Parameter estimation method, device, equipment, medium, program product and vehicle

Abstract

The invention relates to a parameter estimation method and device, equipment, a medium, a program product and a vehicle, and the method comprises the steps: resetting an estimator of a peak road adhesion coefficient under the condition that the slip rate change rate of a wheel meets a preset sudden change condition; and determining a peak road adhesion coefficient of the wheel based on the reset estimator. The invention aims to improve the accuracy of peak road adhesion coefficient estimation in a scene with sudden change of road conditions.

Description

Technical Field

[0001] This application relates to the field of parameter estimation, and in particular, to a parameter estimation method, device, equipment, medium, program product, and vehicle. Background Art

[0002] The purpose of the research on estimating the peak road adhesion coefficient is to effectively and accurately reveal the mechanical state and law between the road surface and the tire, appropriately express the functional relationship between various parameters, and be able to grasp the mechanical state of the vehicle in real time and control it safely and stably. Currently, a peak road adhesion coefficient estimator is used to estimate the peak road adhesion coefficient of the road surface. However, when facing 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. Summary of the Invention

[0003] Embodiments of this application provide a parameter estimation method, device, equipment, medium, program product, and vehicle, which improve the accuracy of estimating the peak road adhesion coefficient in the scenario of sudden changes in road conditions to at least partially solve the above technical problems.

[0004] To achieve the above objective, according to the first aspect of this application, a parameter estimation method is provided. The parameter estimation method includes:

[0005] Reset the estimator of the peak road adhesion coefficient when the change rate of the slip ratio of the wheel meets a preset mutation condition;

[0006] Determine the peak road adhesion coefficient of the wheel based on the reset estimator.

[0007] According to the second aspect of this application, a parameter estimation device is provided, including:

[0008] A reset module, configured to reset the estimator of the peak road adhesion coefficient when the change rate of the slip ratio of the wheel meets a preset mutation condition;

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

[0010] According to the third aspect of this application, an electronic device is further provided, including a processor, the processor is connected to a memory, the memory stores a computer program, and the processor is configured to run the computer program in the memory to execute any of the above parameter estimation methods.

[0011] According to the fourth aspect of this application, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements any of the above parameter estimation methods.

[0012] According to a fifth aspect of the present application, there is provided a computer program product, which includes a computer program, and the computer program is executed by a processor to implement any of the above parameter estimation methods.

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

[0014] In summary, in the embodiments of the present application, through the above technical solutions, when the change rate of the wheel slip ratio satisfies a preset mutation condition, the estimator of the peak road surface adhesion coefficient is reset; based on the reset estimator, the peak road surface adhesion coefficient of the wheel is determined. In this way, when the wheel slip ratio mutates and the road surface condition mutates, the estimator can be reset, thereby avoiding the difficulty in applying the data in the estimator after the mutation, and improving the accuracy of estimating the peak road surface adhesion coefficient in the scenario where the road surface condition mutates.

[0015] Other features and advantages of the present application will be described in detail in the subsequent specific implementation section. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and those skilled in the art can obtain other drawings without creative efforts based on these drawings.

[0017] In order to more fully understand the present application and its beneficial effects, the following description will be made in conjunction with the drawings, where the same reference numerals represent the same parts in the following description.

[0018] Figure 1 is a flowchart of an embodiment of the parameter estimation method provided in the embodiment of the present invention;

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

[0020] Figure 3 is a flowchart of the process for determining the utilization of the road surface adhesion coefficient provided in the embodiment of the present invention;

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

[0022] Figure 5 is a schematic structural diagram of the electronic device provided in the embodiment of the present invention. Detailed Description of the Embodiments

[0023] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the protection scope of the present application.

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

[0025] To solve the above problems, the embodiments of the present application propose a parameter estimation method, device, equipment, medium, program product and vehicle. In the embodiments of the present application, when the change rate of the wheel slip ratio satisfies a preset mutation condition, the estimator of the peak road surface adhesion coefficient is reset; based on the reset estimator, the peak road surface adhesion coefficient of the wheel is determined, which can improve the accuracy of the peak road surface adhesion coefficient estimation in the scenario of sudden change of road surface conditions.

[0026] Specifically, the parameter estimation method in the present application can be applied to vehicles, such as automobiles, electric vehicles, hybrid vehicles, etc. Subsequently, taking the execution subject of the parameter estimation method as a vehicle as an example, each embodiment will be described in detail.

