that It is estimated in step 104. Therefore, the tire vertical force and Real-time estimation is achieved.

[0111] Equations (13) and (14) are expressions for tire lateral force. 、 、 、 、 It can be directly measured by low-cost sensors. are known parameters, 、 and In step 104, it is estimated that 、 、 、 、 、 It is estimated in step 104. Therefore, the tire lateral force and Real-time estimation is achieved.

[0112] Example 2 like Figure 5 As shown, this embodiment provides a real-time parameter and tire force estimation system based on multi-model fusion, including: The parameter acquisition module 501 is used to acquire vehicle parameters of the target vehicle and dynamic parameters during driving; the vehicle parameters include body parameters and wheel parameters.

[0113] The model building module 502 is used to build a three-state single-track vehicle dynamics model integrating a wheel model and a simplified magic formula tire model considering load transfer effect according to the vehicle parameters and dynamic parameters and based on the Newton-Euler law.

[0114] The formula transformation module 503 is used to transform the three-state single-track vehicle dynamics model integrated with the wheel model to obtain formula expressions for longitudinal acceleration, lateral acceleration and yaw acceleration respectively.

[0115] A formula updating module 504 is configured to divide the parameters in the formula expressions for the longitudinal acceleration, lateral acceleration, and yaw acceleration into parameters that can be collected by low-cost sensors and parameters to be estimated, and to update the formula expressions for the longitudinal acceleration, lateral acceleration, and yaw acceleration, respectively; the parameters to be estimated include the longitudinal position of the vehicle's center of mass, the vehicle's mass, the vehicle's moment of inertia about the yaw axis, and the wheel's moment of inertia.

[0116] The optimization problem construction module 505 is used to construct a nonlinear optimization problem based on the longitudinal acceleration, the lateral acceleration and the yaw acceleration based on the updated formula expressions of the longitudinal acceleration, the lateral acceleration and the yaw acceleration.

[0117] The parameter estimation module 506 is used to perform parameter estimation on the parameters to be estimated based on the nonlinear optimization problem, the vehicle parameters of the target vehicle and the dynamic parameters during the historical driving process, and obtain parameter estimation results.

[0118] Tire force estimation module 507 is used to perform real-time state estimation of tire forces based on parameter estimation results; the tire forces include tire longitudinal force, tire vertical force, and tire lateral force; the tire vertical force is calculated based on a simplified magic formula tire model that considers load transfer effects.

[0119] In summary, this application has the following technical effects: This application proposes a method for real-time estimation of complete dynamic parameters and tire lateral, longitudinal, and vertical forces based on multi-model fusion. First, a three-state single-track vehicle dynamics model integrating the wheel model and a simplified magic formula tire model that considers load transfer effects are established. Second, a nonlinear optimization problem based on longitudinal acceleration, lateral acceleration, and yaw acceleration is constructed and solved using historical driving data within the current sliding window, enabling real-time estimation of the vehicle's longitudinal center of mass, vehicle mass, vehicle moment of inertia about the yaw axis, and wheel moment of inertia. Third, an optimization problem based on three equivalent cost terms for the tire lateral force is constructed and solved using historical driving data within the current sliding window, enabling real-time estimation of tire parameters and vehicle center of mass height. Finally, based on the parameter estimation results, real-time state estimation of the tire longitudinal, vertical, and lateral forces is performed. This method achieves real-time estimation of complete dynamic parameters using only low-cost sensors, ensuring ease and efficiency of parameter acquisition, practical applicability, real-time performance, model accuracy, and control robustness. At the same time, this method can perform real-time state estimation of tire longitudinal force, vertical force and lateral force, providing key state information for vehicle motion control, which is conducive to the accurate formulation of control strategies. The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A real-time estimation method of parameters and tire forces based on multi-model fusion, characterized in that: include: Obtain vehicle parameters of the target vehicle and dynamic parameters during driving; The vehicle parameters include body parameters and wheel parameters; According to the vehicle parameters and dynamic parameters, based on the Newton-Euler law, a three-state single-track vehicle dynamics model integrating the wheel model and a simplified magic formula tire model considering the load transfer effect are established respectively; The three-state single-track vehicle dynamics model integrated with the wheel model is transformed to obtain the formula expressions of longitudinal acceleration, lateral acceleration and yaw acceleration respectively. Dividing the parameters in the formulas for longitudinal acceleration, lateral acceleration, and yaw acceleration into parameters that can be collected by low-cost sensors and parameters to be estimated, and updating the formulas for longitudinal acceleration, lateral acceleration, and yaw acceleration respectively; the parameters to be estimated include the longitudinal position of the vehicle's center of mass, vehicle mass, vehicle moment of inertia about the yaw axis, and wheel moment of inertia; Based on the updated expressions of longitudinal acceleration, lateral acceleration and yaw acceleration, a nonlinear optimization problem based on longitudinal acceleration, lateral acceleration and yaw acceleration is constructed; According to the nonlinear optimization problem, based on the vehicle parameters of the target vehicle and the dynamic parameters during the historical driving process, the parameters to be estimated are estimated to obtain parameter estimation results; Based on the parameter estimation results, real-time state estimation of tire forces is performed; the tire forces include tire longitudinal force, tire vertical force and tire lateral force; The tire vertical force is calculated based on a simplified magic formula tire model that takes load transfer effects into account.

