Methods, devices, vehicles, and storage media for estimating road surface adhesion coefficient

Abstract

This application provides a method, apparatus, vehicle, and storage medium for estimating the road surface adhesion coefficient. When the vehicle is in motion, this method fuses the dynamic road surface adhesion coefficient and the kinematic road surface adhesion coefficient. This reflects the characteristics of the road surface from multiple perspectives, reducing errors when estimating the road surface adhesion coefficient from a single factor. Furthermore, a first road surface adhesion coefficient, a fourth road surface adhesion coefficient estimated by acceleration, and the wheel-based adhesion coefficient are fused to determine the target road surface adhesion coefficient. In this way, the method comprehensively considers road surface adhesion coefficients from different sources, effectively correlates and fuses multiple road surface adhesion coefficients, and obtains a road surface adhesion coefficient that better reflects the overall adhesion characteristics of the road surface, thereby improving the estimation accuracy of the road surface adhesion coefficient under different driving conditions and achieving precise control of the vehicle's motion.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to methods, apparatus, vehicles, and storage media for estimating road surface adhesion coefficients in the field of vehicle technology. Background Technology

[0002] With the continuous advancement of automotive technology and the improvement of people's living standards, the target audience for vehicles is becoming increasingly broad. However, this also brings about a growing number of vehicle-related problems, including the need for precise control over the movement of vehicles.

[0003] The coefficient of adhesion (COP) is a key parameter for precise control of the motion process. However, current techniques primarily rely on continuous estimation of the COP using tire dynamics models. This estimation is highly dependent on the tire dynamics model, and since tire characteristics are affected by factors such as temperature and tire pressure during use, the accuracy of the COP estimation is impacted.

[0004] Therefore, there is an urgent need for a method to estimate the road surface adhesion coefficient in order to improve the accuracy of the estimation and achieve precise control of the vehicle's motion process. Summary of the Invention

[0005] This application provides a method, apparatus, vehicle, and storage medium for estimating the road surface adhesion coefficient. The method can improve the estimation accuracy of the road surface adhesion coefficient and achieve precise control of the vehicle's motion process.

[0006] In a first aspect, a method for estimating the road surface adhesion coefficient is provided. The method includes: determining a first road surface adhesion coefficient for the road surface on which the vehicle is traveling, wherein the first road surface adhesion coefficient is obtained by fusing a second road surface adhesion coefficient and a third road surface adhesion coefficient, wherein the second road surface adhesion coefficient is determined based on the tire force in the vehicle and the third road surface adhesion coefficient is determined based on the wheel slip ratio in the vehicle; fusing the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the wheel utilization adhesion coefficient to determine a target road surface adhesion coefficient for the road surface, wherein the fourth road surface adhesion coefficient is determined based on the vehicle acceleration, and the utilization adhesion coefficient is used to indicate the effective utilization rate of the actual friction force between the tire of the wheel and the road surface.

[0007] In the above technical solution, when the vehicle is in motion, the second and third road surface adhesion coefficients are fused to obtain the first road surface adhesion coefficient of the driving surface. That is, the dynamic and kinematic road surface adhesion coefficients are fused, which reflects the characteristics of the driving surface from multiple perspectives and reduces errors when estimating the road surface adhesion coefficient from a single factor. Furthermore, the first road surface adhesion coefficient, the fourth road surface adhesion coefficient estimated by acceleration, and the wheel-based adhesion coefficient are fused to determine the target road surface adhesion coefficient. In this way, the method comprehensively considers road surface adhesion coefficients from different sources, effectively correlates and fuses multiple road surface adhesion coefficients, and obtains a road surface adhesion coefficient that better reflects the overall adhesion characteristics of the driving surface, thereby improving the estimation accuracy of the road surface adhesion coefficient under different driving conditions and achieving precise control of the vehicle's motion process.

[0008] In conjunction with the first aspect, in some possible implementations, the method for determining the third road surface adhesion coefficient includes: taking any wheel in the vehicle as the target wheel, determining the longitudinal slip ratio and lateral slip ratio of the target wheel; determining the road surface composite slip ratio of the target wheel based on the longitudinal slip ratio and the target lateral slip ratio, wherein the target lateral slip ratio is obtained by multiplying the lateral slip ratio by a first coefficient, the first coefficient being used to indicate the degree of influence of the lateral slip ratio on the tire friction performance of the target wheel; determining the road surface adhesion coefficient corresponding to the target wheel based on the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient; and determining the third road surface adhesion coefficient based on the road surface adhesion coefficients corresponding to each wheel in the vehicle.

[0009] In the above technical solution, any wheel in the vehicle is taken as the target wheel. The road surface composite slip ratio of the target wheel is determined by its longitudinal slip ratio and the lateral slip ratio (target lateral slip ratio) considering the friction ellipse. This determines the degree of influence of the lateral slip ratio on the tire friction performance of the target wheel, more accurately reflecting the distribution of friction between the tire and the road surface during actual driving, and more precisely determining the road surface composite slip ratio. Furthermore, based on the road surface composite slip ratio, longitudinal slip ratio, lateral slip ratio, and a first coefficient, the road surface adhesion coefficient corresponding to the target wheel is determined. This road surface composite slip ratio describes the combined longitudinal and lateral slip of the target wheel, helping to more comprehensively evaluate the actual slip state of the target wheel on the road surface. The lateral slip ratio and longitudinal slip ratio reflect the wheel's slip from different directions; combining these factors allows for a more accurate analysis of the friction characteristics between the tire and the road surface. In addition, there is friction between the road surface and each wheel. Based on the road surface adhesion coefficient corresponding to each wheel in the vehicle, a third road surface adhesion coefficient is determined, which can more accurately determine the kinematic road surface adhesion coefficient of the road surface.

[0010] In combination with the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the road surface adhesion coefficient corresponding to the target wheel is determined based on the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio and the first coefficient, including: determining a fifth road surface adhesion coefficient based on the road surface composite slip ratio; and determining the road surface adhesion coefficient corresponding to the target wheel based on the fifth road surface adhesion coefficient, the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio and the first coefficient.

[0011] In the above technical solution, determining the fifth road adhesion coefficient based on the road surface composite slip ratio of the target wheel allows for a more accurate assessment of the tire's friction characteristics under the road surface composite slip ratio while driving on the road surface. By comprehensively considering the lateral slip ratio and longitudinal slip ratio, the actual friction performance of the tire under multi-directional force conditions can be assessed more accurately. Furthermore, introducing the first coefficient further refines the description of the tire's friction performance. Therefore, in this method, the road surface adhesion coefficient corresponding to the target wheel when the vehicle is driving can be accurately determined based on the fifth road surface adhesion coefficient, road surface composite slip ratio, longitudinal slip ratio, lateral slip ratio, and the first coefficient.

[0012] In combination with the first aspect and the above implementation methods, in some possible implementation methods, determining the first road surface adhesion coefficient of the vehicle's driving surface includes: determining the difference between the second road surface adhesion coefficient and the third road surface adhesion coefficient at the current iteration number as the sixth road surface adhesion coefficient at the current iteration number; determining the first product between the sixth road surface adhesion coefficient and the Kalman gain at the current iteration number; and determining the first road surface adhesion coefficient based on the first product and the road surface adhesion coefficient estimated a priori at the current iteration number.

[0013] In the above technical solution, the dynamic road surface adhesion coefficient (second road surface adhesion coefficient) involves the tire force of the wheels, while the kinematic road surface adhesion coefficient (third road surface adhesion coefficient) is related to the vehicle's motion state. By fusing the second and third road surface adhesion coefficients, the characteristics of the driving road surface can be reflected from multiple different perspectives, reducing the error when determining the road surface adhesion coefficient based on a single factor. Furthermore, the Kalman gain of the current iteration number can play a weighting role in the fusion process. Since the Kalman gain is related to the lateral and longitudinal road surface adhesion coefficients corresponding to each wheel, the weights of the second and third road surface adhesion coefficients in the fusion can be reasonably allocated based on the actual adhesion conditions of different wheels, making the first road surface adhesion coefficient closer to the true value. In addition, a priori estimated road surface adhesion coefficient is introduced during the fusion process. This takes into account previous judgments of road conditions, which can correct the current estimated value of the road surface adhesion coefficient to a certain extent. Especially when the vehicle's driving state does not change drastically, the prior estimated road surface adhesion coefficient can provide a stable reference, further improving the estimation accuracy of the first road surface adhesion coefficient.

