Tire road adhesion coefficient estimation method, device, electronic equipment and vehicle

Abstract

The application relates to a tire road surface adhesion coefficient estimation method and device, electronic equipment and vehicle, and relates to the field of automobile safety measurement and control, and comprises the following steps: determining tire longitudinal characteristics and tire lateral characteristics corresponding to each tire of a target vehicle; in response to the tire longitudinal characteristics triggering a longitudinal dynamics estimation condition of a road surface adhesion coefficient, calculating a first road surface adhesion coefficient estimation value based on the tire longitudinal characteristics; in response to the tire lateral characteristics triggering a lateral dynamics estimation condition of the road surface adhesion coefficient, calculating a second road surface adhesion coefficient estimation value based on the tire lateral characteristics; and determining a final road surface adhesion coefficient estimation value of the target vehicle according to the first road surface adhesion coefficient estimation value and / or the second road surface adhesion coefficient estimation value. By applying the technical scheme of the application, the adhesion coefficient deviation caused by the estimation error of the slip rate and the side slip angle under the conditions of small slip and small side slip is effectively avoided, and the adhesion coefficient estimation error under the conditions of high adhesion and insufficient excitation is solved.

Description

Technical Field

[0001] This application relates to the field of automotive safety measurement and control, specifically to a method, device, electronic equipment, and vehicle for estimating tire road surface adhesion coefficient. Background Technology

[0002] The road surface adhesion coefficient is an important parameter in the dynamic control of vehicle systems. Accurate estimation of the road surface adhesion coefficient can significantly improve the control effect of a series of chassis handling technologies, including vehicle electronic stability program, four-wheel steering stability control system, anti-lock braking system and traction control system.

[0003] Existing technologies primarily rely on tire dynamics models to continuously predict the road adhesion coefficient. This method is highly dependent on the accuracy of the tire model and the accuracy of vehicle state estimation. Since tire characteristics change during use due to factors such as wear, temperature, and tire pressure, the estimation accuracy is affected. Furthermore, because vehicle speed estimation largely stems from wheel speed, the estimated slip ratio under small slip conditions can have significant estimation errors, leading to a large error in the estimated road adhesion coefficient. When the road adhesion coefficient is high, it is difficult to achieve sufficient excitation (slip ratio or sideslip angle sufficient to differentiate tire characteristic curves under similar adhesion coefficients). Even with significant slippage or sideslip, the tire characteristic curves under different adhesion coefficients are not clearly distinguishable, resulting in a large error in the adhesion coefficient estimation. Summary of the Invention

[0004] In view of this, this application provides a method, device, electronic device and vehicle for estimating tire road adhesion coefficient, the main purpose of which is to avoid the adhesion coefficient deviation caused by the estimation error of slip ratio and sideslip angle under small slip and small sideslip conditions, and to solve the adhesion coefficient estimation error under high adhesion and insufficient excitation conditions.

[0005] According to a first aspect of this disclosure, a method for estimating tire-road adhesion coefficient is provided, comprising:

[0006] Determine the longitudinal and lateral characteristics of each tire of the target vehicle. The longitudinal characteristics include at least tire slip ratio, tire longitudinal force, vertical load, and tire longitudinal stiffness. The lateral characteristics include at least tire slip angle, tire lateral force, vertical load, and tire lateral stiffness. The longitudinal and lateral stiffness are obtained through tire feature self-learning.

[0007] In response to the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the tire longitudinal characteristics, a first road adhesion coefficient estimate is calculated based on the tire longitudinal characteristics, wherein the longitudinal dynamic estimation condition is that the first tire equivalent stiffness value is less than the first preset critical stiffness value, and the first tire equivalent stiffness value is calculated based on the tire longitudinal force and the tire slip ratio in the tire longitudinal characteristics.

[0008] In response to the lateral dynamics estimation condition of the road adhesion coefficient triggered by the tire lateral features, a second road adhesion coefficient estimate is calculated based on the tire lateral features, wherein the lateral dynamics estimation condition is that the second tire equivalent stiffness value is less than the second preset critical stiffness value, and the second tire equivalent stiffness value is calculated based on the tire lateral force and the tire slip angle in the tire lateral features;

[0009] The final estimated value of the road surface adhesion coefficient for the target vehicle is determined based on the first estimated value of the road surface adhesion coefficient and / or the second estimated value of the road surface adhesion coefficient.