[0027] The present application provides a parameter estimation method. Please refer to Figure 1 The parameter estimation method provided by the embodiments of the present application includes steps S10 - step S20, which will be introduced in detail below.

[0028] S10. When the change rate of the wheel slip ratio satisfies a preset mutation condition, reset the estimator of the peak road surface adhesion coefficient;

[0029] In this embodiment, there will be a slip ratio for the wheels in contact with the ground of the vehicle. The wheel slip ratio refers to the ratio of the sliding speed at the ground contact of the wheel to the moving speed of the wheel center, and 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 the sliding component in the movement of the wheel, and its magnitude has an important impact on the braking performance and driving stability of the vehicle. For the same wheel, its slip ratio is closely related to the adhesion performance brought by the road surface to the wheel. The change rate of the slip ratio can reflect the instantaneous change of the slip ratio. When it satisfies the preset mutation condition, it can also characterize that the slip ratio has a sudden change and the road surface condition has a sudden change.

[0030] The adhesion performance can be represented by the road surface adhesion coefficient. The larger the road surface adhesion coefficient, the stronger the adhesion force provided, and the better the braking performance and driving stability of the vehicle. For different types of road surfaces, the road surface adhesion coefficients are different. For example, the adhesion coefficient of a dry cement road surface is generally greater than that of an ice and snow road surface, which is the smallest.

[0031] The peak road surface adhesion coefficient is the maximum value that the road surface adhesion coefficient can reach under specific conditions. The estimator of the peak road surface adhesion coefficient is an algorithm used to estimate the peak road surface adhesion coefficient in real time. It can be integrated into a device to output the peak road surface adhesion coefficient. The algorithm adopted by the estimator is usually an iterative estimation based on the road surface related information at the previous moment. When the change rate of the wheel slip ratio satisfies a preset mutation condition, the wheel slip ratio mutates. The mutation of the slip ratio means that the relative motion state between the wheel and the road surface has changed rapidly, which may cause the original adhesion coefficient estimation model to fail. Because the relationship between the road surface adhesion coefficient and the slip ratio is non - linear. After the slip ratio mutates, if the estimator is not reset, it may lead to an increase in the estimation error and affect the performance of the vehicle control system. In this embodiment, when the change rate of the wheel slip ratio satisfies the preset mutation condition, the estimator will be reset, and the internal state, parameters or model of the estimator will be updated or re - initialized to ensure that it can more accurately reflect the road surface condition.

[0032] In some embodiments, the slip change rate is calculated by obtaining the estimation module requirement information through vehicle sensors and other estimators, including the wheel speed ω w and the longitudinal vehicle speed v x . Among them, the wheel speed ω w can be the real - time data detected by the wheel sensor, while the longitudinal vehicle speed v x is estimated by other estimators. According to the wheel speed ω w and the longitudinal vehicle speed v x of the vehicle, the longitudinal slip ratio s of the wheel is calculated, and the expression is as follows:

[0033]

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

[0035]

[0036] Among them, s k is the slip ratio at the k - th moment, s k-1 is the slip ratio at the (k - 1) - th moment, 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 value;

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

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

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

[0042]

[0043] where is the rate of change of the slip ratio at the (k - 1)-th moment, is the rate of change of the slip ratio at the k-th moment. When the above conditions are met, it is considered that a mutation has occurred in the slip ratio, a mutation has occurred in the road surface condition, and a mutation has also occurred in the road surface adhesion coefficient. Therefore, it is necessary to reset the estimator to improve the accuracy of determining the peak road surface adhesion coefficient.

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

[0045] In this embodiment, before the situation where the rate of change of the slip ratio of the wheel satisfies the preset mutation condition occurs, the vehicle can accurately determine the peak road surface adhesion coefficient of the wheel based on the estimator of the peak road surface adhesion coefficient. When the situation where the rate of change of the slip ratio of the wheel satisfies the preset mutation condition occurs, the estimator of the peak road surface adhesion coefficient can be reset, and continue to determine the peak road surface adhesion coefficient of the wheel 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 change rate of the slip ratio of the wheel satisfies a preset mutation condition, the estimator of the peak road surface adhesion coefficient is reset; and the peak road surface adhesion coefficient of the wheel is determined based on the reset estimator. In this way, when the slip ratio of the wheel mutates and the road surface condition mutates, the estimator can be reset. The reason for resetting after the road surface mutation is that for non-uniform road surfaces, such as in winter, there is ice adhesion on some roads, so the data accumulated on the paved road surface can no longer be applied to the estimation of the ice and snow road surface. Therefore, in this case, the accumulation of data will generate a large inertia, and the change of the scenario will cause the estimation to be distorted. Therefore, when the road surface mutation is recognized, the algorithm needs to be reset to reset and clear the data, so as to avoid the inapplicability of the data in the estimator after the mutation, and can improve the accuracy of estimating the peak road surface adhesion coefficient of the wheel, especially improve the accuracy of estimating the peak road surface adhesion coefficient in the scenario of road surface condition mutation.

