Parameter estimation methods, devices, equipment, media, program products, and vehicles

Abstract

This application relates to a parameter estimation method, apparatus, device, medium, program product, and vehicle. In this method, when the rate of change of wheel slip ratio meets a preset abrupt change condition, the estimator for the peak road adhesion coefficient is reset; the peak road adhesion coefficient of the wheel is determined based on the reset estimator. This invention aims to improve the accuracy of peak road adhesion coefficient estimation in scenarios with abrupt changes in road conditions.

Description

Technical Field

[0001] This application relates to the field of parameter estimation, and more particularly to a parameter estimation method, apparatus, equipment, medium, program product, and vehicle. Background Technology

[0002] The purpose of peak road adhesion coefficient (BPCC) estimation research is to effectively and accurately reveal the mechanical state and laws between the road surface and the tire, appropriately describe the functional relationships between various parameters, and enable real-time monitoring of the vehicle's mechanical state for safe and stable control. Currently, BPCC estimators are used to estimate the peak road adhesion coefficient. However, the applicability of these estimators decreases when faced with changing road conditions, leading to a reduction in the accuracy of the BPCC output. Summary of the Invention

[0003] This application provides a parameter estimation method, apparatus, device, medium, program product, and vehicle to improve the accuracy of peak road adhesion coefficient estimation in scenarios with abrupt changes in road conditions, thereby at least partially solving the aforementioned technical problems.

[0004] To achieve the above objectives, according to a first aspect of this application, a parameter estimation method is provided, the parameter estimation method comprising:

[0005] When the rate of change of wheel slip ratio meets the preset abrupt change condition, reset the estimator of peak road adhesion coefficient;

[0006] The peak road adhesion coefficient of the wheel is determined based on the reset estimator.

[0007] According to a second aspect of this application, a parameter estimation apparatus is provided, comprising:

[0008] The reset module is used to reset the estimator of the peak road adhesion coefficient when the rate of change of wheel slip ratio meets the preset abrupt change condition.

[0009] A determination module is used to determine the peak road adhesion coefficient of the wheel based on the reset estimator.

[0010] According to a third aspect of this application, an electronic device is also provided, including a processor connected to a memory storing a computer program, the processor being configured to run the computer program in the memory to perform any of the parameter estimation methods described above.

[0011] According to a fourth aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, is any of the above-described parameter estimation methods.

[0012] According to a fifth aspect of this application, a computer program product is provided, comprising a computer program that is executed by a processor to implement any of the above-described parameter estimation methods.

[0013] According to a sixth aspect of this application, a vehicle is provided that performs the parameter estimation method described above, or includes the parameter estimation device or electronic device described above.

[0014] In summary, the embodiments of this application, through the above technical solution, reset the estimator of the peak road adhesion coefficient when the rate of change of the wheel slip ratio meets the preset abrupt change condition; and determine the peak road adhesion coefficient of the wheel based on the reset estimator. This allows the estimator to be reset when the wheel slip ratio or road condition changes abruptly, thereby avoiding the data in the estimator becoming inapplicable after the abrupt change, and improving the accuracy of peak road adhesion coefficient estimation in scenarios with abrupt changes in road conditions.

[0015] Other features and advantages of this application will be described in detail in the following detailed description section. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] To gain a more complete understanding of this application and its beneficial effects, the following description will be provided in conjunction with the accompanying drawings, wherein the same reference numerals in the following description denote the same parts.

[0018] Figure 1 This is a flowchart illustrating one embodiment of the parameter estimation method provided in this invention.

[0019] Figure 2 This is a schematic diagram of the process for determining the peak road surface adhesion coefficient provided in an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of the process for determining the road surface adhesion coefficient provided in an embodiment of the present invention;

[0021] Figure 4 This is a schematic diagram of the parameter estimation device provided in the embodiments of the present invention;

[0022] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the protection scope of this application.

[0024] Based on the problems mentioned in the background art, in related technologies, a peak road adhesion coefficient estimator is used to estimate the peak road adhesion coefficient of the road surface. However, when faced with changing road conditions, the applicability of the estimator will decrease, resulting in a decrease in the accuracy of the peak road adhesion coefficient output by the estimator.

[0025] To address the aforementioned issues, this application proposes a parameter estimation method, apparatus, device, medium, program product, and vehicle. In this application, when the rate of change of wheel slip ratio meets a preset abrupt change condition, the estimator for the peak road surface adhesion coefficient is reset; the peak road surface adhesion coefficient of the wheel is determined based on the reset estimator, which can improve the accuracy of peak road surface adhesion coefficient estimation in scenarios with abrupt changes in road conditions.

[0026] Specifically, the parameter estimation method in this application can be applied to vehicles, such as automobiles, electric vehicles, and hybrid vehicles. The following description uses a vehicle as an example to illustrate the various embodiments.

[0027] This application provides a parameter estimation method; please refer to [link / reference]. Figure 1 The parameter estimation method provided in this application includes steps S10-S20, which will be described in detail below.

[0028] S10. When the rate of change of wheel slip ratio meets the preset abrupt change condition, reset the estimator of peak road adhesion coefficient.