[0010] In some embodiments of this disclosure, determining the tire slip ratio, tire longitudinal force, vertical load, and tire longitudinal stiffness for each tire of the target vehicle includes:

[0011] Obtain the first operating condition parameters of the target vehicle. The first operating condition parameters include at least the measured values ​​of vehicle speed, tire rotation speed, total vehicle mass, distance from the center of gravity to the front and rear axles, center of gravity height, wheelbase, and tire force data.

[0012] The tire slip ratio of each tire of the target vehicle is calculated based on the measured values ​​of the vehicle speed and the tire rotation speed.

[0013] Based on the axle load transfer relationship, as well as the total mass of the target vehicle, the distance from the center of gravity to the front and rear axles, the height of the center of gravity, and the wheelbase, the vertical load of each tire of the target vehicle is calculated.

[0014] By fitting the six-component force data of the tire, the initial longitudinal stiffness of each tire of the target vehicle is obtained, and the tire feature self-learning is performed based on the data collected by the target vehicle during actual operation to update the initial longitudinal stiffness of the tire to the final longitudinal stiffness.

[0015] Calculate the longitudinal force of the tire based on the torque applied to the tire, the tire's moment of inertia, the radius of rotation, and the tire's rotational speed.

[0016] In some embodiments of this disclosure, the method further includes:

[0017] Calculate the ratio of the tire longitudinal force to the tire slip ratio, and determine the ratio of the tire longitudinal force to the tire slip ratio as the first tire equivalent stiffness value;

[0018] Determine whether the equivalent stiffness value of the first tire is less than the first preset critical stiffness value, wherein the first preset critical stiffness value is the ratio of the longitudinal force of the tire to the tire slip ratio under the preset critical condition;

[0019] If so, then determine the longitudinal dynamic estimation conditions for the tire longitudinal characteristics triggering the road adhesion coefficient.

[0020] In some embodiments of this disclosure, calculating the estimated first road adhesion coefficient based on the tire longitudinal characteristics includes:

[0021] Based on the aforementioned tire longitudinal stiffness, a brush model is constructed to characterize the relationship between tire slip ratio, road adhesion coefficient, vertical load, and tire longitudinal force.

[0022] Based on the initial road surface adhesion coefficient, the estimated value of the tire longitudinal force is calculated according to the brush model;

[0023] The estimated value of the tire rotation speed is calculated based on the torque applied to the tire, the tire moment of inertia, the radius of rotation, and the estimated value of the tire longitudinal force.

[0024] Based on the estimated tire speed and the measured tire speed, the road surface adhesion coefficient update rate is calculated. Based on the road surface adhesion coefficient update rate, the initial road surface adhesion coefficient of the target vehicle is iteratively updated to obtain the longitudinal maximum utilization adhesion coefficient.

[0025] The longitudinal maximum utilization adhesion coefficient is compensated to obtain the first road surface adhesion coefficient estimate of the target vehicle.

[0026] In some embodiments of this disclosure, determining the tire slip angle, tire lateral force, vertical load, and tire lateral stiffness of each tire of the target vehicle includes:

[0027] Obtain the second operating condition parameters of the target vehicle. The second operating condition parameters include at least the vehicle yaw rate, tire rotation angle, longitudinal speed, lateral speed, total mass of the target vehicle, distance from the center of gravity to the front and rear axles, center of gravity height, track width, and tire force data.

[0028] Based on the longitudinal velocity, the lateral velocity, the vehicle yaw rate, and the tire rotation angle, calculate the tire slip angle of each tire of the target vehicle.

[0029] Based on the axle load transfer relationship, the total mass of the target vehicle, the distance from the center of gravity to the front and rear axles, the height of the center of gravity, and the wheelbase, the vertical load of each tire of the target vehicle is calculated.

[0030] By fitting the six-component force data of the tire, the initial tire lateral stiffness of each tire of the target vehicle is obtained, and the tire feature self-learning is performed based on the data collected by the target vehicle during actual operation to update the initial tire lateral stiffness to the final tire lateral stiffness.

[0031] Calculate the tire lateral force based on the measured values ​​of the target vehicle's total mass and lateral acceleration.

