A method for estimating road adhesion coefficient and tire force jointly

Abstract

The application discloses a kind of road adhesion coefficient and tire force joint estimation method, including according to motor torque and wheel speed, using PDI control algorithm to estimate automobile longitudinal force, according to automobile longitudinal force, lateral acceleration, longitudinal acceleration, front wheel angle and yaw angular velocity, using extended Kalman filtering algorithm, estimate tire lateral force, according to the longitudinal acceleration and lateral acceleration of automobile, estimate the vertical load of automobile tire, according to vertical load, tire slip ratio, side slip angle, tire longitudinal and lateral stiffness, wheel speed, front wheel angle, yaw angular velocity and automobile longitudinal and lateral acceleration, estimate the normalized tire longitudinal force and lateral force and road adhesion coefficient etc., the application realizes the joint estimation of road, tire force etc. parameter by automobile dynamics parameter and tire parameter, improves the accuracy of estimation effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of state parameter estimation of electric wheel drive vehicles, in particular to a road adhesion coefficient and tire force joint estimation method. BACKGROUND

[0002] With the rapid development of the automobile industry, the social problems it brings have gradually become prominent. The traditional internal combustion engine vehicle has been unable to meet the needs of modern society due to problems such as large exhaust emission and low energy utilization rate, and the electric vehicle with environmental protection, high efficiency, energy diversification and intelligence has become the mainstream development trend. The hub motor, wheel and brake device are integrated into an electric wheel, making the structure of the vehicle more compact, greatly saving the vehicle body space, so that the layout of the whole vehicle becomes more flexible. Its linear control capability makes the chassis more lightweight, the transmission efficiency more efficient, and the power output more efficient and stable. Although the distributed drive electric vehicle with hub motors has made great progress, some key problems have not been solved. Whether it is the vehicle stability control for vehicle safety or the motion control for intelligent driving, the real-time and accurate acquisition of vehicle system state variables is crucial.

[0003] At present, in the estimation of vehicle state parameters, the identification method of road adhesion coefficient excessively depends on the accuracy of related parameters in the tire model and algorithm, and the parameters are generally fixed values that do not change with the change of road conditions and tire shape. In the process of using Kalman filter algorithm, real-time and accuracy can indeed be guaranteed, but most of them only use the vehicle model as the ontology of state estimation, without considering the influence of tire related parameters.

[0004] Therefore, it is urgent to consider the road adhesion coefficient estimation method considering the mechanical state of automobile tires and automobile dynamics. SUMMARY

[0005] This section aims to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, abstract and title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0006] Therefore, the purpose of the present application is to provide a road adhesion coefficient and tire force joint estimation method, which realizes the joint estimation of road, tire force and other parameters through automobile dynamics parameters and tire parameters, and improves the accuracy of estimation effect.

[0007] To solve the above technical problems, according to one aspect of the present application, the present application provides the following technical scheme:

[0008] A road adhesion coefficient and tire force combined estimation method comprises the following steps:

[0009] S1, according to the motor torque and the wheel speed, the PID control algorithm is used to estimate the automobile longitudinal force;

[0010] S2, according to the automobile longitudinal force, lateral acceleration, longitudinal acceleration, front wheel angle and yaw rate, the extended Kalman filter algorithm is used to estimate the tire lateral force;

[0011] S3, according to the longitudinal acceleration and lateral acceleration of the automobile, the vertical load of the automobile tire is estimated;

[0012] S4, according to the vertical load, tire slip ratio, side slip angle, longitudinal and lateral stiffness of the tire, wheel speed, front wheel angle, yaw rate and longitudinal and lateral acceleration of the automobile, the normalized tire longitudinal force and lateral force and road adhesion coefficient are estimated;

[0013] S5, the angle between the tire forces of each wheel of the automobile is determined from the normalized lateral force and longitudinal force, the tire force sum of the ground at this time is determined from the vertical load of the automobile tire and the road adhesion coefficient, and the normalized tire force angle at this time is corrected by the estimated tire lateral force and tire force sum, to obtain the corrected accurate road adhesion coefficient.