[0029] In this embodiment, the wheels of the vehicle in contact with the ground exhibit a slip ratio. The wheel slip ratio is the ratio of the sliding speed at the wheel's contact point to the speed of the wheel's center of motion; it can also be expressed as the ratio of the difference between the vehicle speed and the wheel speed to the vehicle speed. The slip ratio reflects the proportion of sliding components in the wheel's motion, and its magnitude has a significant impact on the vehicle's braking performance and driving stability. For the same wheel, its slip ratio is closely related to the adhesion provided by the road surface. The slip ratio change rate reflects the instantaneous change in the slip ratio; when it meets preset abrupt change conditions, it can also characterize a sudden change in the slip ratio and a sudden change in the road surface conditions.

[0030] Adhesion performance can be expressed by the road surface adhesion coefficient. The higher the road surface adhesion coefficient, the stronger the adhesion provided, and the better the vehicle's braking performance and driving stability. Different types of road surfaces have different road surface adhesion coefficients; for example, the adhesion coefficient of dry cement road surfaces is generally higher than that of icy and snowy road surfaces, which have the lowest adhesion coefficient.

[0031] Peak road adhesion coefficient (POC) is the maximum value that road adhesion coefficient can reach under specific conditions. A POC estimator is an algorithm used for real-time estimation of the POC, which can be integrated into the device to output the POC. The algorithm used by the estimator is typically an iterative estimation based on road surface information from the previous moment. When the rate of change of wheel slip ratio meets a preset abrupt change condition, the wheel slip ratio changes abruptly. This abrupt change in slip ratio means a rapid change in the relative motion state between the wheel and the road surface, which may cause the original adhesion coefficient estimation model to become invalid because the relationship between the road adhesion coefficient and slip ratio is non-linear. After a sudden change in slip ratio, if the estimator is not reset, the estimation error may increase, affecting the performance of the vehicle control system. In this embodiment, when the rate of change of wheel slip ratio meets the preset abrupt change condition, the estimator is reset, and its internal state, parameters, or model are updated or reinitialized to ensure that it can more accurately reflect the road surface conditions.

[0032] In some embodiments, the slip change rate is calculated, and estimation module requirement information, including wheel speed ω, is obtained through vehicle sensors and other estimators. w and the vehicle's longitudinal speed v x Among them, the wheel speed ω w It can be real-time data detected by wheel sensors, while the longitudinal vehicle speed v x It was estimated using other estimators. Based on the wheel speed ω w and the vehicle's longitudinal speed v x The longitudinal slip ratio s of the wheel is calculated using the following expression:

[0033]

[0034] Using the slip ratio at the current moment and the slip ratio at the previous moment, the rate of change of the slip ratio at the current moment can be calculated, as shown in the following expression:

[0035]

[0036] Among them, s k It is the slip ratio at time k, s k-1 Δt is the slip ratio at time k-1, and Δt is the sampling time.

[0037] In one embodiment, the preset mutation condition includes at least one of the following:

[0038] The rate of change of the slip ratio is greater than a preset threshold;

[0039] The rate of change of slip ratio is greater than a threshold determined based on the rate of change of slip ratio at the previous moment.

[0040] In this embodiment, when the rate of change of the wheel slip ratio is greater than a preset threshold and / or greater than a threshold determined based on the slip rate of change at the previous moment, the rate of change of the wheel slip ratio can be considered to meet the preset abrupt change condition. The threshold determined based on the slip rate of change at the previous moment can be a preset multiple of the slip rate of change at the previous moment.

[0041] In one example, the preset mutation condition expression is as follows:

[0042]

[0043] in It is the rate of change of slip ratio at time k-1. It is the rate of change of slip ratio at time k. When the above conditions are met, it is considered that the slip ratio has changed abruptly, the road surface condition has changed abruptly, and the road adhesion coefficient has also changed abruptly. Therefore, it is necessary to reset the estimator to improve the accuracy of determining the peak road adhesion coefficient.

[0044] S20. Determine the peak road adhesion coefficient of the wheel based on the reset estimator.

[0045] In this embodiment, before the rate of change of wheel slip ratio meets the preset abrupt change condition, the vehicle can accurately determine the peak road surface adhesion coefficient of the wheel based on the peak road surface adhesion coefficient estimator. When the rate of change of wheel slip ratio meets the preset abrupt change condition, the peak road surface adhesion coefficient estimator can be reset, and the peak road surface adhesion coefficient of the wheel can continue to be determined based on the reset estimator, so that the peak road surface adhesion coefficient of the wheel can still be accurately determined.

[0046] In the technical solution disclosed in this embodiment, when the rate of change of wheel slip ratio meets a preset abrupt change condition, the estimator for the peak road adhesion coefficient is reset; the peak road adhesion coefficient of the wheel is determined based on the reset estimator. This allows the estimator to be reset when the wheel slip ratio or road condition changes abruptly. The reason for resetting after a road condition change is that for non-uniform road surfaces, such as in winter when some roads have ice, the data accumulated on paved roads is no longer applicable to estimating icy or snowy roads. Therefore, data accumulation in such cases generates significant inertia, and changes in the scene can cause estimation distortion. Therefore, when a road condition change is detected, the algorithm needs to be reset to reset and clear the data, thereby preventing the data in the estimator from becoming unusable after the change. This improves the accuracy of estimating the peak road adhesion coefficient of the wheel, especially in scenarios with abrupt changes in road conditions.