[0032] In some embodiments of this disclosure, the method further includes:

[0033] Calculate the ratio of the tire lateral force to the tire slip angle, and determine the ratio of the tire lateral force to the tire slip angle as the second tire equivalent stiffness value;

[0034] Determine whether the equivalent stiffness value of the second tire is less than the second preset critical stiffness value, wherein the second preset critical stiffness value is the ratio of the tire lateral force to the tire slip angle under the preset critical condition;

[0035] If so, then determine the lateral dynamic estimation conditions for the tire lateral characteristics triggering the road adhesion coefficient.

[0036] In some embodiments of this disclosure, calculating the estimated second road surface adhesion coefficient based on the tire lateral characteristics includes:

[0037] Based on the tire lateral stiffness, a brush model is constructed to characterize the relationship between tire slip angle, road adhesion coefficient, vertical load and tire lateral force.

[0038] Based on the initial road surface adhesion coefficient, the estimated value of the tire lateral force is calculated according to the brush model;

[0039] Based on the total mass of the target vehicle and the estimated value of the tire lateral force, calculate the estimated value of the lateral acceleration.

[0040] Based on the estimated value of the lateral acceleration and the measured value of the lateral acceleration, the road surface adhesion coefficient update rate is calculated. Based on the road surface adhesion coefficient update rate, the initial road surface adhesion coefficient of the target vehicle is iteratively updated to obtain the maximum lateral utilization adhesion coefficient.

[0041] The second road surface adhesion coefficient of the target vehicle is estimated by calculating the compensation for the maximum lateral adhesion coefficient.

[0042] In some embodiments of this disclosure, determining the estimated road surface adhesion coefficient of the target vehicle based on the first estimated road surface adhesion coefficient and / or the second estimated road surface adhesion coefficient includes:

[0043] The first road surface adhesion coefficient estimate of the target vehicle is determined as the final road surface adhesion coefficient estimate of the target vehicle; or,

[0044] The second road surface adhesion coefficient estimate of the target vehicle is determined as the final road surface adhesion coefficient estimate of the target vehicle; or,

[0045] The first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate are combined to obtain the final road surface adhesion coefficient estimate of the target vehicle.

[0046] In some embodiments of this disclosure, the step of fusing the first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate to obtain the final road surface adhesion coefficient estimate of the target vehicle includes:

[0047] Based on the tire slip ratio and the tire sideslip angle, the confidence levels of the first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate are calculated using normalization.

[0048] The final estimated road surface adhesion coefficient of the target vehicle is determined by summing the products of the first and second road surface adhesion coefficient estimates and their corresponding confidence levels.

[0049] According to a second aspect of this disclosure, a tire-road adhesion coefficient estimation device is provided, the device comprising:

[0050] The first determining module is used to determine the longitudinal and lateral characteristics of each tire of the target vehicle. The longitudinal characteristics of the tire include at least tire slip ratio, tire longitudinal force, vertical load, and tire longitudinal stiffness. The lateral characteristics of the tire include at least tire slip angle, tire lateral force, vertical load, and tire lateral stiffness. The longitudinal stiffness and the lateral stiffness of the tire are obtained through tire feature self-learning.

[0051] The first calculation module is used to respond to the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the tire longitudinal characteristics and calculate a first road adhesion coefficient estimate based on the tire longitudinal characteristics. The longitudinal dynamic estimation condition is that the first tire equivalent stiffness value is less than a first preset critical stiffness value. The first tire equivalent stiffness value is calculated based on the tire longitudinal force and the tire slip ratio in the tire longitudinal characteristics.

[0052] The second calculation module is used to respond to the lateral dynamic estimation condition of the road adhesion coefficient triggered by the tire lateral features, and to calculate a second road adhesion coefficient estimate based on the tire lateral features. The lateral dynamic estimation condition is that the second tire equivalent stiffness value is less than a second preset critical stiffness value. The second tire equivalent stiffness value is calculated based on the tire lateral force and the tire slip angle in the tire lateral features.

[0053] The second determining module is used to determine the final road adhesion coefficient estimate of the target vehicle based on the first road adhesion coefficient estimate and / or the second road adhesion coefficient estimate.

[0054] According to a third aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect described above.

[0055] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are used to cause a computer to perform the method of the first aspect described above.

[0056] According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method as described in the first aspect above.