[0014] Combining the first aspect and the above implementation methods, in some possible implementation methods, determining the first product between the sixth road surface adhesion coefficient and the Kalman gain of the current iteration number, and determining the first road surface adhesion coefficient based on the first product, includes: determining the sum of the road surface composite slip ratios of multiple wheels in the vehicle as the total slip ratio; determining an excitation mode based on the total slip ratio, the excitation mode being used to determine the method of updating the Kalman gain during the iterative determination of the first road surface adhesion coefficient through the extended Kalman filter algorithm; updating the Kalman gain of the current iteration number based on the excitation mode, and determining the updated Kalman gain; determining the first product between the sixth road surface adhesion coefficient and the updated Kalman gain, and determining the first road surface adhesion coefficient based on the first product.

[0015] In the above technical solution, the excitation mode is determined based on the sum of the combined road surface slip ratios (total slip ratio) of multiple wheels. This excitation mode is used to determine how to update the Kalman gain during the iterative determination of the first road surface adhesion coefficient using the Extended Kalman Filter (EKF) algorithm. Furthermore, based on this excitation mode, the Kalman gain is updated for the current iteration number. This method uses the EKF algorithm to determine the first road surface adhesion coefficient using a target number of iterations and updates the Kalman gain, which accelerates the fusion of the dynamic and kinematic road surface adhesion coefficients, allowing the method to determine the first road surface adhesion coefficient with fewer iterations. Therefore, this method can fuse the first road surface adhesion coefficient at a faster speed.

[0016] In conjunction with the first aspect and the above-described implementation methods, in some possible implementation methods, the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the wheel's utilization adhesion coefficient are fused to determine the target road surface adhesion coefficient, including any one of the following: determining the maximum adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient as the target road surface adhesion coefficient; determining the average adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient as the target road surface adhesion coefficient; weighting the first road surface adhesion coefficient using a first weight to obtain a first coefficient; weighting the fourth road surface adhesion coefficient using a second weight to obtain a second coefficient; weighting the utilization adhesion coefficient using a third weight to obtain a third coefficient; and determining the sum of the first coefficient, the second coefficient, and the third coefficient as the target road surface adhesion coefficient; wherein the first weight is used to indicate the contribution of the first road surface adhesion coefficient in determining the target road surface adhesion coefficient, the second weight is used to indicate the contribution of the fourth road surface adhesion coefficient in determining the target road surface adhesion coefficient, and the third weight is used to indicate the contribution of the utilization adhesion coefficient in determining the target road surface adhesion coefficient.

[0017] In the above technical solutions, determining the target road surface adhesion coefficient through multiple methods avoids situations where the target road surface adhesion coefficient cannot be determined and also meets different needs when determining the target road surface adhesion coefficient. Furthermore, the target road surface adhesion coefficient determined by the first method is the maximum road surface adhesion coefficient, ensuring that the vehicle can drive in the safest manner under various driving conditions. The average adhesion coefficient determined by the second method can balance the advantages and disadvantages of road surface adhesion coefficients from different sources, obtaining a road surface adhesion coefficient that better reflects the overall adhesion characteristics of the driving road surface. Compared to the second method, the weighted fusion adhesion coefficient of the third method is more accurate. This is because the third method can consider the different contributions of road surface adhesion coefficients from different sources in determining the target road surface adhesion coefficient under the current driving conditions.

[0018] In conjunction with the first aspect and the above-described implementation, in some possible implementations, the method for determining the first weight, the second weight, and the third weight includes: determining the wear degree of the tires of the wheels in the vehicle; if the wear degree is less than or equal to a preset degree, determining the first weight, the second weight, and the third weight based on a first ratio between a first preset value and the total number corresponding to the weight, wherein the first weight, the second weight, and the third weight are the same; if the wear degree is greater than the preset degree, determining the second weight based on the first ratio and the deviation of the wear degree from the preset degree; and determining the first weight and the third weight based on the first preset value and the second weight.

[0019] In the above technical solution, tire wear affects the accuracy of road adhesion coefficients from different sources. Specifically, tire wear has the least impact on the accuracy of the fourth road adhesion coefficient, followed by the wheel's utilization adhesion coefficient, and lastly the first road adhesion coefficient. This is because tire wear has little impact on the acceleration during the determination of the fourth road adhesion coefficient, but it does affect the tire force during the determination of the utilization adhesion coefficient, and even more so the tire force and slip ratio during the determination of the first road adhesion coefficient. Therefore, based on the above theory, this method can accurately assess the contribution of road adhesion coefficients from different sources—namely, the first weight, the second weight, and the third weight—in determining the target road adhesion coefficient by assessing tire wear.

[0020] Secondly, an apparatus for estimating the road surface adhesion coefficient is provided. The apparatus includes: a determining module for determining a first road surface adhesion coefficient of the vehicle's driving surface when the vehicle is in motion, the first road surface adhesion coefficient being obtained by fusing a second road surface adhesion coefficient and a third road surface adhesion coefficient, the second road surface adhesion coefficient being determined based on the tire force in the vehicle, and the third road surface adhesion coefficient being determined based on the wheel slip ratio in the vehicle; and a fusing module for fusing the first road surface adhesion coefficient, a fourth road surface adhesion coefficient, and the wheel's utilization adhesion coefficient to determine a target road surface adhesion coefficient of the driving surface, the fourth road surface adhesion coefficient being determined based on the vehicle's acceleration, and the utilization adhesion coefficient being used to indicate the effective utilization rate of the actual friction force between the tire of the wheel and the driving surface.

[0021] In conjunction with the second aspect, in some possible implementations, the determining module is specifically configured to: take any wheel in the vehicle as the target wheel, determine the longitudinal slip ratio and lateral slip ratio of the target wheel; based on the longitudinal slip ratio and the target lateral slip ratio, determine the road surface composite slip ratio of the target wheel, the target lateral slip ratio being obtained by multiplying the lateral slip ratio by a first coefficient, the first coefficient being used to indicate the degree of influence of the lateral slip ratio on the tire friction performance of the target wheel; based on the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient, determine the road surface adhesion coefficient corresponding to the target wheel; and based on the road surface adhesion coefficients corresponding to each wheel in the vehicle, determine the third road surface adhesion coefficient.

[0022] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is specifically used to: determine the fifth road surface adhesion coefficient based on the road surface composite slip ratio; and determine the road surface adhesion coefficient corresponding to the target wheel based on the fifth road surface adhesion coefficient, the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient.

[0023] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is further configured to: determine the difference between the second road surface adhesion coefficient and the third road surface adhesion coefficient at the current iteration number as the sixth road surface adhesion coefficient at the current iteration number; determine the first product between the sixth road surface adhesion coefficient and the Kalman gain at the current iteration number, and determine the first road surface adhesion coefficient based on the first product.

[0024] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is further specifically used for: determining the sum of the road surface composite slip ratios of multiple wheels in the vehicle as the total slip ratio; determining an excitation mode based on the total slip ratio, the excitation mode being used to determine the method of updating the Kalman gain during the iterative determination of the first road surface adhesion coefficient through the extended Kalman filter algorithm; updating the Kalman gain for the current iteration number based on the excitation mode, and determining the updated Kalman gain; determining the first product between the sixth road surface adhesion coefficient and the updated Kalman gain, and determining the first road surface adhesion coefficient based on the first product and the prior estimated road surface adhesion coefficient for the current iteration number.

[0025] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the fusion module is specifically used for any of the following: determining the maximum adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient as the target road surface adhesion coefficient; determining the average adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient as the target road surface adhesion coefficient; weighting the first road surface adhesion coefficient with a first weight to obtain a first coefficient; weighting the fourth road surface adhesion coefficient with a second weight to obtain a second coefficient; weighting the utilization adhesion coefficient with a third weight to obtain a third coefficient; and determining the sum of the first coefficient, the second coefficient, and the third coefficient as the target road surface adhesion coefficient; wherein the first weight is used to indicate the contribution of the first road surface adhesion coefficient in determining the target road surface adhesion coefficient, the second weight is used to indicate the contribution of the fourth road surface adhesion coefficient in determining the target road surface adhesion coefficient, and the third weight is used to indicate the contribution of the utilization adhesion coefficient in determining the target road surface adhesion coefficient.

[0026] In conjunction with the second aspect and the above implementation methods, in some possible implementation methods, the determining module is further specifically used for: determining the wear degree of the tires of the wheels in the vehicle; when the wear degree is less than or equal to a preset degree, determining the first weight, the second weight, and the third weight based on a first ratio between a first preset value and the total number corresponding to the weights, wherein the first weight, the second weight, and the third weight are the same; when the wear degree is greater than the preset degree, determining the second weight based on the first ratio and the deviation of the wear degree from the preset degree; and determining the first weight and the third weight based on the first preset value and the second weight.