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

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

[0049] In this embodiment, resetting the estimator of the peak road surface adhesion coefficient mainly includes resetting the initial value of the estimator. This initial value is the parameter used as the basis when the estimator performs iterative calculations, generally the parameter involved at the previous moment, and changes at different moments as the estimator calculates. When the slip ratio mutates, resetting the initial value of the estimator and no longer using the historical value can improve the accuracy of the estimator in calculating the peak road surface adhesion coefficient.

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

[0051] Determine the target data based on the initial value slip ratio, initial value gain coefficient, and initial value utilization road surface adhesion coefficient corresponding to the peak road surface adhesion coefficient;

[0052] Reset the initial value of the estimator of the peak road surface adhesion coefficient to the target data.

[0053] In this embodiment, the initial value can be reset based on the initial value slip ratio, initial value gain coefficient, and initial value utilization road surface adhesion coefficient corresponding to the peak road surface adhesion coefficient. The initial value slip ratio is represented by s tIt is shown that the initial slip ratio is the slip ratio corresponding to when the peak value is reached. However, since the specific characteristics of the road surface are not known in advance, the initial slip ratio can be set to a calibration value, generally taking 0.15. Similarly, the initial gain coefficient is represented by k, generally preset to 1 - 2, and the initial road surface adhesion coefficient utilized is represented by μ. Using the above initial slip ratio, initial slip ratio, and initial gain coefficient to determine the peak value of the estimator, determine the target data, and the form of the determined target data is:

[0054]

[0055] Based on the above form, a set of target data can be obtained. Reset the initial value of the estimator to this target data to complete the reset of the initial value when a mutation occurs. After the road surface mutation, the reset initial value can be used for estimation, thereby improving the estimation accuracy.

[0056] In one embodiment, the initial road surface adhesion coefficient utilized includes the road surface adhesion coefficient utilized when the slip ratio change rate satisfies a preset mutation condition.

[0057] In this embodiment, the initial road surface adhesion coefficient utilized can be the current road surface adhesion coefficient utilized, that is, the road surface adhesion coefficient utilized when the slip ratio change rate satisfies the preset mutation condition. This value is the same each time it is reset to cope with each road surface mutation.

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

[0059] In this embodiment, the recursive least squares algorithm is an adaptive filtering algorithm used to online estimate the parameters of a linear regression model. Through the recursive least squares algorithm, the peak road surface adhesion coefficient can be accurately estimated, and thus the peak road surface coefficient can be accurately estimated. The recursive least squares algorithm is a linear algorithm, and the estimator constructed based on the recursive least squares algorithm can make the calculation process efficient and have strong robustness.

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

[0061] Obtain the road surface adhesion coefficient utilized by the wheel;

[0062] Input the road surface adhesion coefficient utilized into the reset estimator to determine the peak road surface adhesion coefficient of the wheel.

[0063] In this embodiment, obtaining the road surface adhesion coefficient utilized by the wheel, the road surface adhesion coefficient utilized is the road surface adhesion coefficient that the wheel can currently utilize. Under specific conditions, this value will change with the change of the slip ratio s, and this change has a maximum value, which is also the peak road surface adhesion coefficient to be estimated in the embodiments of the present application.

[0064] Use the current road surface adhesion coefficient as the input of the estimator, and at the same time obtain the current slip ratio as the input of the estimator. Input the road surface adhesion coefficient and the corresponding slip ratio into 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] When the slip ratio of the wheel is less than the preset threshold, input the road surface adhesion coefficient into the reset estimator to obtain the peak road surface adhesion coefficient of the wheel.