[0057] According to a sixth aspect of this disclosure, a vehicle is provided, comprising at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method as described in the first aspect above.

[0058] The tire road adhesion coefficient estimation method, apparatus, electronic equipment, and vehicle disclosed herein first determine the longitudinal and lateral characteristics of each tire of the target vehicle; further, when the longitudinal characteristics of the tire trigger the longitudinal dynamic estimation condition for the road adhesion coefficient, a first road adhesion coefficient estimate is calculated based on the tire longitudinal characteristics; and when the lateral characteristics of the tire trigger the lateral dynamic estimation condition for the road adhesion coefficient, a second road adhesion coefficient estimate is calculated based on the tire lateral characteristics; finally, the final road adhesion coefficient estimate of the target vehicle is determined based on the first and / or second road adhesion coefficient estimates. Through the scheme of this disclosure, by pre-determining whether the longitudinal characteristics of the tire trigger the longitudinal dynamic estimation condition for the road adhesion coefficient, and whether the lateral characteristics of the tire trigger the lateral dynamic estimation condition for the road adhesion coefficient, it is ensured that the tire road adhesion coefficient is estimated only when the tire slip ratio and / or tire sideslip angle are greater than a certain threshold. This avoids adhesion coefficient deviations caused by slip ratio and sideslip angle estimation errors under small slip and small sideslip conditions, and also solves adhesion coefficient estimation errors under high adhesion and insufficient excitation conditions.

[0059] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0060] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a schematic flowchart illustrating a method for estimating tire road surface adhesion coefficient provided in an embodiment of this disclosure.

[0063] Figure 2 A schematic diagram illustrating the principle of tire road surface adhesion coefficient estimation provided in this embodiment of the disclosure;

[0064] Figure 3 A schematic flowchart illustrating a method for estimating tire road surface adhesion coefficient according to another embodiment of this disclosure;

[0065] Figure 4This is a schematic flowchart illustrating a method for estimating tire road surface adhesion coefficient according to an embodiment of this disclosure.

[0066] Figure 5 This is a schematic diagram of a tire longitudinal stiffness self-learning method provided in an embodiment of the present disclosure;

[0067] Figure 6 This is a schematic diagram of a triggering strategy based on tire longitudinal saturation provided in an embodiment of this disclosure;

[0068] Figure 7 This is a schematic flowchart illustrating a method for estimating tire road surface adhesion coefficient according to an embodiment of this disclosure.

[0069] Figure 8 A schematic diagram illustrating a tire lateral stiffness self-learning method provided in an embodiment of this disclosure;

[0070] Figure 9 This is a schematic diagram of a triggering strategy based on tire lateral saturation provided in an embodiment of the present disclosure;

[0071] Figure 10 This is a schematic diagram of the structure of a tire road surface adhesion coefficient estimation device provided in an embodiment of the present disclosure;

[0072] Figure 11 This is a schematic diagram of a tire road surface adhesion coefficient estimation device provided in an embodiment of the present disclosure. Detailed Implementation

[0073] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0074] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and vehicle for estimating tire road surface adhesion coefficient according to embodiments of the present disclosure.

[0075] In related technologies, the road adhesion coefficient is mainly predicted continuously through tire dynamics models. This method is highly dependent on the accuracy of the tire model and the accuracy of vehicle state estimation. Since tire characteristics change during use due to wear, temperature, tire pressure, etc., the estimation accuracy is affected. Furthermore, because vehicle speed estimation largely originates from wheel speed, the estimated slip ratio under small slip conditions will have a large estimation error, leading to a significantly inaccurate road adhesion coefficient estimate. When the road adhesion coefficient is high, it is difficult to achieve sufficient excitation (the slip ratio or sideslip angle is sufficient to differentiate tire characteristic curves under similar adhesion coefficients). Even with significant slippage or sideslip, the tire characteristic curves under different adhesion coefficients are not clearly distinguishable, resulting in a large error in the adhesion coefficient estimation.