[0027] Thirdly, a vehicle is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, causing the vehicle to perform the methods described in the first aspect or any possible implementation thereof.

[0028] Fourthly, a computer-readable storage medium is provided that stores executable program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of a scenario where a vehicle is used, provided in an embodiment of this application;

[0030] Figure 2 This is a schematic flowchart illustrating a method for estimating the road surface adhesion coefficient provided in an embodiment of this application;

[0031] Figure 3 This is a schematic block diagram illustrating an estimation of the road surface adhesion coefficient provided in an embodiment of this application;

[0032] Figure 4 This is a schematic flowchart illustrating an embodiment of the present application for estimating the road surface adhesion coefficient;

[0033] Figure 5 This is a schematic diagram of the structure of a device for estimating the road surface adhesion coefficient provided in an embodiment of this application;

[0034] Figure 6 This is a structural schematic diagram of a vehicle provided in an embodiment of this application. Detailed Implementation

[0035] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0036] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0037] Figure 1 This is a schematic diagram of a scenario where a vehicle is used, as provided in an embodiment of this application.

[0038] It should be understood that the coefficient of friction is a key parameter for achieving precise control of vehicle motion. For example, by using the coefficient of friction, vehicle control systems can more accurately determine the safe distance a vehicle must maintain during operation, thereby issuing timely warnings in potentially dangerous situations and improving driving safety. For instance, as shown... Figure 1 As shown, if the safe distance is determined by an inaccurate road adhesion coefficient, it will increase the probability of vehicle A causing a traffic accident when driving at a safe distance.

[0039] In related technologies, the road adhesion coefficient is mainly predicted continuously through tire dynamics models. The road adhesion coefficient determined by this technology is highly dependent on the tire dynamics model. Since the tire characteristics of the tire dynamics model change during use due to factors such as temperature and tire pressure, this affects the accuracy of the road adhesion coefficient estimation.

[0040] To address the aforementioned problems, this application proposes a method for estimating the road surface adhesion coefficient, thereby improving the accuracy of the estimation and enabling precise control of the vehicle's motion. Specific implementation steps can be found in [reference needed]. Figure 2 .

[0041] Figure 2 This is a schematic flowchart illustrating a method for estimating the road surface adhesion coefficient provided in an embodiment of this application.

[0042] It should be understood that the method for estimating the road surface adhesion coefficient provided in this application embodiment can be applied to, for example, Figure 1 The vehicle controller in the vehicle shown.

[0043] For example, such as Figure 2As shown, the method 200 includes:

[0044] Step 201: When the vehicle is in motion, the vehicle controller determines the first road surface adhesion coefficient of the vehicle's driving surface. The first road surface adhesion coefficient is obtained by fusing the second road surface adhesion coefficient and the third road surface adhesion coefficient. The second road surface adhesion coefficient is determined based on the tire force in the vehicle, and the third road surface adhesion coefficient is determined based on the wheel slip ratio in the vehicle.

[0045] It should be understood that in step 201 above, the "second road surface adhesion coefficient" can be considered as the dynamic road surface adhesion coefficient of the driving road surface. The dynamic road surface adhesion coefficient is the road surface adhesion coefficient estimated through the vehicle's dynamic model, which involves the tire forces in the vehicle. The dynamic model estimation method can more accurately reflect the dynamic response of the vehicle under different road surface conditions. The "third road surface adhesion coefficient" can be considered as the kinematic road surface adhesion coefficient of the driving road surface. The kinematic road surface adhesion coefficient is the road surface adhesion coefficient estimated through the vehicle's kinematic model (involving slip ratio, etc.). This method infers the road surface adhesion coefficient based on the vehicle's motion state.

[0046] In some embodiments, the method for determining the second road surface adhesion coefficient in step 201 includes: the vehicle controller taking any wheel in the vehicle as the target wheel, determining the lateral utilization adhesion coefficient of the target wheel based on the lateral tire force and vertical tire force of the target wheel, and determining the longitudinal utilization adhesion coefficient of the target wheel based on the longitudinal tire force and vertical tire force of the target wheel; the vehicle controller determining the second road surface adhesion coefficient based on the lateral utilization adhesion coefficient and longitudinal utilization adhesion coefficient of each wheel in the vehicle.

[0047] In some embodiments, the vehicle controller determines the lateral utilization adhesion coefficient of the target wheel based on the lateral tire force and vertical tire force of the target wheel, and determines the longitudinal utilization adhesion coefficient of the target wheel based on the longitudinal tire force and vertical tire force of the target wheel, including: the vehicle controller determines the lateral utilization adhesion coefficient and the longitudinal utilization adhesion coefficient of the target wheel based on the following formulas (1) and (2).

[0048]

[0049] Where, μ xi Let μ be the longitudinal adhesion coefficient of the target wheel i. yi Let F be the lateral adhesion coefficient of the target wheel i. xi F is the longitudinal tire force of the target wheel i. zi F is the vertical tire force of the target wheel i. yi Let i be the lateral tire force of the target wheel i.

[0050] In some embodiments, the vehicle controller determines the second road surface adhesion coefficient based on the lateral and longitudinal adhesion coefficients of each wheel in the vehicle, including: the vehicle determines the second road surface adhesion coefficient based on the following formula (3);

[0051] μ2=[μ x1 ,μ x2 ,μ xi ,...,μ y1 ,μ y2 ,μ yi ,...] T (3)

[0052] Where μ2 is the adhesion coefficient of the second road surface, μ x1 Let μ be the longitudinal utilization adhesion coefficient of the first wheel in the vehicle. y1 μ is the lateral adhesion coefficient of the first wheel. x2 Let μ be the longitudinal utilization adhesion coefficient of the second wheel in the vehicle. y2 The lateral adhesion coefficient of the second wheel is μ. xi Let μ be the longitudinal utilization adhesion coefficient of the i-th wheel in the vehicle, i.e., the longitudinal utilization adhesion coefficient of wheel i. yi Let be the lateral utilization adhesion coefficient of the i-th wheel, i.e., the lateral utilization adhesion coefficient of wheel i. It should be understood that the second road surface adhesion coefficient is determined jointly by the lateral utilization adhesion coefficients and longitudinal utilization adhesion coefficients of all wheels in the vehicle. Here, the maximum value corresponding to i is the total number of wheels N in the vehicle.

[0053] In some embodiments, when the total number of wheels in the vehicle is 4, the value of i is 4, and the second road adhesion coefficient μ2 = [μ x1 ,μ x2 ,μ x3 ,μ x4 ,μ y1 ,μ y2 ,μ y3 ,μ y4 ] T .

[0054] In one possible implementation, the method for determining the third road surface adhesion coefficient in step 201 includes: the vehicle controller taking any wheel in the vehicle as the target wheel and determining the longitudinal slip ratio and lateral slip ratio of the target wheel; the vehicle controller determining the road surface composite slip ratio of the target wheel based on the longitudinal slip ratio and the target lateral slip ratio, wherein the target lateral slip ratio is obtained by multiplying the lateral slip ratio by a first coefficient, the first coefficient being used to indicate the degree of influence of the lateral slip ratio on the tire friction performance of the target wheel; the vehicle controller determining the road surface adhesion coefficient corresponding to the target wheel based on the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient; and the vehicle controller determining the third road surface adhesion coefficient based on the road surface adhesion coefficients corresponding to each wheel in the vehicle.

[0055] It should be understood that during vehicle operation, tires are subjected to both longitudinal friction (such as during acceleration or braking) and lateral friction (such as during cornering). There is a limit to the friction between the tire and the road surface. The resultant force between longitudinal and lateral friction cannot exceed the friction limit represented by the ellipse (friction ellipse). The "first coefficient" in the above scheme is a proportionality coefficient added before the lateral slip ratio after considering the friction ellipse. This friction ellipse indicates the mutual constraint between longitudinal and lateral friction; the longitudinal slip ratio and lateral slip ratio cannot be simply combined directly to obtain the composite slip ratio. By considering the influence of the friction ellipse, this method can more accurately reflect the distribution of friction between the tire and the road surface during actual vehicle operation, thus more precisely determining the road surface composite slip ratio.