[0047] In one embodiment, the estimator for resetting the peak road adhesion coefficient includes:

[0048] Reset the initial value of the estimator for the peak road adhesion coefficient.

[0049] In this embodiment, resetting the peak road adhesion coefficient estimator mainly includes resetting the estimator's initial value. This initial value is the basic parameter used by the estimator during iterative calculations, and is generally a parameter involved in the previous moment. As the estimator calculates, this value changes at different times. When the slip ratio changes abruptly, resetting the estimator's initial value and no longer using historical values ​​can improve the accuracy of the estimator in calculating the peak road adhesion coefficient.

[0050] In one embodiment, resetting the initial value of the estimator for the peak road adhesion coefficient includes:

[0051] Based on the initial slip ratio, initial gain coefficient, and initial target data determined using the road adhesion coefficient corresponding to the peak road adhesion coefficient;

[0052] The initial value of the peak road adhesion coefficient estimator is reset to the target data.

[0053] In this embodiment, the initial value can be based on the initial slip ratio corresponding to the peak road surface adhesion coefficient, the initial gain coefficient, and the initial value reset using the road surface adhesion coefficient. The initial slip ratio is denoted by s. tThe initial slip ratio is the slip ratio corresponding to the peak value. However, since the specific characteristics of the road surface are unknown beforehand, the initial slip ratio can be set to a calibration value, typically 0.15. Similarly, the initial gain coefficient is represented by k, usually preset to 1-2, and the initial value is represented by μ, which represents the road adhesion coefficient. Using the initial slip ratio, initial gain coefficient, and initial value, the peak value of the estimator is determined, and the target data is determined. The target data is in the following form:

[0054]

[0055] Based on the above, a set of target data can be obtained. The initial value of the estimator is then reset to this target data, completing the initial value reset for the sudden event. After a road surface incident, the reset initial value can be used for estimation, thereby improving the estimation accuracy.

[0056] In one embodiment, the initial value using the road surface adhesion coefficient includes the road surface adhesion coefficient when the slip rate change rate satisfies a preset abrupt change condition.

[0057] In this embodiment, the initial value of the road surface adhesion coefficient can be the current road surface adhesion coefficient, that is, the road surface adhesion coefficient when the slip rate change rate meets the preset sudden change condition. This value is the same every time it is reset, so as to cope with each sudden change in the road surface.

[0058] In one embodiment, the estimator for the peak road adhesion coefficient is constructed based on a recursive least squares algorithm.

[0059] In this embodiment, the recursive least squares algorithm is an adaptive filtering algorithm used for online estimation of parameters of a linear regression model. The peak road adhesion coefficient can be accurately estimated using the recursive least squares algorithm, thus accurately estimating the peak road coefficient. As a linear algorithm, the estimator built based on the recursive least squares algorithm makes the computation process efficient and robust.

[0060] In one embodiment, determining the peak road adhesion coefficient of the wheel based on the reset estimator includes:

[0061] Obtain the road surface adhesion coefficient of the wheel;

[0062] The peak road adhesion coefficient of the wheel is determined by the estimator after the road adhesion coefficient is input and reset.

[0063] In this embodiment, the road surface adhesion coefficient of the wheel is obtained. The road surface adhesion coefficient is the road surface adhesion coefficient that the wheel can currently utilize. Under certain conditions, this value will change with the slip ratio s. This change has a maximum value, which is also the peak road surface adhesion coefficient that needs to be estimated in this embodiment.

[0064] Using the current road surface adhesion coefficient as input to the estimator, and also using the current slip ratio as input to the estimator, the road surface adhesion coefficient and the corresponding slip ratio are input to the reset estimator to obtain the reset peak road surface adhesion coefficient.

[0065] In one embodiment, the step of inputting the road surface adhesion coefficient into the estimator to obtain the peak road surface adhesion coefficient of the wheel includes:

[0066] If the slip ratio of the wheel is less than a preset threshold, the peak road adhesion coefficient of the wheel is obtained by inputting the road adhesion coefficient into the reset estimator.

[0067] In this embodiment, within the small to medium slip ratio range, the change in road surface adhesion coefficient is linearly related to the change in slip ratio. Based on this linear relationship, a linear estimator can be constructed. When the wheel slip ratio is less than a preset threshold, the peak road surface adhesion coefficient of the wheel can be obtained more accurately by using the road surface adhesion coefficient as input to the reset linear estimator.

[0068] In this embodiment, the method further includes:

[0069] In the process of obtaining the peak road surface adhesion coefficient of the wheel by inputting the road surface adhesion coefficient into the reset estimator, the process returns to the step of resetting the estimator of the peak road surface adhesion coefficient when the rate of change of the wheel slip ratio meets the preset abrupt change condition.

[0070] In this embodiment, during the process of obtaining the peak road surface adhesion coefficient of the wheel by inputting the road surface adhesion coefficient into the reset estimator, that is, during the process of determining the peak road surface adhesion coefficient based on the estimator, if a road surface change occurs again where the rate of change of wheel slip rate meets the preset change condition, the estimator of the peak road surface adhesion coefficient can be reset again, so as to respond to the road surface change in real time and improve the stability of the estimator.