[0067] In this embodiment, within the range of medium and small slip ratios, the change of the road surface adhesion coefficient and the change of the slip ratio are linearly related. A linear estimator can be constructed based on the linear relationship between the road surface adhesion coefficient and the slip ratio. When the slip ratio of the wheel is less than the preset threshold, inputting the road surface adhesion coefficient into the reset linear estimator can more accurately obtain the peak road surface adhesion coefficient of the wheel.

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

[0069] During the process of inputting the road surface adhesion coefficient into the reset estimator to obtain the peak road surface adhesion coefficient of the wheel, return to execute the step of resetting the estimator of the peak road surface adhesion coefficient when the change rate of the slip ratio of the wheel meets the preset mutation condition.

[0070] In this embodiment, during the process of inputting the road surface adhesion coefficient into the reset estimator to obtain the peak road surface adhesion coefficient of the wheel, that is, during the process of determining the peak road surface adhesion coefficient based on the estimator, if there is again a road surface mutation situation where the change rate of the slip ratio of the wheel meets the preset mutation condition, the estimator of the peak road surface adhesion coefficient can be reset again to enable real-time response to the road surface mutation situation and improve the stability of the estimator.

[0071] In this embodiment, when the slip ratio of the wheel is greater than or equal to the preset threshold, that is, in the case of a large range of slip ratios, at this time the road surface adhesion coefficient reaches the peak. Therefore, it can be considered that the peak road surface adhesion coefficient is equivalent to the road surface adhesion coefficient in use, and the road surface adhesion coefficient in use 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 2, longitudinally vehicle speed and wheel speed are obtained in real time, the real-time slip ratio of the wheel is calculated, and it is determined whether the slip ratio is greater than or equal to a preset threshold. When the slip ratio of the wheel is greater than or equal to the preset threshold, the wheel is in a large slip ratio state, and the current road surface adhesion coefficient can be output as the peak road surface adhesion coefficient.

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

[0074]

[0075] where μ(s) is the current road surface adhesion coefficient utilized, s is the current slip ratio, μ p is the peak road surface adhesion coefficient, and s p is the slip ratio corresponding to the peak road surface adhesion coefficient. Based on this formula, an estimator based on the recursive least squares algorithm is designed as the estimator of the peak road surface adhesion coefficient. Let a = 2μ p s p , then there is the following expression:

[0076]

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

[0078] y = A.x

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

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

[0081]

[0082] In the above recursive least squares algorithm, only two initial values of P 0 and x 0 need to be given to calculate continuously. When calculating, P 0 can be set as the identity matrix, and x 0 is the initial value of the recursive least squares algorithm, specifically including:

[0083]

[0084] Among them, k is the initial value gain coefficient, generally preset to 1-2, μ is the utilization road surface adhesion coefficient of the initial value, generally the utilization road surface adhesion coefficient at this moment, and s t is the initial slip ratio corresponding to the peak road surface adhesion coefficient, generally preset to 0.15. As the recursion progresses, the initial value x 0 will be updated and changed to x 1 、x 2 ...x k , and when the change rate of the wheel slip ratio satisfies the preset mutation condition, that is, when the road surface mutates, the initial value of the estimator can be reset, that is, the initial value of the algorithm is reset. Specifically, calculate the target data again using and reset the x k used in the recurrence formula to the target data, that is, x 0 . It should be noted that since u may be different, the currently calculated x 0 may also be different from the previous x 0 . In this way, the algorithm reset can be realized to cope with the occurrence of road surface mutation and ensure the accuracy of the algorithm.

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

[0086] Determine the utilization road surface adhesion coefficient of the wheel according to the tire force and vertical force of the wheel.

[0087] In this embodiment, the utilization road surface adhesion coefficient is calculated through the tire force and vertical force of the wheel. The available tire force divided by the vertical force of the tire is the utilization road surface adhesion coefficient. The calculation expression is as follows:

[0088]

[0089] Among them, F represents the tire force of the wheel, F z represents the vertical force of the wheel, and i represents the i-th wheel. Thus, the utilization road surface adhesion coefficient of the wheel can be calculated quickly and accurately.

[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. The expression of the resultant tire force is:

[0092]

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

[0094] In one embodiment, before determining the utilization road adhesion coefficient of the wheel according to the tire force and vertical force of the wheel, it further includes:

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

[0096] Perform linear filtering processing based on the wheel speed and the driving torque to obtain the longitudinal force of the wheel.