2. The method for real-time estimation of parameters and tire forces based on multi-model fusion according to claim 1, characterized in that: The formula of the three-state single-track vehicle dynamics model integrating the wheel model is: ； in, is the distance between the vehicle's center of mass and the front axle, is the distance between the vehicle's center of mass and the rear axle, is the vehicle mass, is the moment of inertia about the yaw axis, is the rear wheel lateral force, is the lateral force on the front wheel, is the yaw angular velocity, is the yaw angular acceleration, is the longitudinal velocity of the center of mass, is the lateral velocity of the center of mass, is the front wheel turning angle, is the rear wheel drive torque, is the moment of inertia of the front and rear wheels, is the rear wheel rotation angular acceleration, are the radii of the front and rear wheels, is the longitudinal acceleration of the center of mass, is the lateral acceleration of the center of mass.

3. The method for real-time estimation of parameters and tire forces based on multi-model fusion according to claim 2, characterized in that: The simplified magic formula tire model considering the load transfer effect is expressed as: ； ； in, is the height of the vehicle's center of mass, is the acceleration due to gravity, 、 、 、 、 are tire parameters, l For distance.

4. The method for real-time estimation of parameters and tire forces based on multi-model fusion according to claim 3, characterized in that: The three-state single-track vehicle dynamics model integrated with the wheel model is transformed to obtain the formula expressions of longitudinal acceleration, lateral acceleration and yaw acceleration, including: The three equations in the three-state single-track vehicle dynamics model integrating the wheel model are combined in pairs to form three sets of equations respectively; Perform matrix transformation on the three sets of equations to obtain the matrix form of the three sets of equations; Substituting the matrix forms of the three sets of equations into the three-state single-track vehicle dynamics model integrated with the wheel model, the formula expressions for the longitudinal acceleration, lateral acceleration and yaw acceleration are obtained.

5. The method for real-time estimation of parameters and tire forces based on multi-model fusion according to claim 4, characterized in that: The three sets of equations are: ； ； 。 6. The method for real-time estimation of parameters and tire forces based on multi-model fusion according to claim 5, characterized in that: The matrix forms of the three sets of equations are specifically: ； ； 。 7. The method for real-time estimation of parameters and tire forces based on multi-model fusion according to claim 6, characterized in that: The formula expressions for the longitudinal acceleration, lateral acceleration and yaw acceleration are specifically: ； ； 。 8. The method for real-time estimation of parameters and tire forces based on multi-model fusion according to claim 7, characterized in that: The formula expression of the nonlinear optimization problem is: ； in, For the The predicted lateral acceleration at time , For the The predicted longitudinal acceleration at time , For the The predicted yaw acceleration at time , For the The lateral acceleration at time For the The longitudinal acceleration at time For the Yaw acceleration at the moment; is the weight coefficient of the lateral acceleration cost term, is the weight coefficient of the longitudinal acceleration cost term, is the weight coefficient of the yaw acceleration cost term, is the sliding window length, k It is the sampling point index corresponding to the moment after the right boundary of the sliding window.

9. The method for real-time estimation of parameters and tire forces based on multi-model fusion according to claim 8, characterized in that: Based on the parameter estimation results, the tire force is estimated in real time, including: According to the formula , to estimate the longitudinal force of the tire; where, is the longitudinal force of the rear wheel; According to the formula , to estimate the vertical force; where, is the vertical force on the front wheel, is the vertical force on the rear wheel; The front wheel lateral force and the rear wheel lateral force are estimated based on the formula expression of the simplified magic formula tire model considering the load transfer effect.

10. A real-time parameter and tire force estimation system based on multi-model fusion, characterized in that: include: A parameter acquisition module is used to obtain the vehicle parameters of the target vehicle and the dynamic parameters during driving; The vehicle parameters include body parameters and wheel parameters; a model building module for building, based on the vehicle parameters and dynamic parameters and the Newton-Euler law, a three-state single-track vehicle dynamics model integrating a wheel model and a simplified magic formula tire model considering a load transfer effect; The formula transformation module is used to transform the three-state single-track vehicle dynamics model integrated with the wheel model to obtain the formula expressions of longitudinal acceleration, lateral acceleration and yaw acceleration respectively; a formula updating module for dividing the parameters in the formula expressions for longitudinal acceleration, lateral acceleration, and yaw acceleration into parameters that can be collected by low-cost sensors and parameters to be estimated, and updating the formula expressions for longitudinal acceleration, lateral acceleration, and yaw acceleration respectively; the parameters to be estimated include the longitudinal position of the vehicle's center of mass, the vehicle's mass, the vehicle's moment of inertia about the yaw axis, and the wheel's moment of inertia; An optimization problem construction module, used for constructing a nonlinear optimization problem based on the longitudinal acceleration, the lateral acceleration and the yaw acceleration based on the updated formula expressions of the longitudinal acceleration, the lateral acceleration and the yaw acceleration; a parameter estimation module, configured to perform parameter estimation on parameters to be estimated based on the nonlinear optimization problem and the vehicle parameters of the target vehicle and the dynamic parameters during historical driving, thereby obtaining a parameter estimation result; A tire force estimation module, configured to perform real-time state estimation of tire forces based on parameter estimation results; the tire forces include tire longitudinal force, tire vertical force, and tire lateral force; The tire vertical force is calculated based on a simplified magic formula tire model that takes load transfer effects into account.

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