[0071] In this embodiment, when the wheel slip ratio is greater than or equal to a preset threshold, that is, when the slip ratio is within a large range, the road surface adhesion coefficient reaches its peak value. Therefore, the peak road surface adhesion coefficient can be considered to be equivalent to the road surface adhesion coefficient. The road surface adhesion coefficient at the current moment can be determined as the peak road surface adhesion coefficient of the wheel, thereby improving the efficiency of determining the peak road surface adhesion coefficient.

[0072] In one example, refer to Figure 2It can acquire longitudinal vehicle speed and wheel speed in real time, calculate the real-time slip ratio of the wheel, and determine whether the slip ratio is greater than or equal to a preset threshold. If the wheel slip ratio is greater than or equal to the preset threshold, the wheel is in a high slip ratio state, and the current road surface adhesion coefficient can be output as the peak road surface adhesion coefficient.

[0073] When the wheel slip ratio is less than a preset threshold, the wheel is in a low to medium slip ratio state. The peak road adhesion coefficient can be determined using an estimator for the peak road adhesion coefficient and the current road adhesion coefficient. In the estimator, the linear relationship between the road adhesion coefficient and the peak road adhesion coefficient can be represented by the following μ-s curve formula:

[0074]

[0075] Where μ(s) is the current road surface adhesion coefficient, s is the current slip ratio, and μ p It is the peak road adhesion coefficient, s p The slip ratio corresponding to the peak road adhesion coefficient. Based on this formula, an estimator based on the recursive least squares algorithm is designed as the estimator for the peak road adhesion coefficient, assuming a = 2μ. p s p , Then we have the following expression:

[0076]

[0077] Let y = μ(s)s 2 A = [s - μ(s)] The above formula can then be expressed as:

[0078] y = Ax

[0079] Where y = μ(s)s 2 Both A = [s - μ(s)] have been obtained according to the steps above.

[0080] In the estimator, the peak road adhesion coefficient can be estimated using the recursive least squares algorithm based on the above expression. The recursive formula for the recursive least squares algorithm is as follows:

[0081]

[0082] In the above recursive least squares algorithm, only two initial values, P0 and x0, are needed to continue the calculation. During the calculation, P0 can be set as the identity matrix, and x0 is the initial value of the recursive least squares algorithm, specifically including:

[0083]

[0084] Where k is the initial gain coefficient, typically preset to 1-2, μ is the initial road surface adhesion coefficient, typically the road surface adhesion coefficient at that moment, and s t This is the initial slip ratio corresponding to the peak road adhesion coefficient, typically preset to 0.15. As the iteration proceeds, the initial value x0 will be updated to x1, x2...x k However, if the rate of change of wheel slip meets the preset abrupt change condition, that is, if a sudden change occurs in the road surface, the initial value of the estimator can be reset, that is, the initial value of the algorithm can be reset. Specifically, the algorithm is then re-evaluated. Calculate the target data, using x in the recursive formula k The algorithm is reset to the target data, i.e., x0. It should be noted that since u may vary, the currently calculated x0 may also be different from the previous x0. This algorithm reset can cope with sudden changes in road surface conditions and ensure the accuracy of the algorithm.

[0085] In one embodiment, obtaining the road surface adhesion coefficient of the wheel includes:

[0086] The coefficient of friction of the wheel is determined based on the tire force and vertical force of the wheel.

[0087] In this embodiment, the road adhesion coefficient is calculated using the tire force and vertical force of the wheel. The usable tire force divided by the tire's vertical force is the usable road adhesion coefficient, and the calculation expression is as follows:

[0088]

[0089] Where F represents the tire force of the wheel, F z Let represent the vertical force of the wheel, and 'i' represent the i-th wheel. This allows for the rapid and accurate calculation of the wheel's road adhesion coefficient.

[0090] In one embodiment, the tire force includes the resultant force of the longitudinal force and the lateral force of the wheel.

[0091] In this embodiment, the tire force of the wheel is the resultant force of the longitudinal force and the lateral force of the wheel, and the expression for the resultant tire force is:

[0092]

[0093] Among them, F x It is the longitudinal force of the wheel, F z It is the lateral force of the wheel.

[0094] In one embodiment, before determining the coefficient of friction of the wheel based on the tire force and vertical force of the wheel, the method further includes:

[0095] Obtain the wheel speed and driving torque of the wheel;

[0096] The longitudinal force of the wheel is obtained by performing linear filtering based on the wheel speed and the driving torque.

[0097] In this embodiment, the longitudinal force of the wheel is obtained through linear filtering. This linear filtering can be based on a linear Kalman filter algorithm, where the state variables of the linear Kalman filter algorithm include the longitudinal force of the wheel, the observation variables include the wheel speed, and the input variable can be the driving torque provided by the motor to the wheel. Thus, based on the real-time wheel speed and driving torque, linear filtering can be performed to output an accurate longitudinal force of the wheel in real time.