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

[0098] Specifically, referring to Figure 3 , the wheel speed ω of the vehicle wheel is monitored in real time through a wheel speed sensor and a motor torque sensor w and the driving torque T d . The moment of inertia of the wheel rotating around the wheel center is J w . When the hardware structure design of the whole vehicle is finalized, the moment of inertia of the wheel is a fixed physical quantity and can therefore be obtained by measurement. The distance from the wheel center of the wheel to the ground contact point is R, and the longitudinal force of the wheel is F x . Based on the longitudinal dynamics model, the dynamic equation for the rotation of this wheel around the wheel center can be obtained as:

[0099]

[0100] Based on this equation, a linear Kalman filter algorithm can be designed. The state variables of this linear Kalman filter algorithm are x = [ω w , F x T , the observation variable z = ω w , the input variable u = T d . Then the state space equation and the observation equation can be expressed as:

[0101]

[0102] Z = Hx + v

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

[0104] ​

[0105] H = [1, 0]

[0106] In the Kalman filter algorithm, the following time-discrete state equation and observation equation 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 moment; 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 moment; w(k) and v(k) are the process noise and measurement noise of the system, respectively.

[0110] Make a priori estimation and update the state error covariance matrix:

[0111]

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

[0113] Where, is the a priori state estimation value at time k; is the posterior state estimation value 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. P - (k) is the a priori 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 covariance matrix of the process noise. Since the process noise has been defined as a Gaussian white noise vector with a mean of 0, the covariance matrix Q of the process noise is a given matrix.

[0114] Then perform 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, calculate the posterior state estimation and update the posterior state estimation error covariance matrix:

[0118]

[0119] P(k) = (I - K(k)H(k))P - (k)

[0120] So far, the process of the linear Kalman filter algorithm is completed. The posterior state estimate value estimated by the Kalman filter 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 through the Kalman filter algorithm.

[0121] In one embodiment, before determining the utilization road adhesion coefficient of the wheel according to the tire force and vertical force of the wheel, it further includes:

[0122] Determine the lateral force of the wheel based on the longitudinal force of the wheel through a linear dynamics model.

[0123] In this embodiment, the lateral force of the wheel can be calculated through a linear dynamics model, and the linear dynamics model can be a vehicle lateral dynamics model, such as a 7DOFs vehicle dynamics model. The linear dynamics model requires the known longitudinal force of the wheel. Based on the longitudinal force of each wheel and the linear dynamics model, the lateral force of the wheel can be accurately obtained.

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

[0125] Obtain the lateral forces of the front and rear axles of the vehicle through the linear dynamics model based on the longitudinal force of the wheel, the yaw angular acceleration of the vehicle to which the wheel belongs, and the lateral acceleration of the vehicle;

[0126] Determine the lateral force of the wheel 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, through the linear dynamics model, based on the longitudinal forces of the vehicle's wheels, the yaw angular acceleration of the vehicle to which the wheel to be estimated belongs, and the lateral acceleration of the vehicle, first obtain the lateral forces of the front and rear axles of the vehicle, and then accurately determine the lateral force of the wheel based on the vertical force of the wheel, the lateral acceleration, and the lateral forces of the front and rear axles.

[0128] Specifically, referring to Figure 3 , collect the longitudinal acceleration a x , lateral acceleration a y and yaw angular velocity signal ω r of the vehicle through the inertial sensor IMU, and first calculate the yaw angular acceleration of the whole vehicle in real time through the difference method:

[0129]

[0130] Among them, ω r，k is the yaw angular velocity at the k-th moment, ω r，k-1 is the yaw angular velocity at the (k - 1)-th moment, Δt is the sampling time, and α r，k is the yaw angular acceleration at the k-th moment.

[0131] The vehicle lateral dynamics model is:

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

[0133]

[0134] Among them, m is the total vehicle mass, δ is the front wheel steering angle, I z is the yaw moment of inertia of the whole vehicle about the 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, t f is the front wheel track, t r is the rear wheel track, F yf is the total lateral force of the front axle, F yr is the total lateral force of the rear axle, F xf is the total longitudinal force of the front axle, F x_fl , F x_fr , F x_rl , F x_rr is the longitudinal force of the four wheels. Then, based on the known signals, the lateral forces F yf and F yr of the front and rear axles of the whole vehicle can be calculated.

[0135] To obtain the lateral force of each tire, it is necessary to distribute the lateral forces of the front and rear axles to the wheels according to the vertical force of the tire and the lateral acceleration of the whole vehicle.