[0056] In the above technical solution, any wheel in the vehicle is taken as the target wheel. The vehicle controller determines the road surface composite slip ratio of the target wheel by using its longitudinal slip ratio and lateral slip ratio after considering the friction ellipse. This can determine the degree of influence of the lateral slip ratio on the tire friction performance of the target wheel, and can more accurately reflect the distribution of friction force between the tire and the road surface during actual driving, thus more accurately determining the road surface composite slip ratio. Furthermore, the vehicle controller determines the road surface adhesion coefficient corresponding to the target wheel based on the road surface composite slip ratio, longitudinal slip ratio, lateral slip ratio, and a first coefficient. This road surface composite slip ratio is used to describe the combined longitudinal and lateral slip of the target wheel, helping to more comprehensively evaluate the actual slip state of the target wheel on the road surface. The lateral slip ratio and longitudinal slip ratio reflect the wheel slip from different directions; combining these factors allows for a more accurate analysis of the friction characteristics between the tire and the road surface. In addition, there is friction between the road surface and each wheel. The vehicle controller determines the third road surface adhesion coefficient based on the road surface adhesion coefficient corresponding to each wheel in the vehicle, which can more accurately determine the kinematic road surface adhesion coefficient of the road surface.

[0057] In some embodiments, the vehicle controller determines the lateral slip ratio and longitudinal slip ratio of the target wheel, including: the vehicle controller determines the longitudinal slip ratio and lateral slip ratio of the target wheel based on the following formulas (4) and (5);

[0058]

[0059] Among them, S xi S represents the longitudinal slip ratio of target wheel i, used to indicate the degree of slippage of target wheel i in the rolling direction. yi ω represents the lateral slip ratio of target wheel i, used to indicate the degree of slippage of target wheel i in the lateral direction. The rolling direction is perpendicular to the lateral direction. wi Let r be the rotational angular velocity of the target wheel i. ei Let v be the rolling radius of the target wheel i. wxi v is the longitudinal velocity at the center of target wheel i, used to indicate the velocity component of the center of target wheel i along the vehicle's forward direction. wyi The lateral velocity of the center of the target wheel i is used to indicate the velocity component of the center of the target wheel i along the lateral direction of the vehicle, with the forward direction perpendicular to the lateral direction.

[0060] In some embodiments, the vehicle controller determines the road surface composite slip ratio of the target wheel based on the longitudinal slip ratio and the target lateral slip ratio, including: the vehicle controller determines the road surface composite slip ratio of the target wheel based on the following formula (6);

[0061]

[0062] Among them, s i Let k be the road surface composite slip ratio of the target wheel i. xy This is the first coefficient.

[0063] In some embodiments, the vehicle controller determines the third road surface adhesion coefficient based on the road surface adhesion coefficient corresponding to each wheel in the vehicle, including: the vehicle controller determines the third road surface adhesion coefficient based on the following formula (7);

[0064]

[0065] in, This is the adhesion coefficient of the third road surface. This refers to the longitudinal road surface adhesion coefficient corresponding to the first wheel of the vehicle. This is the lateral road adhesion coefficient corresponding to the first wheel. This refers to the longitudinal road surface adhesion coefficient corresponding to the second wheel of the vehicle. This refers to the lateral road surface adhesion coefficient corresponding to the second wheel. This refers to the longitudinal road surface adhesion coefficient corresponding to the i-th wheel of the vehicle, i.e., the longitudinal road surface adhesion coefficient corresponding to wheel i. This is the lateral road adhesion coefficient corresponding to the i-th wheel, i.e., the lateral road adhesion coefficient corresponding to wheel i. It should be understood that the third road adhesion coefficient is determined by the lateral road adhesion coefficients and the corresponding longitudinal road adhesion coefficients of each wheel in the vehicle.

[0066] In some embodiments, when the total number of wheels in the vehicle is 4, the value of i is 4, and the third road adhesion coefficient...

[0067] In one possible implementation, the vehicle controller determines the road adhesion coefficient corresponding to the target wheel based on the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient, including: the vehicle controller determines a fifth road adhesion coefficient based on the road surface composite slip ratio; the vehicle controller determines the road adhesion coefficient corresponding to the target wheel based on the fifth road adhesion coefficient, the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient.

[0068] In the above technical solution, determining the fifth road adhesion coefficient based on the road surface composite slip ratio of the target wheel enables a more accurate assessment of the tire's friction characteristics under the road surface composite slip ratio while driving on the road surface. By comprehensively considering the lateral slip ratio and longitudinal slip ratio, the actual friction performance of the tire under multi-directional force conditions can be assessed more accurately. Furthermore, introducing the first coefficient further refines the description of the tire's friction performance. Therefore, in this method, the vehicle controller can accurately determine the road surface adhesion coefficient corresponding to the target wheel when the vehicle is driving, based on the fifth road surface adhesion coefficient, road surface composite slip ratio, longitudinal slip ratio, lateral slip ratio, and the first coefficient.

[0069] In some embodiments, the vehicle controller determines a fifth road surface adhesion coefficient based on the road surface composite slip ratio, including: the vehicle controller determines the fifth road surface adhesion coefficient based on the following formula (8);

[0070]

[0071] Where, f(S) i ) is the fifth road surface adhesion coefficient determined based on the composite slip ratio of the road surface. Formula (8) can be understood as the mapping relationship between the road surface adhesion coefficient and the slip ratio established by the wheel-ground contact model (such as the Burckhardt longitudinal and lateral coupling tire model). c1, c2 and c3 are the fitting parameters in the wheel-ground contact model. c1, c2 and c3 are only related to the road surface adhesion conditions.

[0072] It should be noted that the pavement adhesion coefficient is simulated by the pavement composite slip ratio in the above formula (8). Specifically, the pavement adhesion coefficient shows a trend of "first increasing and then decreasing" with the change of the pavement composite slip ratio, and the exponential term in formula (8) During the rising phase when the low pavement composite slip ratio is dominant, the linear term (c3S) in formula (8) i The exponential and linear terms, which dominate the descent phase of high pavement composite slip ratio, can comprehensively describe the pavement adhesion characteristics across the entire slip ratio range. Experiments have shown that the superposition of the exponential and linear terms effectively simulates the pavement adhesion coefficient curves for different pavement types (dry, wet, snowy, etc.). Furthermore, the variables (c1, c2, c3, S) in formula (8)... i and f(S) i )) are dimensionless.

[0073] Optionally, for dry road surfaces, c1 is 1.7, c2 is 20, and c3 is 0.03; for icy and snowy road surfaces, c1 is 0.1, c2 is 5, and c3 is 0.03.

[0074] In some embodiments, the vehicle controller determines the road adhesion coefficient corresponding to the target wheel based on the fifth road adhesion coefficient, the road composite slip ratio, the longitudinal slip ratio, the lateral slip ratio and the first coefficient, including: the vehicle controller determines the road adhesion coefficient corresponding to the target wheel based on the following formula (9);

[0075]

[0076] in, This refers to the longitudinal road adhesion coefficient within the road adhesion coefficient corresponding to the target wheel i. Let a0 be the lateral road adhesion coefficient in the road adhesion coefficient corresponding to the target wheel i, and let a0 be the peak friction coefficient of the road surface, which is dimensionless.

[0077] In one possible implementation, the vehicle controller in step 201 determines the first road surface adhesion coefficient of the vehicle's driving surface, including: the vehicle controller determines the difference between the second road surface adhesion coefficient and the third road surface adhesion coefficient of the current iteration number as the sixth road surface adhesion coefficient of the current iteration number; the vehicle controller determines the first product between the sixth road surface adhesion coefficient and the Kalman gain of the current iteration number, and determines the first road surface adhesion coefficient based on the first product.

[0078] It should be understood that the above scheme describes the use of the Extended Kalman Filter (EKF) algorithm, which utilizes the Kalman gain of the current iteration number and the prior estimated road adhesion coefficient to fuse the second and third road adhesion coefficients of the current iteration number (current time) to determine the first road adhesion coefficient of the current iteration number (current time). However, the current iteration number can be the last iteration number, and the first road adhesion coefficient of the current iteration number is used as the first road adhesion coefficient in step 201.

[0079] It should also be understood that the Kalman gain for the current iteration number is related to the lateral and longitudinal road adhesion coefficients corresponding to each wheel in the third road adhesion coefficient. Furthermore, the Kalman gain for the current iteration number is specifically a Kalman gain matrix.

[0080] In the above technical solution, the dynamic road surface adhesion coefficient (second road surface adhesion coefficient) involves the tire force of the wheel, while the kinematic road surface adhesion coefficient (third road surface adhesion coefficient) is related to the vehicle's motion state. By fusing the second and third road surface adhesion coefficients, the characteristics of the driving road surface can be reflected from multiple different perspectives, reducing the error when determining the road surface adhesion coefficient from a single factor. Furthermore, the Kalman gain of the current iteration number can play a weighting role in the fusion process. Since the Kalman gain is related to the lateral and longitudinal road surface adhesion coefficients corresponding to each wheel, the vehicle controller can reasonably allocate the weights of the second and third road surface adhesion coefficients in the fusion based on the actual adhesion conditions of different wheels, making the first road surface adhesion coefficient closer to the true value.