[0098] Specifically, refer to Figure 3 The wheel speed ω of the vehicle is monitored in real time using wheel speed sensors and motor torque sensors. w and driving torque T d The moment of inertia of the wheel about its center is J. w Once the overall vehicle hardware structure design is finalized, the moment of inertia of the wheels becomes a fixed physical quantity and can therefore be measured. The distance from the wheel center to the contact point is R, and the longitudinal force on the wheel is F. x Based on the longitudinal dynamic model, the dynamic equation for the rotation of the wheel around its center can be obtained as follows:

[0099]

[0100] Based on this equation, a linear Kalman filter algorithm can be designed, where the state variable x = [ω] w ,F x ] T The observed variable z = ω w Input variable u = T d Therefore, the state-space equation and the observation equation can be expressed as:

[0101]

[0102] Z = Hx + v

[0103] Where w and v are both Gaussian white noise vectors with a mean of 0, the state transition matrix A, the control matrix B, and the observation matrix H are represented as follows:

[0104]

[0105] H = [1, 0]

[0106] In the Kalman filter algorithm, the following time-discrete state equations and observation equations can be obtained:

[0107] x(k)=Ax(k-1)+Bu(k-1)+w(k-1)

[0108] z(k)=Hx(k)+v(k)

[0109] Where x(k) is the state vector at the next time step; x(k-1) is the state variable; u(k-1) is the input variable; z(k) is the output vector of the state observation system at the next time step; w(k) and v(k) are the process noise and measurement noise of the system, respectively.

[0110] Make prior estimates and update the state error covariance matrix:

[0111]

[0112] P - (k)=AP(k-1)A T +Q

[0113] in, Let k be the prior state estimate at time k; P represents the posterior state estimate at time k-1; u(k-1) is the system input vector at time k-1, and w(k-1) is the process noise vector at time k-1. - (k) is the prior state estimation error covariance matrix at time k, P(k-1) is the posterior state estimation error covariance matrix at time k-1, and Q is the process noise covariance matrix. Since the process noise has been defined as a Gaussian white noise vector with a mean of 0, the process noise covariance matrix Q is a given matrix.

[0114] Then perform a gain update:

[0115] K(k)=P - (k)H(k) T (H(k)P - (k)H(k) T +R) -1

[0116] Where K(k) is the Kalman gain, and R is the covariance matrix of the observation noise, which is also a given value.

[0117] Finally, the posterior state estimate is calculated and the posterior state estimate error covariance matrix is ​​updated:

[0118]

[0119] P(k)=(IK(k)H(k))P - (k)

[0120] The linear Kalman filter algorithm process is now complete. The posterior state estimate obtained through Kalman filtering is the output value of the algorithm, which includes the longitudinal force of the tire. Therefore, the longitudinal force of the wheel can be estimated based on the driving torque and wheel speed of the wheel using the Kalman filter algorithm.

[0121] In one embodiment, before determining the coefficient of friction of the wheel based on the tire force and vertical force of the wheel, the method further includes:

[0122] The lateral force of the wheel is determined based on the longitudinal force of the wheel using a linear dynamics model.

[0123] In this embodiment, the lateral force of the wheel can be calculated using a linear dynamics model, which can be a vehicle lateral dynamics model, such as a 7DOF (7-degree-of-freedom) vehicle dynamics model. The linear dynamics model requires known longitudinal forces of the wheel. Based on the longitudinal forces of each wheel and the linear dynamics model, the lateral force of the wheel can be accurately obtained.

[0124] In one embodiment, determining the lateral force of the wheel based on the longitudinal force of the wheel using a linear dynamics model includes:

[0125] Using the linear dynamics model, the lateral forces of the front and rear axles of the vehicle are obtained based on the longitudinal force of the wheel, the yaw acceleration of the vehicle to which the wheel belongs, and the lateral acceleration of the vehicle.

[0126] The lateral force of the wheel is determined based on the vertical force of the wheel, the lateral acceleration, and the lateral forces of the front and rear axles.

[0127] In this embodiment, the linear dynamics model is used to first obtain the lateral forces of the front and rear axles of the vehicle based on the longitudinal forces of each wheel, the yaw acceleration of the vehicle to which the wheel to be estimated belongs, and the lateral acceleration of the vehicle. Then, based on the vertical forces, lateral acceleration, and lateral forces of the wheels, the lateral forces of the wheels are accurately determined.

[0128] Specifically, refer to Figure 3 The longitudinal acceleration a of the vehicle is collected by an inertial measurement unit (IMU). x lateral acceleration a y With the yaw rate signal ω r First, the yaw acceleration of the entire vehicle is calculated in real time using the difference method:

[0129]

[0130] Where, ω r，k It is the yaw rate at time k, ω r，k-1 It is the yaw rate at time k-1, Δt is the sampling time, and α is the yaw rate.r，k It is the yaw acceleration at time k.

[0131] The vehicle's lateral dynamics model is as follows:

[0132] ma y =F yf cosδ+F yr +F xf sinδ

[0133]

[0134] Where m is the total vehicle mass, δ is the front wheel steering angle, and I z t is the yaw moment of inertia of the entire vehicle about its center of mass, a is the distance from the center of mass to the front axle, b is the distance from the center of mass to the rear axle, and t is the yaw moment of inertia. f It is the front wheel track, t r It refers to the rear wheel track, F. yf It is the total lateral force on the front axle, F yr It is the total lateral force on the rear axle, F xf It is the total longitudinal force on the front axle, F x_fl F x_fr F x_rl F x_rr This refers to the longitudinal force on all four wheels. Therefore, based on the known signals, the lateral force F on the front and rear axles of the entire vehicle can be calculated. yf and F yr .