[0076] To address the aforementioned technical problems, this disclosure provides a method, apparatus, electronic device, and vehicle for estimating tire road surface adhesion coefficient. By pre-determining whether the longitudinal characteristics of the tire trigger the longitudinal dynamic estimation condition for the road surface adhesion coefficient, and whether the lateral characteristics of the tire trigger the lateral dynamic estimation condition for the road surface adhesion coefficient, it can ensure that the tire road surface adhesion coefficient is estimated only when the tire slip ratio and / or tire sideslip angle are greater than a certain threshold. This avoids adhesion coefficient deviations caused by slip ratio and sideslip angle estimation errors under small slip and small sideslip conditions, and also solves the adhesion coefficient estimation error under high adhesion and insufficient excitation conditions.

[0077] like Figure 1 The embodiments of this disclosure provide a method for estimating the tire-road adhesion coefficient, including:

[0078] Step 101: Determine the longitudinal and lateral characteristics of each tire of the target vehicle.

[0079] Among them, the longitudinal characteristics of the tire include at least the tire slip ratio, the longitudinal force of the tire, the vertical load, and the longitudinal stiffness of the tire; the lateral characteristics of the tire include at least the tire slip angle, the lateral force of the tire, the vertical load, and the lateral stiffness of the tire. The longitudinal stiffness and the lateral stiffness of the tire are obtained through tire feature self-learning.

[0080] For embodiments of this disclosure, such as Figure 2As shown, to achieve road surface adhesion coefficient estimation, this invention mainly includes road surface adhesion coefficient estimation based on longitudinal dynamics, road surface adhesion coefficient estimation based on lateral dynamics, and longitudinal-lateral fusion. The road surface adhesion coefficient estimation based on longitudinal dynamics utilizes the characteristic curve between tire slip ratio and tire longitudinal force to estimate the road surface adhesion coefficient; the road surface adhesion coefficient estimation module based on lateral dynamics utilizes the characteristic curve between tire slip angle and wheel lateral force to estimate the road surface adhesion coefficient; the longitudinal-lateral fusion module obtains a fused road surface adhesion coefficient based on the triggering state of the longitudinal / lateral modules, the estimated road surface adhesion coefficient value, and the confidence level of the estimated value. Furthermore, both the road surface adhesion coefficient estimation based on longitudinal dynamics and the road surface adhesion coefficient estimation based on lateral dynamics include four steps: tire characteristic self-learning, triggering condition judgment, adhesion coefficient estimation, and estimated value compensation. Where μ x_com μ y_com These are the pavement adhesion coefficient values ​​estimated and compensated based on longitudinal dynamics and lateral dynamics, respectively, i.e., the first pavement adhesion coefficient estimate and the second pavement adhesion coefficient estimate, μ. fusion It is the final road surface adhesion coefficient value obtained by combining two according to a certain fusion rule, that is, the final road surface adhesion coefficient estimate of each tire.

[0081] Step 102: In response to the longitudinal dynamic estimation condition of the road surface adhesion coefficient triggered by the tire longitudinal characteristics, calculate the first road surface adhesion coefficient estimate based on the tire longitudinal characteristics.

[0082] The longitudinal dynamic estimation condition is that the equivalent stiffness value of the first tire is less than the first preset critical stiffness value. The equivalent stiffness value of the first tire is calculated based on the longitudinal force and slip ratio of the tire in the longitudinal characteristics of the tire.

[0083] In this embodiment of the disclosure, the estimation of the first road surface adhesion coefficient based on longitudinal dynamic estimation conditions mainly includes four parts: tire characteristic self-learning, trigger condition judgment, adhesion coefficient estimation, and estimation value compensation. Among them, adhesion coefficient estimation is the main part, which uses the characteristic curve between tire slip ratio and wheel longitudinal force to estimate the road surface adhesion coefficient.

[0084] Step 103: In response to the lateral dynamic estimation condition of the road adhesion coefficient triggered by the tire lateral features, calculate the second road adhesion coefficient estimate based on the tire lateral features.

[0085] The lateral dynamics estimation condition is that the equivalent stiffness value of the second tire is less than the second preset critical stiffness value. The equivalent stiffness value of the second tire is calculated based on the tire lateral force and tire slip angle in the tire lateral characteristics.

[0086] In this embodiment of the disclosure, the estimation of the second road surface adhesion coefficient based on lateral dynamics estimation conditions mainly includes four parts: tire characteristic self-learning, trigger condition judgment, and adhesion coefficient estimation. Among them, adhesion coefficient estimation is the main part, which uses the characteristic curve between the tire slip angle and the wheel lateral force to fit the road surface adhesion coefficient.