[0081] In some embodiments, the method for determining the Kalman gain for the current iteration includes: the vehicle controller determining the covariance matrix of the road adhesion coefficient estimated a priori for the current iteration based on the Jacobian system matrix corresponding to the extended Kalman filter, the covariance matrix of the road adhesion coefficient estimated posteriorly for the previous iteration, and the covariance matrix of the process noise; and the vehicle controller determining the Kalman gain for the current iteration based on the covariance matrix of the road adhesion coefficient estimated a priori for the current iteration, the Jacobian observation matrix of the current iteration corresponding to the extended Kalman filter, and the covariance matrix of the measurement noise.

[0082] In some embodiments, the vehicle controller determines the covariance matrix of the road adhesion coefficient estimated a priori for the current iteration number based on the Jacobian system matrix corresponding to the extended Kalman filter, the covariance matrix of the road adhesion coefficient estimated posteriorly for the previous iteration number, and the covariance matrix of the process noise, including: the vehicle controller determines the covariance matrix of the road adhesion coefficient estimated a priori for the current iteration number based on the following formula (10); and the vehicle controller determines the Kalman gain for the current iteration number based on the covariance matrix of the road adhesion coefficient estimated a priori for the current iteration number, the Jacobian observation matrix of the current iteration number corresponding to the extended Kalman filter, and the covariance matrix of the measurement noise, including: the vehicle controller determines the Kalman gain for the current iteration number based on the following formula (11);

[0083]

[0084] in, Let F be the covariance matrix of the prior estimated road adhesion coefficient for the current iteration number k, and let F be the Jacobian system matrix, which refers to the Jacobian system matrix when estimating the first road adhesion coefficient for the current iteration number k. F is the covariance matrix of the road adhesion coefficient estimated posteriorly in the previous iteration k-1. T Let Q be the transpose of the Jacobian system matrix, Q be the covariance matrix of the process noise, and K be the... k H is the Kalman gain for the current iteration number k. k Let H be the Jacobian observation matrix for the current iteration number k corresponding to this extended Kalman filter, and R be the covariance matrix of the measurement noise. k The value is given by the following formula (12). In addition, since the first road surface adhesion coefficient is determined iteratively, the method also gives the covariance matrix of the road surface adhesion coefficient estimated posteriorly for the current iteration number k by the following formula (13).

[0085]

[0086] It should be understood that the "covariance matrix of the road adhesion coefficient estimated a priori for the current iteration number" in the above scheme is not the covariance matrix determined under the condition of known observation data (the Jacobian observation matrix of the current iteration number). The "covariance matrix of the road adhesion coefficient estimated posteriorly for the current iteration number" is the covariance matrix after correcting the covariance matrix of the road adhesion coefficient estimated a priori for the current iteration number, given the observation data (the Jacobian observation matrix of the current iteration number K).

[0087] In some embodiments, the vehicle controller determines a first product between the sixth road surface adhesion coefficient and the Kalman gain of the current iteration number, and determines the first road surface adhesion coefficient based on the first product, including: the vehicle controller determines the first road surface adhesion coefficient based on the following formula (14);

[0088]

[0089] It should be understood that and The dimensions remain consistent, that is to say, and The number of rows is the same. and The number of columns is the same, K k The number of rows and The number of columns remains consistent, K k The number of columns and The number of rows remains consistent. When the total number of wheels in the vehicle is 4, The dimensions are 8*1. The dimensions are 8*1. The dimensions are 8*1, K k The dimensions are 1*8. It should also be understood that... This can be considered as the actual measured road adhesion coefficient (actual measured value) of the current iteration number. It can be regarded as the measured and predicted road adhesion coefficient (measured and predicted value) of the current iteration number.

[0090] in, Let be the intermediate variable used to determine the first road surface adhesion coefficient for the current iteration number k, i.e., the intermediate variable for the posterior estimate of the current iteration number k. The corresponding true first road surface adhesion coefficient for the current iteration number k is: The intermediate variable is the prior estimate of the current iteration number k. The adhesion coefficient of the second road surface at the current iteration number k. The adhesion coefficient of the third road surface at the current iteration number k. The adhesion coefficient of the sixth road surface at the current iteration number k. This is the first product at the current iteration number k. It is important to note that... The value range is (0,1).

[0091] In one possible implementation, the vehicle controller determines a first product between the sixth road surface adhesion coefficient and the Kalman gain of the current iteration, and determines the first road surface adhesion coefficient based on the first product. This includes: the vehicle controller determining the total slip ratio by summing the composite slip ratios of multiple wheels in the vehicle; the vehicle controller determining an excitation mode based on the total slip ratio, the excitation mode being used to determine how to update the Kalman gain during the iterative determination of the first road surface adhesion coefficient using the extended Kalman filter algorithm; the vehicle controller updating the Kalman gain of the current iteration based on the excitation mode, and determining the updated Kalman gain; and the vehicle controller determining the first product between the sixth road surface adhesion coefficient and the updated Kalman gain, and determining the first road surface adhesion coefficient based on the first product.

[0092] It should be understood that when determining the first road surface adhesion coefficient using the EKF algorithm, a target number of iterations is pre-set. This method, in the process of fusing the EKF algorithm to obtain the first road surface adhesion coefficient, also judges the sum of the composite slip ratios of multiple wheels (total slip ratio) to determine the total slip ratio, thus determining how to update the Kalman gain during the determination of the first road surface adhesion coefficient. In this way, the update process of the Kalman gain can be controlled during the iterative process of determining the first road surface adhesion coefficient, which also indirectly controls the number of iterations, ensuring that the first road surface adhesion coefficient is determined with fewer iterations than the target number.

[0093] It should also be understood that the "Kalman gain" of the current iteration in the above scheme is determined by the covariance matrix of the road adhesion coefficient estimated a priori for the current iteration. Therefore, the "excitation mode" in the above scheme is specifically used to determine the way to update the covariance matrix of the road adhesion coefficient estimated a priori during the process of iteratively determining the first road adhesion coefficient through the extended Kalman filter algorithm.

[0094] It should also be understood that the process of “the vehicle controller determining the first product between the sixth road surface adhesion coefficient and the updated Kalman gain, and determining the first road surface adhesion coefficient based on the first product” in the above scheme is similar to the process of “the vehicle controller determining the first product between the sixth road surface adhesion coefficient and the Kalman gain of the current iteration number, and determining the first road surface adhesion coefficient based on the first product”, and will not be elaborated here.

[0095] In the above technical solution, the vehicle controller determines the excitation mode based on the sum of the combined road surface slip ratios (total slip ratio) of multiple wheels. This excitation mode is used to determine how to update the Kalman gain during the iterative determination of the first road surface adhesion coefficient using the Extended Kalman Filter (EKF) algorithm. Furthermore, the vehicle controller updates the Kalman gain for the current iteration number based on this excitation mode. By using the EKF algorithm to determine the first road surface adhesion coefficient with a target number of iterations and updating the Kalman gain, the vehicle controller can accelerate the fusion of the dynamic and kinematic road surface adhesion coefficients, enabling the vehicle controller to determine the first road surface adhesion coefficient with fewer iterations. Therefore, this method can fuse the first road surface adhesion coefficient at a faster speed.

[0096] In some embodiments, the vehicle controller determines an excitation mode based on the total slip ratio, including: when the total slip ratio is greater than or equal to a first preset slip ratio, the vehicle controller determines the excitation mode as a first mode, the first mode being used to indicate updating the Kalman gain at a first update rate; when the total slip ratio is less than the first preset slip ratio, the vehicle controller determines the excitation mode as a second mode, the second mode being used to indicate updating the Kalman gain at a second update rate, the first update rate being greater than the second update rate, the second update rate being the rate at which the Kalman gain is updated in the extended Kalman filter algorithm using the covariance matrix of the road adhesion coefficient estimated a priori, the Jacobian observation matrix, and the covariance matrix of the process noise.

[0097] It should be understood that the "second update speed is the speed at which the Kalman gain is updated using the covariance matrix of the road adhesion coefficient estimated a priori, the Jacobian observation matrix, and the covariance matrix of the measurement noise in the extended Kalman filter algorithm" in the above scheme can be understood as the speed at which the Kalman gain is updated using formula (11). When the excitation mode is specifically used to determine the method of updating the covariance matrix of the road adhesion coefficient estimated a priori during the iterative determination of the first road adhesion coefficient using the extended Kalman filter algorithm, this second update speed is the speed at which the covariance matrix of the road adhesion coefficient estimated a priori is updated using the covariance matrix of the road adhesion coefficient estimated a priori, the Jacobian system matrix, and the covariance matrix of the process noise in the extended Kalman filter algorithm, specifically the speed at which the covariance matrix of the road adhesion coefficient estimated a priori is updated using formula (10).