[0135] To obtain the lateral force of each tire, the front and rear axle lateral forces need to be distributed to the wheels based on the vertical force of the tire and the lateral acceleration of the whole vehicle.

[0136] The formula for calculating the vertical force on the wheel is as follows:

[0137]

[0138] Where g is the acceleration due to gravity, l is the wheelbase, and h is the height of the center of mass. Therefore, the lateral forces on the four tires can be calculated as follows:

[0139]

[0140] Wherein, λ1 and λ2 are preset allocation coefficients.

[0141] Finally, the road adhesion coefficient of the wheel can be calculated using the longitudinal, lateral, and vertical forces of the wheel.

[0142] Based on the above embodiments, linear recursive least squares algorithm, linear Kalman filter algorithm, and linear dynamic model are used to determine the peak road adhesion coefficient, longitudinal force, and lateral force, respectively. This makes the whole process more linear, computationally efficient, and robust. In order to deal with sudden changes in road surface conditions, a criterion for sudden changes in slip ratio is introduced. When the slip ratio change rate changes abruptly, the algorithm quickly resets the initial value to converge to the new road adhesion coefficient, thereby improving the accuracy of the algorithm.

[0143] This embodiment also provides a parameter estimation device, which can be integrated into a vehicle, for example, Figure 4 As shown, the parameter estimation device may include:

[0144] The reset module 1001 is used to reset the estimator of the peak road adhesion coefficient when the rate of change of wheel slip ratio meets the preset abrupt change condition.

[0145] The determination module 1002 is used to determine the peak road adhesion coefficient of the wheel based on the reset estimator.

[0146] Optionally, the reset module 1001 is also used for:

[0147] Reset the initial value of the estimator for the peak road adhesion coefficient.

[0148] Optionally, the reset module 1001 is also used for:

[0149] Based on the initial slip ratio, initial gain coefficient, and initial target data determined using the road adhesion coefficient corresponding to the peak road adhesion coefficient;

[0150] The initial value of the peak road adhesion coefficient estimator is reset to the target data.

[0151] Optionally, the initial value using the road surface adhesion coefficient includes the road surface adhesion coefficient when the slip rate change rate satisfies the preset abrupt change condition.

[0152] Optionally, the estimator for the peak road adhesion coefficient includes a recursive least squares algorithm.

[0153] Optionally, the preset mutation condition includes at least one of the following:

[0154] The rate of change of the slip ratio is greater than a preset threshold;

[0155] The rate of change of slip ratio is greater than a threshold determined based on the rate of change of slip ratio at the previous moment.

[0156] Optionally, the determining module 1002 is also used for:

[0157] Obtain the road surface adhesion coefficient of the wheel;

[0158] The peak road adhesion coefficient of the wheel is determined by the estimator after the road adhesion coefficient is input and reset.

[0159] Optionally, the determining module 1002 is also used for:

[0160] If the slip ratio of the wheel is less than a preset threshold, the peak road surface adhesion coefficient of the wheel is obtained by inputting the road surface adhesion coefficient and the slip ratio of the wheel into the reset estimator.

[0161] Optionally, the determining module 1002 is also used for:

[0162] In the process of obtaining the peak road surface adhesion coefficient of the wheel by inputting the road surface adhesion coefficient into the reset estimator, the process returns to the step of resetting the estimator of the peak road surface adhesion coefficient when the rate of change of the wheel slip ratio meets the preset abrupt change condition.

[0163] Optionally, the determining module 1002 is further configured to: determine the road surface adhesion coefficient as the peak road surface adhesion coefficient of the wheel when the slip ratio of the wheel is greater than or equal to the preset threshold.

[0164] Optionally, the determining module 1002 is further configured to: determine the road surface adhesion coefficient of the wheel based on the tire force and vertical force of the wheel.

[0165] Optionally, the tire force includes the resultant force of the longitudinal force and the lateral force of the wheel.

[0166] Optionally, the determining module 1002 is further configured to: obtain the wheel speed and driving torque of the wheel;

[0167] The longitudinal force of the wheel is obtained by performing linear filtering based on the wheel speed and the driving torque.

[0168] Optionally, the determining module 1002 is further configured to: determine the lateral force of the wheel based on the longitudinal force of the wheel using a linear dynamics model.

[0169] Optionally, the determining module 1002 is further configured to: obtain the lateral forces of the front and rear axles of the vehicle based on the longitudinal force of the wheel, the yaw acceleration of the vehicle to which the wheel belongs, and the lateral acceleration of the vehicle through the linear dynamics model;

[0170] The lateral force of the wheel is determined based on the vertical force of the wheel, the lateral acceleration, and the lateral forces of the front and rear axles.

[0171] In this embodiment, when the rate of change of wheel slip ratio meets a preset abrupt change condition, the estimator for the peak road adhesion coefficient is reset; the peak road adhesion coefficient of the wheel is determined based on the reset estimator. This allows the estimator to be reset when the wheel slip ratio or road condition changes abruptly, thus avoiding the data in the estimator becoming unusable after the abrupt change and improving the accuracy of estimating the peak road adhesion coefficient of the wheel.