[0136] Among them, the calculation formula for the vertical force of the wheel is as follows:

[0137]

[0138] Among them, g is the gravitational acceleration, l is the wheelbase, and h is the height of the center of mass. Then, the lateral forces of the four tires can be calculated as:

[0139]

[0140] Among them, λ1 and λ2 are preset distribution coefficients.

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

[0142] Based on the above embodiments, when determining the peak road surface adhesion coefficient, longitudinal force, and lateral force, a linear recursive least squares algorithm, a linear Kalman filter algorithm, and a linear dynamics model are respectively adopted, making the entire process more linear, computationally efficient, and robust. In order to handle the situation of sudden road surface changes, a criterion for sudden change in slip ratio is introduced. When the change rate of slip ratio undergoes a sudden change, the algorithm quickly resets the initial values to rapidly converge to the new road surface adhesion coefficient, improving the accuracy of the algorithm.

[0143] This embodiment also provides a parameter estimation device, which can be specifically integrated in a vehicle. For example, as Figure 4 shown, the parameter estimation device may include:

[0144] A reset module 1001, configured to reset the estimator of the peak road surface adhesion coefficient when the change rate of the slip ratio of the wheel satisfies a preset sudden change condition;

[0145] A determination module 1002, configured to determine the peak road surface adhesion coefficient of the wheel based on the reset estimator.

[0146] Optionally, the reset module 1001 is further configured to:

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

[0148] Optionally, the reset module 1001 is further configured to:

[0149] Determine target data based on the initial value slip ratio, initial value gain coefficient, and initial value utilization road surface adhesion coefficient corresponding to the peak road surface adhesion coefficient;

[0150] Reset the initial value of the estimator of the peak road surface adhesion coefficient to the target data.

[0151] Optionally, the initial value utilization road surface adhesion coefficient includes the road surface adhesion coefficient when the change rate of the slip ratio satisfies the preset sudden change condition.

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

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

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

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

[0156] Optionally, the determination module 1002 is further configured to:

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

[0158] The estimator after resetting the input of the utilized road surface adhesion coefficient determines the peak road surface adhesion coefficient of the wheel.

[0159] Optionally, the determining module 1002 is further configured to:

[0160] When the slip rate of the wheel is less than a preset threshold, input the utilized road surface adhesion coefficient and the slip rate of the wheel into the estimator after resetting, to obtain the peak road surface adhesion coefficient of the wheel.

[0161] Optionally, the determining module 1002 is further configured to:

[0162] During the process of inputting the utilized road surface adhesion coefficient into the estimator after resetting to obtain the peak road surface adhesion coefficient of the wheel, return to execute the step of resetting the estimator of the peak road surface adhesion coefficient when the change rate of the slip rate of the wheel meets a preset mutation condition.

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

[0164] Optionally, the determining module 1002 is further configured to: determine the utilized road surface adhesion coefficient of the wheel according to the tire force and the 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 the driving torque of the wheel;

[0167] Perform linear filtering processing based on the wheel speed and the driving torque to obtain the longitudinal force of the wheel.

[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 through a linear dynamics model.

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

[0170] Determine the lateral force of the wheel 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 change rate of the wheel slip ratio satisfies a preset mutation condition, the estimator of the peak road surface adhesion coefficient is reset; and the peak road surface adhesion coefficient of the wheel is determined based on the reset estimator. In this way, when the wheel slip ratio mutates and the road surface condition mutates, the estimator can be reset, thereby avoiding the inapplicability of the data in the estimator after the mutation and improving the accuracy of estimating the peak road surface adhesion coefficient of the wheel.

[0172] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated herein.

[0173] Correspondingly, an embodiment of the present application further provides an electronic device, as Figure 5 shown, Figure 5 is a schematic structural diagram of the electronic device provided by the embodiment of the present 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 in the memory 1102 and executable on the processor. Among them, the processor 1101 is electrically connected to the memory 1102. Those skilled in the art can understand that the structure of the electronic device shown in the figure does not constitute a limitation on the electronic device, and it may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0174] The processor 1101 is the control center of the electronic device 1100, connecting various parts of the entire electronic device 1100 through various interfaces and lines. By running or loading software programs and / or units stored in the memory 1102, and calling data stored in the memory 1102, the processor 1101 executes various functions of the electronic device 1100 and processes data, thereby monitoring the entire electronic device 1100. The processor 1101 may be a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc., and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application.