[0014] As a preferred scheme of the road adhesion coefficient and tire force combined estimation method, in the step S1, the automobile longitudinal dynamics estimation equation is:

[0015] ;

[0016] wherein, is the motor torque value obtained by the sensor, is the speed value obtained by the sensor, is the moment of inertia of the automobile yaw, is the true value of the automobile longitudinal force, is the rolling radius of the automobile wheel.

[0017] According to the above formula, the longitudinal force PID estimator of a single wheel is obtained, and the state observer equation is as follows:

[0018] ;

[0019] In the above formula, is the estimation result of the PID state observer, and are the proportional and differential structure parameters in the PID observer, is the error between the wheel speed estimation value and the true value of the wheel speed, The error between the longitudinal force estimation value of the single wheel and the true value;

[0020] wherein, , the angular acceleration estimation value of the single wheel Obtained by the PID observer:

[0021] ;

[0022] In the above formula is the integral link structure parameter in the tire rotation speed PID observer.

[0023] As a preferred scheme of the road adhesion coefficient and tire force joint estimation method, in step S2, according to the vehicle longitudinal force, lateral acceleration, longitudinal acceleration, front wheel steering angle and yaw rate, the tire lateral force is estimated by using the extended Kalman filter algorithm, and the steps are as follows:

[0024] First, the vehicle dynamics system is designed based on the double-track three-degree-of-freedom model to estimate the wheel lateral force by using the extended Kalman filter observer; the longitudinal, lateral and yaw motion equations of the double-track three-degree-of-freedom vehicle model are as follows:

[0025] ;

[0026] ;

[0027] ;

[0028] wherein, is the front wheel steering angle; is the vehicle yaw moment of inertia; is the longitudinal force estimation result of the previous step, and is the distance from the center of mass to the front and rear axles, and is the wheel track of the front and rear axles. is the vehicle mass, is the vehicle longitudinal acceleration, is the vehicle lateral acceleration, is the vehicle yaw rate;

[0029] Select as the state variable of the tire lateral force observation system, as the observation variable, the three-degree-of-freedom yaw is converted into a state space equation:

[0030] ;

[0031] ;

[0032] where, state transition matrix ; and respectively represent the model uncertainty in the process equation and the measurement equation in the state-space system, the state observation matrix and the input matrix in the measurement equation are respectively:

[0033] ;

[0034] .

[0035] As a preferred scheme of the road adhesion coefficient and tire force joint estimation method, in step S3, the calculation formula of the four wheel loads is:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] In the formula, and respectively correspond to the vertical loads of the four wheels, is the height of the mass center of the vehicle, is the wheelbase of the vehicle body.

[0041] As a preferred scheme of the road adhesion coefficient and tire force joint estimation method, in step S4, the Dugoff tire model is selected as the calculation method of the tire normalized tire force, and the calculation formula is as follows:

[0042] ;

[0043] ;

[0044] In the formula is the road adhesion coefficient, is the tire slip angle, is the tire slip ratio, is the tire longitudinal stiffness, is the tire lateral stiffness. Wherein is the nonlinear error caused by tire slip, which is calculated as:

[0045] ;

[0046] ;

[0047] wherein, as the impact factor, is the longitudinal speed of the automobile wheel;

[0048] According to the formula, combined with the three-degree-of-freedom dynamic model of the automobile, the quadratic Kalman filtering algorithm is used, the state variable is , the measurement variable is , the input is , and the road adhesion coefficient is obtained.

[0049] As a preferred scheme of the road adhesion coefficient and tire force joint estimation method, in the step S5, the automobile tire force angle is:

[0050] ;

[0051] wherein is the longitudinal force of the tire, is the lateral force of the tire, is the road adhesion coefficient, is the automobile tire force angle;

[0052] According to the formula:

[0053] ;

[0054] wherein is the normalized lateral force correction estimation value, is the vertical load of the tire, and the final normalized tire force is obtained by designing a PID observer:

[0055] ;

[0056] wherein: and are PID parameters.

[0057] The normalized tire force is used as the feedback value, and the joint parameter estimation is formed.