[0172] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0173] Accordingly, embodiments of this application also provide an electronic device, such as... Figure 5 As shown, Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 1100 further includes a processor 1101 with one or more processing cores, a memory 1102 with one or more computer-readable storage media, and a computer program stored on the memory 1102 and executable on the processor. The processor 1101 and the memory 1102 are electrically connected. Those skilled in the art will understand that the electronic device structure shown in the figure does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0174] The processor 1101 is the control center of the electronic device 1100. It connects various parts of the electronic device 1100 via various interfaces and lines. By running or loading software programs and / or units stored in the memory 1102, and by calling data stored in the memory 1102, it executes various functions and processes data of the electronic device 1100, thereby providing overall monitoring of the electronic device 1100. The processor 1101 can be a processor (Central Processing Unit, CPU), a graphics processing unit (GPU), a network processor (NP), etc., and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0175] In this embodiment, the processor 1101 in the electronic device 1100 loads the instructions corresponding to the processes of one or more applications into the memory 1102 according to the following steps, and the processor 1101 runs the applications stored in the memory 1102 to realize various functions, such as:

[0176] When the rate of change of wheel slip ratio meets the preset abrupt change condition, reset the estimator of peak road adhesion coefficient;

[0177] The peak road adhesion coefficient of the wheel is determined based on the reset estimator.

[0178] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0179] Optional, such as Figure 5 As shown, the electronic device 1100 also includes: a touch display screen 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. The processor 1101 is electrically connected to the touch display screen 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107. Those skilled in the art will understand that... Figure 5 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0180] The touch display screen 1103 can be used to display a graphical user interface (GUI) and receive operation commands generated by the user interacting with the GUI. The touch display screen 1103 may include a display panel and a touch panel. The display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the electronic device. These graphical user interfaces can be composed of graphics, text, icons, video, and any combination thereof. Optionally, the display panel can be configured using a liquid crystal display (LCD), organic light-emitting diode (OLED), or other similar technologies. The touch panel can be used to collect touch operations performed by the user on or near it (such as operations performed by the user using a finger, stylus, or any suitable object or accessory on or near the touch panel), generate corresponding operation commands, and execute the corresponding program according to the operation commands. Optionally, the touch panel may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch location and the signal generated by the touch operation, transmitting the signal to the touch controller. The touch controller receives touch information from the touch detection device, converts it into touch point coordinates, and sends it to the processor 1101. It can also receive and execute commands from the processor 1101. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it transmits the information to the processor 1101 to determine the type of touch event. Subsequently, the processor 1101 provides corresponding visual output on the display panel based on the type of touch event. In this embodiment, the touch panel and the display panel can be integrated into the touch display screen 1103 to achieve input and output functions. However, in some embodiments, the touch panel and the touch display screen 1103 can be used as two independent components to achieve input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to achieve input functions.

[0181] The radio frequency circuit 1104 can be used to transmit and receive radio frequency signals to establish wireless communication with networked medical devices or other electronic devices, and to transmit and receive signals with networked medical devices or other electronic devices.

[0182] Audio circuit 1105 can be used to provide an audio interface between a user and an electronic device via a speaker and a microphone. Audio circuit 1105 can convert received audio data into electrical signals and transmit them to the speaker, where the speaker converts them into sound signals for output. Conversely, the microphone converts the collected sound signals into electrical signals, which are then received by audio circuit 1105, converted back into audio data, and then processed by processor 1101 before being transmitted via radio frequency circuit 1104 to, for example, another electronic device, or output to memory 1102 for further processing. Audio circuit 1105 may also include an earphone jack to provide communication between peripheral headphones and electronic devices.

[0183] The input unit 1106 can be used to receive input numbers, characters, or user characteristic information (such as fingerprints, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function control.

[0184] Power supply 1107 is used to supply power to various components of electronic device 1100. Optionally, power supply 1107 can be logically connected to processor 1101 through a power management device, thereby enabling functions such as charging, discharging, and power consumption management through the power management device. Power supply 1107 may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0185] although Figure 5 As not shown in the diagram, the electronic device 1100 may also include a camera, sensor, wireless fidelity module, Bluetooth module, etc., which will not be described in detail here.

[0186] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0187] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0188] Therefore, embodiments of this application provide a computer-readable storage medium storing multiple computer programs. These computer programs can be loaded by a processor to execute any of the parameter estimation methods provided in this application. The computer program can perform the following steps of the parameter estimation method:

[0189] When the rate of change of wheel slip ratio meets the preset abrupt change condition, reset the estimator of peak road adhesion coefficient;

[0190] The peak road adhesion coefficient of the wheel is determined based on the reset estimator.

[0191] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0192] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0193] Since the computer-readable storage medium contains a computer program that can implement any of the parameter estimation methods provided in the embodiments of this application, and can execute any of the parameter estimation methods provided in the embodiments of this application, the effects are detailed in the preceding embodiments and will not be repeated here.

[0194] Optionally, embodiments of this application also provide a vehicle that includes any of the above-described parameter estimation devices, electronic devices, computer-readable storage media, and computer program products, and executes any of the above-described methods.

[0195] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0196] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0197] The embodiments, implementation methods, and related technical features of this application can be combined and substituted for each other without conflict.