[0175] In the embodiment of the present application, the processor 1101 in the electronic device 1100 will, according to the following steps, load the instructions corresponding to the processes of one or more application programs into the memory 1102, and the processor 1101 will run the application programs stored in the memory 1102 to implement various functions, such as:

[0176] When the change rate of the wheel slip ratio satisfies a preset mutation condition, reset the estimator of the peak road surface adhesion coefficient;

[0177] Determine the peak road surface adhesion coefficient of the wheel based on the reset estimator.

[0178] For the specific implementation of each of the above operations, reference may be made to the previous embodiments, which will not be elaborated here.

[0179] Optionally, as Figure 5 shown, the electronic device 1100 further includes: a touch display screen 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. Among them, 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 respectively. Those skilled in the art can understand that Figure 5 the structure of the electronic device shown in

[0180] The touch display screen 1103 can be used to display a graphical user interface and receive operation instructions generated by a user's interaction with the graphical user interface. The touch display screen 1103 may include a display panel and a touch panel. Among them, 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, videos, and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. The touch panel can be used to collect touch operations of the user on or near it (such as operations of the user using a finger, a stylus, or any suitable object or accessory on or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute the corresponding program. Optionally, the touch panel can include two parts: a touch detection device and a touch controller. Among them, the touch detection device detects the touch position of the user and detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 1101, and can receive and execute commands sent by 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 it to the processor 1101 to determine the type of touch event. Subsequently, the processor 1101 provides a corresponding visual output on the display panel according to the type of touch event. In the embodiment of the present invention, the touch panel and the display panel can be integrated into the touch display screen 1103 to implement input and output functions. However, in some embodiments, the touch panel and the touch panel can be implemented as two independent components to implement input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to implement the input function.

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

[0182] The audio circuit 1105 can be used to provide an audio interface between the user and the electronic device through a speaker and a microphone. The audio circuit 1105 can transmit the electrical signal converted from the received audio data to the speaker, and the speaker converts it into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 1105 and then converted into audio data. After the audio data is output to the processor 1101 for processing, it is sent to another electronic device, for example, through the radio frequency circuit 1104, or the audio data is output to the memory 1102 for further processing. The audio circuit 1105 may also include an earphone jack to provide communication between the peripheral earphone and the electronic device.

[0183] The input unit 1106 can be used to receive input digital, character information or user characteristic information (such as fingerprint, iris, face information, etc.), and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function controls.

[0184] The power supply 1107 is used to supply power to each component of the electronic device 1100. Optionally, the power supply 1107 can be logically connected to the processor 1101 through a power management device, so as to realize functions such as management of charging, discharging, and power consumption management through the power management device. The power supply 1107 may also include any components such as one or more DC or AC power supplies, a recharge device, a power failure detection circuit, a power converter or inverter, and a power status indicator.

[0185] Although Figure 5 not shown in the figure, the electronic device 1100 may also include a camera, a sensor, a Wi-Fi module, a Bluetooth module, etc., which will not be elaborated here.

[0186] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0187] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0188] Therefore, an embodiment of the present application provides a computer-readable storage medium, in which multiple computer programs are stored. The computer programs can be loaded by a processor to execute any parameter estimation method provided by the embodiments of the present application. The computer programs can execute the following steps of the parameter estimation method:

[0189] Reset the estimator of the peak road adhesion coefficient when the slip rate change rate of the wheel meets the preset mutation condition;

[0190] Determine the peak road surface adhesion coefficient of the wheel based on the reset estimator.

[0191] For the specific implementation of each of the above operations, reference may be made to the previous embodiments, which will not be elaborated here.

[0192] Among them, the computer-readable storage medium may include: Read Only Memory (ROM), Random Access Memory (RAM), magnetic disk or optical disc, etc.

[0193] Since the computer program stored in the computer-readable storage medium can execute any parameter estimation method provided by the embodiments of the present application, the beneficial effects achieved by any parameter estimation method provided by the embodiments of the present application can be realized. For details, refer to the previous embodiments, which will not be elaborated here.

[0194] Optionally, an embodiment of the present application further provides a vehicle, which includes any one of the above parameter estimation devices, electronic devices, computer-readable storage media, and computer program products, and executes any one of the above methods.

[0195] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the present application, "a plurality" means two or more, unless otherwise specifically defined.