[0098] It should also be understood that the "first mode" in the above scheme can be regarded as the urgent update mode, and the "second mode" can be regarded as the slow update mode.

[0099] In some embodiments, the vehicle controller updates the Kalman gain for the current iteration number based on the excitation mode and determines the updated Kalman gain, including: when the excitation mode is the first mode, the vehicle controller determines the sum of the Kalman gain for the current iteration number and the target preset value as the updated Kalman gain; when the excitation mode is the second mode, the vehicle controller determines the Kalman gain for the current iteration number as the updated Kalman gain.

[0100] It should be understood that the above scheme describes that when the excitation mode is the first mode, the vehicle controller adds a target preset value to the Kalman gain based on the current iteration number to obtain a larger Kalman gain. That is, the Kalman gain is further increased based on the original Kalman gain updated by formula (11) in the EKF algorithm. When the excitation mode is the second mode, the vehicle controller only updates the Kalman gain by formula (11) in the EKF algorithm.

[0101] Step 202: The vehicle controller integrates the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient of the wheel to determine the target road surface adhesion coefficient. The fourth road surface adhesion coefficient is determined based on the vehicle's acceleration, and the utilization adhesion coefficient is used to indicate the effective utilization rate of the actual friction force between the wheel's tire and the road surface.

[0102] It should be understood that the specific solution described in step 202 above is to adjust the first road surface adhesion coefficient. (k is the current iteration number, specifically the last iteration number), fourth road surface adhesion coefficient μ a and utilizing the adhesion coefficient μ f The process involves fusing the data to determine the target road surface adhesion coefficient. Furthermore, in step 202 above, the "fourth road surface adhesion coefficient" can be considered as the road surface adhesion coefficient estimated by acceleration. The "utilized adhesion coefficient" refers to the minimum road surface adhesion coefficient required to prevent wheel lock-up under a specific braking intensity. The utilized adhesion coefficient describes the relationship between the actual adhesion force provided by the road surface and the maximum braking force required by the vehicle during braking. This method uses the maximum utilized adhesion coefficient among the N wheels of the vehicle to equivalently obtain μ... f The coefficient of adhesion for each wheel can be determined by the part in parentheses in the following formula (16). Where N is a positive integer greater than or equal to 2.

[0103] It should also be understood that if the current iteration number has not reached the target iteration number, the road surface adhesion coefficient needs to be updated again using the following formula (15) to determine the target road surface adhesion coefficient.

[0104]

[0105] in, The first road adhesion coefficient that can determine the next iteration number (k+1)

[0106] In some embodiments, the method for determining the fourth road surface adhesion coefficient in step 202 includes: the vehicle controller determining the fourth road surface adhesion coefficient based on the following formula (16);

[0107]

[0108] Among them, a x Let a be the longitudinal acceleration of the vehicle. y Let μ be the lateral acceleration of the vehicle, g be the acceleration due to gravity, and θ be the slope angle of the road surface. It's important to note that the slope angle can be positive or negative; it's positive when the vehicle is going uphill and negative when going downhill. Also note that μ... a The value range is (0,1).

[0109] In some embodiments, the method for determining the utilization adhesion coefficient of the wheel in step 202 includes: the vehicle controller determining the utilization adhesion coefficient of the wheel based on the following formula (17);

[0110]

[0111] Among them, F xi F is the longitudinal tire force of wheel i. yi F is the lateral tire force of wheel i. zi Let μ be the vertical tire force of wheel i. It should be understood that the coefficient of adhesion of this wheel is used. f It is the maximum coefficient of adhesion among the N wheels of the vehicle. It is important to note that μ... f The value range is (0,1).

[0112] In one possible implementation, the vehicle controller in step 202 fuses the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the wheel's utilization adhesion coefficient to determine the target road surface adhesion coefficient, including any one of the following: the vehicle controller determines the maximum adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient as the target road surface adhesion coefficient; the vehicle controller determines the average adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient as the target road surface adhesion coefficient; the vehicle controller applies a first weight to the first road surface adhesion coefficient... The system performs a weighted average to obtain a first coefficient; the vehicle controller then performs a weighted average on the fourth road surface adhesion coefficient using a second weight to obtain a second coefficient; the vehicle controller then performs a weighted average on the utilized adhesion coefficient using a third weight to obtain a third coefficient; the sum of the first coefficient, the second coefficient, and the third coefficient is determined as the target road surface adhesion coefficient; wherein, the first weight indicates the contribution of the first road surface adhesion coefficient in determining the target road surface adhesion coefficient, the second weight indicates the contribution of the fourth road surface adhesion coefficient in determining the target road surface adhesion coefficient, and the third weight indicates the contribution of the utilized adhesion coefficient in determining the target road surface adhesion coefficient.

[0113] It should be understood that the above scheme provides three ways to determine the target road surface adhesion coefficient of the driving road surface. Specifically, the first method is to select the maximum adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient; the second method is to select the average adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient; and the third method is to select the adhesion coefficient after weighted fusion of the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient.

[0114] It should also be understood that the target road surface adhesion coefficient determined by the first method described above is the maximum road surface adhesion coefficient, ensuring that the vehicle can drive in the safest way under various driving conditions. For example, when braking, setting the braking strategy with the maximum road surface adhesion coefficient as the target allows the vehicle to stop in the shortest distance, making full use of the maximum friction provided by the road surface. When accelerating, using the maximum road surface adhesion coefficient as a basis allows for optimization of the vehicle's power output, enabling the vehicle to increase speed as quickly as possible without slipping. The average adhesion coefficient determined by the second method balances the advantages and disadvantages of road surface adhesion coefficients from different sources, resulting in a road surface adhesion coefficient that better reflects the overall adhesion characteristics of the road surface. This average adhesion coefficient can provide a relatively accurate reference for road conditions during vehicle operation, allowing for better adjustment of vehicle status. The weighted fusion adhesion coefficient determined by the third method, like the average adhesion coefficient determined by the second method, reflects the overall adhesion characteristics of the road surface, but the weighted fusion adhesion coefficient of the third method is more accurate than that of the second method. This is because the third method can take into account the different contributions of road adhesion coefficients from different sources in determining the target road adhesion coefficient under the current driving conditions.

[0115] In the above technical solutions, determining the target road surface adhesion coefficient through multiple methods avoids situations where the target road surface adhesion coefficient cannot be determined and also meets different needs when determining the target road surface adhesion coefficient. Furthermore, the target road surface adhesion coefficient determined by the first method is the maximum road surface adhesion coefficient, ensuring that the vehicle can drive in the safest manner under various driving conditions. The average adhesion coefficient determined by the second method can balance the advantages and disadvantages of road surface adhesion coefficients from different sources, obtaining a road surface adhesion coefficient that better reflects the overall adhesion characteristics of the driving road surface. Compared to the second method, the weighted fusion adhesion coefficient of the third method is more accurate. This is because the third method can consider the different contributions of road surface adhesion coefficients from different sources in determining the target road surface adhesion coefficient under the current driving conditions.

[0116] In one possible implementation, the method for determining the first weight, the second weight, and the third weight includes: the vehicle controller determining the wear degree of the tires of the wheels in the vehicle; if the wear degree is less than or equal to a preset degree, the vehicle controller determining the first weight, the second weight, and the third weight based on a first ratio between a first preset value and the total number corresponding to the weight, wherein the first weight, the second weight, and the third weight are the same; if the wear degree is greater than the preset degree, the vehicle controller determining the second weight based on the first ratio and the deviation of the wear degree from the preset degree; and the vehicle controller determining the first weight and the third weight based on the first preset value and the second weight.

[0117] It should be understood that in the above scheme, the "first preset value" is 1, and the "total number corresponding to the weights" is 3. This is because method 200 includes a total of three weights: the first weight, the second weight, and the third weight. It should also be understood that the "preset degree" in the above scheme is used to indicate the degree of wear that does not affect the accurate determination of multiple vehicle parameters after the tires of the wheel are worn. These multiple vehicle parameters include tire force, slip ratio, and rolling resistance of the tires, etc. Both the degree of wear and the preset degree are expressed as percentages.