[0058] Compared with the prior art, the present application has the beneficial effect that: the accurate dynamic parameters obtained by the automobile sensor are combined to estimate the longitudinal force, lateral force, normalized longitudinal force and normalized lateral force of the automobile, and the road adhesion coefficient is estimated according to the values, and the wheel force angle and lateral force are corrected. In the longitudinal force estimation, the rotation speed is used as the basis and the torque is corrected, which greatly improves the accuracy of the longitudinal force estimation. The extended Kalman filtering algorithm is introduced to estimate the lateral force of the automobile, the road adhesion coefficient is estimated by using the normalized tire force, and the error in the extended Kalman filtering algorithm is corrected by using the normalized tire force to calculate the tire angle. Through the combined estimation of the tire force and the road adhesion coefficient, not only the wheel tire force (including longitudinal force and lateral force) can be accurately estimated, but also the tire factor can be introduced into the road adhesion coefficient, which is close to the actual driving conditions of the automobile. The three dimensions of the road, the tire and the automobile are considered, and the three dimensions are dynamically corrected, which truly realizes the accuracy of the algorithm in a wide range of working conditions and provides accurate reference values for subsequent automobile control algorithms. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the present application will be described in detail below with reference to the drawings and detailed embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Among them:

[0060] Figure 1 The flowchart of the road adhesion coefficient and tire force combined estimation method of the present application. DETAILED DESCRIPTION

[0061] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings.

[0062] Secondly, the present application is described in detail in combination with the schematic diagram. In order to facilitate the description, the cross-sectional view of the device structure will be partially enlarged without general proportion, and the schematic diagram is only an example, which should not limit the scope of protection of the present application. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual manufacture.

[0063] In order to make the purposes, technical solutions and advantages of the present application more clear, the embodiments of the present application will be further described in detail below with reference to the drawings.

[0064] The application provides a road adhesion coefficient and tire force combined estimation method, which realizes combined estimation of road surface, tire force and other parameters through automobile dynamics parameters and tire parameters, and improves the accuracy of estimation effect.

[0065] Figure 1 The flow chart of the road adhesion coefficient and tire force combined estimation method is shown, please refer to Figure 1 The road adhesion coefficient and tire force combined estimation method of the embodiment is specifically as follows:

[0066] S1, according to the motor torque, wheel speed, PID control algorithm is used to estimate the longitudinal force of the automobile.

[0067] The automobile longitudinal dynamics estimation equation is:

[0068] ;

[0069] Among them, The motor torque value obtained by the sensor, The speed value obtained by the sensor, The moment of inertia of automobile yaw, The true value of automobile longitudinal force, according to the above formula, the longitudinal force PID estimator of single wheel is obtained, and the state observer equation is as follows:

[0070] ;

[0071] In the above formula, The estimation result of the PID state observer, And The proportional and differential structure parameters in the PID observer, The error between the wheel speed estimation value and the true value of the wheel speed, The error between the single wheel longitudinal force estimation value and the true value, which can be calculated as:

[0072] ;

[0073] ;

[0074] The angular acceleration estimation value of single wheel Can be obtained by the PID observer:

[0075] ;

[0076] In the above formula The integral link structure parameter in the tire speed PID observer.

[0077] S2, according to the longitudinal force of the automobile, lateral acceleration, longitudinal acceleration, front wheel steering angle and yaw rate, the tire lateral force is estimated by using the extended Kalman filter algorithm.

[0078] Firstly, the extended Kalman filter observer is designed to estimate the tire lateral force based on the double-track three-degree-of-freedom model. The longitudinal, lateral and yaw motion equations of the double-track three-degree-of-freedom vehicle model can be written as:

[0079] ;

[0080] ;

[0081] ;

[0082] wherein, is the front wheel steering angle; is the yaw moment of inertia of the vehicle; is the longitudinal force estimation result of the previous step, and are the distances from the mass center to the front and rear axles, and are the wheelbase of the front and rear axles. is the mass of the automobile, is the longitudinal acceleration of the automobile, is the lateral acceleration of the automobile, is the yaw rate of the automobile.