[0196] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For the parts not elaborated in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0197] The embodiments, implementation manners and related technical features of the present application can be combined and replaced with each other without conflict.

[0198] The above are only the preferred embodiments of the present application, and do not impose any form of limitation on the present application. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present application without departing from the content of the technical solution of the present application still fall within the scope of the technical solution of the present application.

Claims

1. A parameter estimation method, characterized in that: include: When the slip rate change rate of the wheel meets a preset mutation condition, resetting the estimator of the peak road adhesion coefficient; A peak road adhesion coefficient of the wheel is determined based on the reset estimator.

2. The parameter estimation method according to claim 1, characterized in that: The estimator for resetting the peak road adhesion coefficient includes: The initial value of the estimator of the peak road adhesion coefficient is reset.

3. The parameter estimation method according to claim 2, characterized in that: The resetting of the initial value of the estimator of the peak road adhesion coefficient comprises: Determine target data based on an initial slip ratio, an initial gain coefficient and an initial utilization road adhesion coefficient corresponding to a peak road adhesion coefficient; The initial value of the estimator of the peak road adhesion coefficient is reset to the target data.

4. The parameter estimation method according to claim 3, characterized in that: The initial value utilized road surface adhesion coefficient includes the utilized road surface adhesion coefficient when the slip rate change rate meets a preset mutation condition.

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

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

7. The parameter estimation method according to claim 1, characterized in that: The determining of the peak road adhesion coefficient of the wheel based on the reset estimator comprises: Obtaining the road adhesion coefficient of the wheel; The estimator reset using the road adhesion coefficient input determines a peak road adhesion coefficient of the wheel.

8. The parameter estimation method according to claim 7, characterized in that: The step of inputting the utilized road adhesion coefficient into the estimator to obtain the peak road adhesion coefficient of the wheel comprises: When the slip ratio of the wheel is less than a preset threshold, the road adhesion coefficient is input into the reset estimator to obtain the peak road adhesion coefficient of the wheel.

9. The parameter estimation method according to claim 8, characterized in that: The method further comprises: In the process of obtaining the peak road adhesion coefficient of the wheel by inputting the reset estimator using the road adhesion coefficient, the step of resetting the peak road adhesion coefficient estimator is returned to when the slip rate change rate of the wheel meets the preset mutation condition.

10. The parameter estimation method according to claim 8, characterized in that: The method further comprises: When the slip ratio of the wheel is greater than or equal to the preset threshold, the utilized road adhesion coefficient is determined as a peak road adhesion coefficient of the wheel.

11. The parameter estimation method according to claim 7, characterized in that: The obtaining of the road adhesion coefficient of the wheel includes: The utilized road adhesion coefficient of the wheel is determined according to the tire force and the vertical force of the wheel.

12. The parameter estimation method according to 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 according to claim 12, characterized in that: Before determining the utilized road adhesion coefficient of the wheel according to the tire force and the vertical force of the wheel, the method further includes: Obtaining the wheel speed and driving torque of the wheel; A linear filtering process is performed based on the wheel speed and the driving torque to obtain the longitudinal force of the wheel.

14. The parameter estimation method according to claim 12, characterized in that: Before determining the utilized road adhesion coefficient of the wheel according to the tire force and the 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 through a linear dynamic model.

15. The parameter estimation method according to claim 14, characterized in that: Determining the lateral force of the wheel based on the longitudinal force of the wheel by using a linear dynamic model includes: Obtaining the lateral forces of the front and rear axles of the vehicle through the linear dynamic model based on the longitudinal force of the wheel, the yaw angular 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 front and rear axle lateral forces.

16. A parameter estimation device, characterized in that: include: A reset module, used for resetting the estimator of the peak road adhesion coefficient when the slip rate change rate of the wheel meets a preset mutation condition; A determination module is used to determine a peak road adhesion coefficient of the wheel based on the reset estimator.

17. An electronic device, characterized in that: It includes a processor, the processor is connected to a memory, the memory stores a computer program, and the processor is used to run the computer program in the memory to execute 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, and when the computer program is executed by a processor, the parameter estimation method according to any one of claims 1 to 15 is implemented.

19. A computer program product, characterized in that The method comprises a computer program, wherein the computer program 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 executes the parameter estimation method as described in any one of claims 1 to 15, or includes the parameter estimation device as described in claim 16 or the electronic device as described in claim 17.

Images