[0118] It should also be understood that in the above scheme, when the wear level is less than or equal to the preset level, the vehicle determines the first weight, second weight and third weight as 1 / 3, 1 / 3 and 1 / 3 respectively based on the first ratio between the first preset value 1 and the total number 3 corresponding to the weight.

[0119] In the above technical solution, tire wear affects the accuracy of road adhesion coefficients from different sources. Specifically, tire wear has the least impact on the accuracy of the fourth road adhesion coefficient, followed by the wheel's utilization adhesion coefficient, and lastly the first road adhesion coefficient. This is because tire wear has little impact on the acceleration during the determination of the fourth road adhesion coefficient, but it does affect the tire force during the determination of the utilization adhesion coefficient, and even more so the tire force and slip ratio during the determination of the first road adhesion coefficient. Therefore, based on the above theory, the vehicle controller in this method can accurately assess the contribution of road adhesion coefficients from different sources—namely, the first weight, the second weight, and the third weight—when determining the target road adhesion coefficient by measuring tire wear.

[0120] In some embodiments, the wear level of the wheel's tires is the maximum wear level, average wear level, or minimum wear level of the tires of N wheels in the vehicle.

[0121] In some embodiments, the method for determining the deviation of the wear level from the preset level includes: the vehicle controller determining the difference between the wear level and the preset level as the deviation.

[0122] In some embodiments, the vehicle controller determines the wear level of the tires of the wheels in the vehicle, including: the vehicle controller acquiring the height deviation between the current height of the tread pattern on the tire of any wheel and the target height of the wear indicator on the tire; the vehicle controller determining a second ratio between the height deviation and the target height; and the vehicle controller determining the difference between the first preset value and the second ratio as the wear level.

[0123] In some embodiments, the vehicle controller determines the second weight based on the first ratio and the deviation of the wear level from the preset level, including: the vehicle controller determining a second product between the first ratio and the deviation; the vehicle controller determining the second weight as the sum of the second product and the first ratio.

[0124] It should be understood that the "second weight" in the above scheme is a weighting coefficient for the road adhesion coefficient (fourth road adhesion coefficient) estimated by acceleration. The degree of tire wear does not significantly affect the accuracy of determining vehicle acceleration, and therefore does not significantly affect the accuracy of determining the fourth road adhesion coefficient. Thus, when the wear degree is greater than the preset degree, the fourth road adhesion coefficient is more accurate than the first road adhesion coefficient and the utilized adhesion coefficient, and therefore its corresponding second weight is larger.

[0125] In some embodiments, the vehicle controller determines the first weight and the third weight based on the first preset value and the second weight, including: the vehicle controller determining a second difference between the first preset value and the second weight; the vehicle controller determining the first weight and the third weight based on the ratio between the second difference and the second preset value, wherein the first weight and the third weight are the same; or, the vehicle controller determining a third ratio between the second difference and the third preset value as the first weight, and determining the product between the third ratio and the second preset value as the third weight.

[0126] It should be understood that in the above scheme, the "second preset value" is 2 and the "third preset value" is 3. It should also be understood that the above scheme provides two methods for determining the first and third weights. The first method: divide the second difference into two equal parts to determine the first and third weights, with the first and third weights being the same. The second method: divide the second difference into three equal parts, determining one part as the first weight and the other two parts as the third weight. This is because the first weight is a weighted coefficient of the first road surface adhesion coefficient, which is affected by both tire force and slip ratio, while the third weight is a weighted coefficient using the adhesion coefficient, which is only affected by tire force. Relatively speaking, using the adhesion coefficient is more accurate than using the first road surface adhesion coefficient. Therefore, when determining the target road surface adhesion coefficient, the contribution of the adhesion coefficient is greater than that of the first road surface adhesion coefficient; the third weight accounts for a larger proportion of the second difference, while the first weight accounts for a smaller proportion.

[0127] Figure 3 This is a schematic block diagram illustrating an estimation of the road surface adhesion coefficient provided in an embodiment of this application.

[0128] For example, such as Figure 3As shown, the vehicle controller inputs the lateral slip ratio and longitudinal slip ratio of the wheels into the kinematic model to determine the third road adhesion coefficient, and inputs the vertical tire force, longitudinal tire force, and lateral tire force of the wheels into the dynamic model to determine the second road adhesion coefficient. The vehicle controller performs a first fusion of the second and third road adhesion coefficients to obtain the first road adhesion coefficient. The vehicle controller determines the fourth road adhesion coefficient using the vehicle's acceleration, and determines the utilization adhesion coefficient using the vertical tire force, longitudinal tire force, and lateral tire force of the wheels. The vehicle controller performs a second fusion of the first, fourth, and utilization adhesion coefficients to obtain an estimated value of the road adhesion coefficient. The vehicle controller transforms this estimated value using -ln(estimated value) to obtain a transformed estimated value, which is used to determine the first road adhesion coefficient in the next iteration until the target iteration number is reached. The finally determined road adhesion coefficient is used as the target road adhesion coefficient. Here, -ln(estimated value) is given in the first iteration.

[0129] Figure 4 This is a schematic flowchart illustrating an embodiment of the present application for estimating the road surface adhesion coefficient.

[0130] For example, such as Figure 4 As shown, the vehicle controller fuses the second and third road surface adhesion coefficients to obtain the first road surface adhesion coefficient. The vehicle controller then fuses this first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the wheel adhesion coefficients to determine the target road surface adhesion coefficient for the vehicle's driving surface. The second road surface adhesion coefficient is determined based on the tire forces in the vehicle, the third road surface adhesion coefficient is determined based on the wheel slip ratio, and the fourth road surface adhesion coefficient is determined based on the vehicle's acceleration.

[0131] Figure 5 This is a schematic diagram of the structure of a device for estimating the road surface adhesion coefficient provided in an embodiment of this application.

[0132] For example, such as Figure 5 As shown, the device 500 includes:

[0133] The determination module 501 is used to determine the first road surface adhesion coefficient of the vehicle when the vehicle is in motion. The first road surface adhesion coefficient is obtained by fusing the second road surface adhesion coefficient and the third road surface adhesion coefficient. The second road surface adhesion coefficient is determined based on the tire force in the vehicle, and the third road surface adhesion coefficient is determined based on the wheel slip ratio in the vehicle.

[0134] The fusion module 502 is used to fuse the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient of the wheel to determine the target road surface adhesion coefficient of the driving road surface. The fourth road surface adhesion coefficient is determined based on the acceleration of the vehicle, and the utilization adhesion coefficient is used to indicate the effective utilization rate of the actual friction force between the tire of the wheel and the driving road surface.

[0135] Optionally, the determining module 501 is specifically configured to: take any wheel in the vehicle as the target wheel, determine the longitudinal slip ratio and lateral slip ratio of the target wheel; based on the longitudinal slip ratio and the target lateral slip ratio, determine the road surface composite slip ratio of the target wheel, wherein the target lateral slip ratio is obtained by multiplying the lateral slip ratio with a first coefficient, the first coefficient being used to indicate the degree of influence of the lateral slip ratio on the tire friction performance of the target wheel; based on the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio and the first coefficient, determine the road surface adhesion coefficient corresponding to the target wheel; and based on the road surface adhesion coefficients corresponding to each wheel in the vehicle, determine the third road surface adhesion coefficient.

[0136] Optionally, the determining module 501 is further configured to: determine a fifth road surface adhesion coefficient based on the road surface composite slip ratio; and determine the road surface adhesion coefficient corresponding to the target wheel based on the fifth road surface adhesion coefficient, the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient.

[0137] Optionally, the determining module 501 is further configured to: determine the difference between the second road surface adhesion coefficient and the third road surface adhesion coefficient in the current iteration as the sixth road surface adhesion coefficient in the current iteration; determine the first product between the sixth road surface adhesion coefficient and the Kalman gain in the current iteration, and determine the first road surface adhesion coefficient based on the first product and the prior estimated road surface adhesion coefficient in the current iteration.

[0138] Optionally, the determining module 501 is further configured to: determine the sum of the road surface composite slip ratios of multiple wheels in the vehicle as the total slip ratio; determine an excitation mode based on the total slip ratio, the excitation mode being used to determine the method of updating the Kalman gain during the iterative determination of the first road surface adhesion coefficient through the extended Kalman filter algorithm; update the Kalman gain for the current iteration number based on the excitation mode, and determine the updated Kalman gain; determine the first product between the sixth road surface adhesion coefficient and the updated Kalman gain, and determine the first road surface adhesion coefficient based on the first product.