[0198] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A parameter estimation method, characterized in that, include: When the rate of change of wheel slip ratio meets the preset abrupt change condition, a road surface abrupt change is identified, and the estimator of peak road surface adhesion coefficient is reset; wherein, the estimator of resetting peak road surface adhesion coefficient includes: updating or re-initializing at least one of the state, parameters or model inside the estimator; The peak road adhesion coefficient of the wheel is determined based on the reset estimator.

2. The parameter estimation method as described in claim 1, characterized in that, The estimator for resetting the peak road adhesion coefficient includes: Reset the initial value of the estimator for the peak road adhesion coefficient.

3. The parameter estimation method as described in claim 2, characterized in that, The initial value of resetting the estimator for the peak road adhesion coefficient includes: Based on the initial slip ratio, initial gain coefficient, and initial target data determined using the road adhesion coefficient corresponding to the peak road adhesion coefficient; The initial value of the peak road adhesion coefficient estimator is reset to the target data.

4. The parameter estimation method as described in claim 3, characterized in that, The initial value using the road surface adhesion coefficient includes the road surface adhesion coefficient when the slip rate change rate meets the preset abrupt change condition.

5. The method according to any one of claims 1-4, characterized in that, The estimator for the peak road surface adhesion coefficient is constructed based on a recursive least squares algorithm.

6. The parameter estimation method as described in claim 1, characterized in that, The preset mutation conditions include at least one of the following: The rate of change of the slip ratio is greater than a preset threshold; The rate of change of slip ratio is greater than a threshold determined based on the rate of change of slip ratio at the previous moment.

7. The parameter estimation method as described in claim 1, characterized in that, The determination of the peak road adhesion coefficient of the wheel based on the reset estimator includes: Obtain the road surface adhesion coefficient of the wheel; The peak road adhesion coefficient of the wheel is determined by the estimator after the road adhesion coefficient is input and reset.

8. The parameter estimation method as described in claim 7, characterized in that, The step of inputting the road surface adhesion coefficient into the estimator to obtain the peak road surface adhesion coefficient of the wheel includes: If the slip ratio of the wheel is less than a preset threshold, the peak road adhesion coefficient of the wheel is obtained by inputting the road adhesion coefficient into the reset estimator.

9. The parameter estimation method as described in claim 8, characterized in that, The method further includes: In the process of obtaining the peak road surface adhesion coefficient of the wheel by inputting the road surface adhesion coefficient into the reset estimator, the process returns to the step of resetting the estimator of the peak road surface adhesion coefficient when the rate of change of the wheel slip ratio meets the preset abrupt change condition.

10. The parameter estimation method as described in claim 8, characterized in that, The method further includes: If the slip ratio of the wheel is greater than or equal to the preset threshold, the road surface adhesion coefficient is determined as the peak road surface adhesion coefficient of the wheel.

11. The parameter estimation method as described in claim 7, characterized in that, The process of obtaining the road surface adhesion coefficient of the wheel includes: The coefficient of friction of the wheel is determined based on the tire force and vertical force of the wheel.

12. The parameter estimation method as described in claim 11, characterized in that, The tire force includes the resultant force of the longitudinal force and the lateral force of the wheel.

13. The parameter estimation method as described in claim 12, characterized in that, Before determining the coefficient of friction of the wheel based on the tire force and vertical force of the wheel, the method further includes: Obtain the wheel speed and driving torque of the wheel; The longitudinal force of the wheel is obtained by performing linear filtering based on the wheel speed and the driving torque.

14. The parameter estimation method as described in claim 12, characterized in that, Before determining the coefficient of friction of the wheel based on the tire force and vertical force of the wheel, the method further includes: The lateral force of the wheel is determined based on the longitudinal force of the wheel using a linear dynamics model.

15. The parameter estimation method as described in claim 14, characterized in that, The determination of the lateral force of the wheel based on the longitudinal force of the wheel using a linear dynamics model includes: Using the linear dynamics model, the lateral forces of the front and rear axles of the vehicle are obtained based on the longitudinal force of the wheel, the yaw acceleration of the vehicle to which the wheel belongs, and the lateral acceleration of the vehicle. The lateral force of the wheel is determined based on the vertical force of the wheel, the lateral acceleration, and the lateral forces of the front and rear axles.

16. A parameter estimation device, characterized in that, include: A reset module is used to determine and identify a road surface mutation when the rate of change of wheel slip ratio meets a preset mutation condition, and reset the estimator of peak road surface adhesion coefficient; wherein, the estimator for resetting peak road surface adhesion coefficient includes: updating or reinitializing at least one of the state, parameters or model inside the estimator; A determination module is used to determine the peak road adhesion coefficient of the wheel based on the reset estimator.

17. An electronic device, characterized in that, The device includes a processor connected to a memory storing a computer program, the processor being configured to run the computer program in the memory to perform the parameter estimation method according to any one of claims 1 to 15.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the parameter estimation method according to any one of claims 1 to 15.

19. A computer program product, characterized in that, It includes a computer program that is executed by a processor to implement the parameter estimation method according to any one of claims 1 to 15.

20. A vehicle, characterized in that, The vehicle performs the parameter estimation method as described in any one of claims 1-15, or includes the parameter estimation device as described in claim 16 or the electronic device as described in claim 17.

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