[0083] The state variable of the tire lateral force observation system is selected as , and the observation variable is selected as , and the three-degree-of-freedom yaw is converted into a state space equation:

[0084] ;

[0085] ;

[0086] wherein, the state transition matrix ; and respectively represent the model uncertainty in the process equation and the measurement equation in the state space system, and the state observation matrix and the input matrix in the measurement equation are respectively:

[0087] ;

[0088] .

[0089] S3, according to the longitudinal and lateral acceleration of the vehicle, estimate the vertical load of the vehicle tires, the calculation formula of the four wheel loads is:

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] In the formula, and correspond to the vertical load of the four wheels, is the height of the vehicle mass center, is the wheelbase of the vehicle body.

[0095] S4, according to the vertical load, tire slip ratio, side slip angle, and tire longitudinal and lateral stiffness, wheel speed, front wheel angle, yaw rate, vehicle longitudinal and lateral acceleration, estimate the normalized tire longitudinal force and lateral force, and road adhesion coefficient.

[0096] Fully consider the authenticity of parameter estimation, select Dugoff tire model as the calculation method of tire normalized tire force, the calculation formula is as follows:

[0097] ;

[0098] ;

[0099] In the formula is the road adhesion coefficient, is the tire side slip angle, is the tire slip ratio, is the tire longitudinal stiffness, is the lateral stiffness of the tire. Wherein is the nonlinear error caused by tire slip, which is calculated as:

[0100] ;

[0101] ;

[0102] In the formula, as an influencing factor, is the longitudinal speed of the vehicle wheel.

[0103] In order to ignore the influence of road adhesion coefficient, introduce the formula of normalized tire force as follows:

[0104] ;

[0105] ;

[0106] According to the formula, combined with the three-degree-of-freedom dynamics model of the vehicle, the quadratic Kalman filter algorithm is used to As a state variable, As measurement variables, the input is , and obtain the road adhesion coefficient.

[0107] S5. Calculate the tire force angle for each wheel based on the normalized lateral and longitudinal forces. Using the vertical load on the tires and the road adhesion coefficient, the sum of the tire forces on the ground can be calculated. The normalized tire force angle can be corrected based on the estimated sum of the lateral and tire forces to achieve the corrected tire force, thereby obtaining the corrected road adhesion coefficient.

[0108] The tire force angle of the car is:

[0109] ;

[0110] In the formula is the tire longitudinal force, is the tire lateral force, is the vehicle tire force angle.

[0111] At this time, compared with the lateral force and road adhesion coefficient obtained by the secondary state observer, the tire longitudinal force obtained in step 1 has a greater reference value, so according to the formula:

[0112] ;

[0113] in is the normalized lateral force correction estimate, is the vertical load on the tire, For the road adhesion coefficient, a PID observer is designed and the final normalized tire force is obtained as:

[0114] ;

[0115] Where: and are PID parameters.

[0116] The normalized tire force is used as the feedback value to form a joint parameter estimate.

[0117] Although the present application has been described with reference to the embodiments above, various changes and modifications can be suggested to one skilled in the art, and it is intended that the present application encompass such changes and modifications as fall within the scope of the appended claims. Particularly, each feature disclosed in the description and / or the claims can be used in the combination with each of the features disclosed in the description and / or the claims, unless specifically stated otherwise. Therefore, the present application is not intended to be limited to the particular embodiments disclosed in the description and / or the claims.

Claims

1. A method for jointly estimating road adhesion coefficient and tire force, characterized in that: include: S1, using the PID control algorithm to estimate the longitudinal force of the vehicle based on the motor torque and wheel speed; S2, using the extended Kalman filter algorithm to estimate the tire lateral force based on the vehicle longitudinal force, lateral acceleration, longitudinal acceleration, front wheel angle and yaw rate; S3. Estimate the vertical load on the vehicle tires based on the longitudinal acceleration and lateral acceleration of the vehicle; S4, estimating normalized tire longitudinal force and lateral force and road adhesion coefficient based on vertical load, tire slip rate, sideslip angle, tire longitudinal and lateral stiffness, wheel speed, front wheel angle, yaw rate, and vehicle longitudinal and lateral acceleration; S5. Calculate the angle of the tire forces of each wheel of the vehicle from the normalized lateral force and longitudinal force, calculate the sum of the tire forces on the ground at this time using the vertical load of the vehicle tire and the road adhesion coefficient, correct the normalized tire force angle at this time using the estimated sum of the tire lateral force and the tire force, and obtain the corrected accurate road adhesion coefficient.