[0139] Optionally, the fusion module 502 is specifically used for any of the following: determining the maximum adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient as the target road surface adhesion coefficient; determining the average adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient as the target road surface adhesion coefficient; weighting the first road surface adhesion coefficient with a first weight to obtain a first coefficient; weighting the fourth road surface adhesion coefficient with a second weight to obtain a second coefficient; weighting the utilization adhesion coefficient with a third weight to obtain a third coefficient; and determining the sum of the first coefficient, the second coefficient, and the third coefficient as the target road surface adhesion coefficient; wherein the first weight is used to indicate the contribution of the first road surface adhesion coefficient in determining the target road surface adhesion coefficient, the second weight is used to indicate the contribution of the fourth road surface adhesion coefficient in determining the target road surface adhesion coefficient, and the third weight is used to indicate the contribution of the utilization adhesion coefficient in determining the target road surface adhesion coefficient.

[0140] Optionally, the determining module 501 is further configured to: determine the wear degree of the tires of the wheels in the vehicle; if the wear degree is less than or equal to a preset degree, determine the first weight, the second weight, and the third weight based on a first ratio between a first preset value and the total number corresponding to the weights, wherein the first weight, the second weight, and the third weight are the same; if the wear degree is greater than the preset degree, determine the second weight based on the first ratio and the deviation of the wear degree from the preset degree; and determine the first weight and the third weight based on the first preset value and the second weight.

[0141] Figure 6 This is a structural schematic diagram of a vehicle provided in an embodiment of this application.

[0142] For example, such as Figure 6 As shown, the vehicle 600 includes a memory 601 and a processor 602, wherein the memory 601 stores executable program code 603, and the processor 602 is used to call and execute the executable program code 603 to perform a method for estimating the road surface adhesion coefficient.

[0143] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a method for estimating the road surface adhesion coefficient provided in embodiments of this application.

[0144] This embodiment can divide the device into functional modules based on the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods.

[0145] When the functional modules are divided according to their respective functions, the device may also include a determination module and a fusion module, etc. It should be noted that all relevant content in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0146] It should be understood that the apparatus provided in this embodiment is used to perform the above-described method for estimating the road surface adhesion coefficient, and therefore can achieve the same effect as the above-described implementation method.

[0147] When using an integrated unit, the device may include a processing module and a storage module. When the device is applied to a vehicle, the processing module can be used to control and manage the vehicle's movements. The storage module can be used to support the vehicle in executing relevant executable program code.

[0148] The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits shown in conjunction with the disclosure of this application. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0149] In addition, the device provided in the embodiments of this application may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute a method for estimating the road surface adhesion coefficient provided in the above embodiments.

[0150] This embodiment also provides a computer-readable storage medium storing executable program code. When the executable program code is run on a computer, the computer performs the above-described related method steps to implement the method for estimating the road surface adhesion coefficient provided in the above embodiment.

[0151] This embodiment also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the method for estimating the road surface adhesion coefficient provided in the above embodiment.

[0152] In this embodiment, the device, computer-readable storage medium, computer program product, or chip are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0153] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0154] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0155] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for estimating the road surface adhesion coefficient, characterized in that, The method includes: When the vehicle is in motion, a first road surface adhesion coefficient is determined for the road surface on which the vehicle is traveling. This determination includes: determining the difference between the second road surface adhesion coefficient and the third road surface adhesion coefficient at the current iteration number as the sixth road surface adhesion coefficient at the current iteration number; the second road surface adhesion coefficient is determined based on the tire force in the vehicle, and the third road surface adhesion coefficient is determined based on the wheel slip ratio in the vehicle; determining the total slip ratio by summing the combined road surface slip ratios of multiple wheels in the vehicle; determining an excitation mode based on the total slip ratio, the excitation mode being used to determine the method for updating the Kalman gain during the iterative determination of the first road surface adhesion coefficient using the extended Kalman filter algorithm; updating the Kalman gain at the current iteration number based on the excitation mode, and determining the updated Kalman gain; determining a first product between the sixth road surface adhesion coefficient and the updated Kalman gain, and determining the first road surface adhesion coefficient based on the first product and the prior estimated road surface adhesion coefficient at the current iteration number. The first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilization adhesion coefficient of the wheel are combined to determine the target road surface adhesion coefficient. The fourth road surface adhesion coefficient is determined based on the vehicle's acceleration, and the utilization adhesion coefficient is used to indicate the effective utilization rate of the actual friction force between the wheel's tire and the road surface.

2. The method according to claim 1, characterized in that, The method for determining the third road surface adhesion coefficient includes: Using any wheel in the vehicle as the target wheel, determine the longitudinal slip ratio and lateral slip ratio of the target wheel; Based on the longitudinal slip ratio and the target lateral slip ratio, the road surface composite slip ratio of the target wheel is determined. The target lateral slip ratio is obtained by multiplying the lateral slip ratio with a first coefficient, which is used to indicate the degree of influence of the lateral slip ratio on the tire friction performance of the target wheel. Based on the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient, the road surface adhesion coefficient corresponding to the target wheel is determined; The third road surface adhesion coefficient is determined based on the road surface adhesion coefficient corresponding to each wheel in the vehicle.

3. The method according to claim 2, characterized in that, The determination of the road adhesion coefficient corresponding to the target wheel based on the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient includes: Based on the aforementioned road surface composite slip ratio, the fifth road surface adhesion coefficient is determined; Based on the fifth road surface adhesion coefficient, the road surface composite slip ratio, the longitudinal slip ratio, the lateral slip ratio, and the first coefficient, the road surface adhesion coefficient corresponding to the target wheel is determined.

4. The method according to claim 1, characterized in that, The step of fusing the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the wheel's utilization adhesion coefficient to determine the target road surface adhesion coefficient includes any one of the following: The maximum adhesion coefficient among the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the utilized adhesion coefficient is determined as the target road surface adhesion coefficient. The average adhesion coefficient between the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the adhesion coefficient is determined as the target road surface adhesion coefficient. The first road surface adhesion coefficient is weighted by a first weight to obtain a first coefficient; the fourth road surface adhesion coefficient is weighted by a second weight to obtain a second coefficient; the adhesion coefficient is weighted by a third weight to obtain a third coefficient; the sum of the first coefficient, the second coefficient, and the third coefficient is determined as the target road surface adhesion coefficient. Wherein, the first weight is used to indicate the contribution of the first road surface adhesion coefficient in determining the target road surface adhesion coefficient, the second weight is used to indicate the contribution of the fourth road surface adhesion coefficient in determining the target road surface adhesion coefficient, and the third weight is used to indicate the contribution of the adhesion coefficient in determining the target road surface adhesion coefficient.

5. The method according to claim 4, characterized in that, The methods for determining the first weight, the second weight, and the third weight include: Determine the degree of wear of the tires on the wheels of the vehicle; When the wear level is less than or equal to a preset level, the first weight, the second weight, and the third weight are determined based on a first ratio between the first preset value and the total number corresponding to the weight, wherein the first weight, the second weight, and the third weight are the same; If the wear level is greater than the preset level, the second weight is determined based on the first ratio and the deviation of the wear level from the preset level; Based on the first preset value and the second weight, the first weight and the third weight are determined.

6. An apparatus for estimating the coefficient of road surface adhesion, characterized in that, The device includes: A determination module is used to determine a first road surface adhesion coefficient of the vehicle's driving surface when the vehicle is in motion. The determination of the first road surface adhesion coefficient includes: determining a sixth road surface adhesion coefficient for the current iteration by the difference between a second road surface adhesion coefficient and a third road surface adhesion coefficient for the current iteration, where the second road surface adhesion coefficient is determined based on the tire force in the vehicle, and the third road surface adhesion coefficient is determined based on the wheel slip ratio in the vehicle; determining a total slip ratio by the sum of the combined road surface slip ratios of multiple wheels in the vehicle; determining an excitation mode based on the total slip ratio, where the excitation mode determines the method for updating the Kalman gain during the iterative determination of the first road surface adhesion coefficient using the extended Kalman filter algorithm; updating the Kalman gain for the current iteration based on the excitation mode, and determining the updated Kalman gain; determining a first product between the sixth road surface adhesion coefficient and the updated Kalman gain, and determining the first road surface adhesion coefficient based on the first product and a priori estimated road surface adhesion coefficient for the current iteration. The fusion module is used to fuse the first road surface adhesion coefficient, the fourth road surface adhesion coefficient, and the wheel's utilization adhesion coefficient to determine the target road surface adhesion coefficient of the driving road surface. The fourth road surface adhesion coefficient is determined based on the vehicle's acceleration.

7. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores executable program code that, when executed, implements the method as described in any one of claims 1 to 5.

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