2. A method for jointly estimating road adhesion coefficient and tire force according to claim 1, characterized in that: In step S1, the vehicle longitudinal dynamics estimation equation is: ； in, is the motor torque value obtained by the sensor, is the speed value obtained by the sensor, is the vehicle's yaw moment of inertia, is the true value of the longitudinal force of the vehicle, is the rolling radius of the car's wheels; According to the above formula, the longitudinal force PID estimator of a single wheel is obtained, and the state observer equation is as follows: ； In the above formula, is the estimation result of the PID state observer, and are the proportional and differential parameters in the PID observer, is the error between the estimated wheel speed and the true wheel speed, is the error between the estimated value and the true value of the longitudinal force of a single wheel; in, , , estimated angular acceleration of a single wheel Through the PID observer, we can get: ； In the above formula is the structural parameter of the integral link in the tire speed PID observer.

3. The method for jointly estimating road adhesion coefficient and tire force according to claim 1, characterized in that: In step S2, the steps of estimating the tire lateral force using the extended Kalman filter algorithm based on the vehicle longitudinal force, lateral acceleration, longitudinal acceleration, front wheel angle, and yaw rate are as follows: First, based on the dual-track three-degree-of-freedom model, the vehicle dynamics system is designed and the extended Kalman filter observer is used to estimate the wheel lateral force; the longitudinal, lateral and yaw motion equations of the dual-track three-degree-of-freedom vehicle model are: ； ； ； in, is the front wheel turning angle; is the vehicle's yaw moment of inertia; is the longitudinal force estimation result of the previous step, and is the distance between the center of mass and the front and rear axles, and is the track width of the front and rear axles, For car quality, is the longitudinal acceleration of the vehicle, is the lateral acceleration of the car, is the vehicle's yaw angular velocity; choose As the state variable of the tire lateral force observation system, As the observed variable, the three-degree-of-freedom yaw is transformed into the state space equation: ； ； Among them, the state transfer matrix ; and They represent the model uncertainty in the process equation and measurement equation in the state space system, and the state observation matrix in the measurement equation With the input matrix They are: ， 。 4. The method for jointly estimating road adhesion coefficient and tire force according to claim 1, characterized in that: In step S3, the vertical load calculation formula of the four vehicle tires is: ； ； ； ； Where, and They correspond to the vertical load of the car tire, is the height of the car's center of mass, It is the track width of the front and rear axles of the vehicle.

5. The method for jointly estimating road adhesion coefficient and tire force according to claim 1, characterized in that: In step S4, the Dugoff tire model is used as the calculation method for the tire normalized tire force, and its calculation formula is as follows: ； ； In the formula is the road adhesion coefficient, is the tire slip angle, is the tire slip rate, is the tire longitudinal stiffness, is the lateral stiffness of the tire. is the nonlinear error caused by tire slip, which is calculated as: ； ； Where, As an impact factor, is the longitudinal speed of the vehicle wheel; According to the formula, combined with the three-degree-of-freedom dynamics model of the vehicle, the quadratic Kalman filter algorithm is used to As a state variable, As measurement variables, the input is , and obtain the road adhesion coefficient.

6. The method for jointly estimating road adhesion coefficient and tire force according to claim 1, characterized in that: In step S5, the vehicle tire force angle is: ； In the formula is the tire longitudinal force, is the tire lateral force, is the vehicle tire force angle; According to the formula: ； in is the normalized lateral force correction estimate, is the vertical load on the tire, For the road adhesion coefficient, a PID observer is designed and the final normalized tire force is obtained as: ； Where: and is the PID parameter; The normalized tire force is used as the feedback value to form a joint parameter estimate.

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