[0087] Step 104: Determine the final road adhesion coefficient estimate for each tire of the target vehicle based on the first road adhesion coefficient estimate and / or the second road adhesion coefficient estimate.

[0088] In this embodiment of the present disclosure, a road surface adhesion coefficient estimation strategy based on longitudinal and lateral coupling is proposed. The road surface adhesion coefficient can be estimated from both longitudinal dynamics and lateral dynamics. The estimation values ​​are then fused according to the triggering conditions of the two modules and the confidence level of the estimation values ​​to obtain the final road surface adhesion coefficient estimate.

[0089] In summary, the tire road adhesion coefficient estimation method provided in this application, by pre-determining whether the longitudinal characteristics of the tire trigger the longitudinal dynamic estimation condition of the road adhesion coefficient, and whether the lateral characteristics of the tire trigger the lateral dynamic estimation condition of the road adhesion coefficient, can ensure that the tire road adhesion coefficient is estimated only when the tire slip ratio and / or tire sideslip angle are greater than a certain threshold. This can avoid the adhesion coefficient deviation caused by the estimation error of slip ratio and sideslip angle under small slip and small sideslip conditions, and can also solve the adhesion coefficient estimation error under high adhesion and insufficient excitation conditions.

[0090] Furthermore, as a refinement and extension of the above embodiments, in order to fully illustrate the specific implementation process of the method in this embodiment, this embodiment provides the following: Figure 3 The specific method shown includes:

[0091] 201a. Obtain the first operating condition parameters of the target vehicle. The first operating condition parameters shall include at least the measured values ​​of vehicle speed, tire speed, total vehicle mass, distance from the center of gravity to the front axle and rear axle, center of gravity height, track width, and tire force data.

[0092] 202a. Calculate the tire slip ratio of each tire of the target vehicle based on the measured values ​​of vehicle speed and tire rotation speed. Calculate the vertical load of each tire of the target vehicle based on the axle load transfer relationship, the total mass of the target vehicle, the distance from the center of gravity to the front and rear axles, the height of the center of gravity, and the track width.

[0093] For embodiments of this disclosure, such as Figure 4 As shown, the tire slip ratio of each tire of the target vehicle can be calculated based on the measured tire rotation speed and vehicle speed, as shown in the following expression:

[0094]

[0095] In the formula, s is the tire slip ratio, w represents the wheel speed; v x Represents longitudinal velocity; i = fl, fr, rl, rr represent the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively.

[0096] Secondly, the vertical loads of each tire of the target vehicle can be obtained by solving the axle load transfer relationship. The specific expression is as follows:

[0097]

[0098]

[0099]

[0100]

[0101] In the formula, F zfl F zfr F zrl F zrr denoted by , respectively, the vertical loads on the left front wheel, right front wheel, left rear wheel, and right rear wheel; m is the total mass of the target vehicle; g and a are the vertical loads on the target vehicle. x a y These are gravitational acceleration, longitudinal acceleration, and lateral acceleration, respectively; f l r h represents the distance from the center of mass to the front and rear axles, respectively. g The height of the center of mass; t w This refers to the wheel track.

[0102] 203a. By fitting the six-component force data of the tire, the initial longitudinal stiffness of each tire of the target vehicle is obtained, and the tire feature self-learning is performed based on the data collected by the target vehicle during actual operation to update the initial longitudinal stiffness of the tire to the final longitudinal stiffness; the longitudinal force of the tire is calculated based on the torque applied to the tire, the moment of inertia of the tire, the radius of rotation, and the speed of the tire.

[0103] In this embodiment of the disclosure, the longitudinal dynamic characteristics of the tire can be obtained from the tire's six-component force data and fitted using empirical models (magic formula, Utire model, Dugoff model, brush model, etc.). Considering the tire fitting accuracy, the complexity of the expression, and the resource consumption of the solution, the brush model is used to fit the relationship between the tire slip angle, road adhesion coefficient, vertical load, and tire lateral force, and its specific expression is as follows.

[0104]

[0105] In the formula, F xF z These represent the longitudinal force and vertical load of the tire, respectively, where s is the tire slip ratio, and c is the longitudinal force and vertical load of the tire. x μ is the longitudinal stiffness of the tire. x Let c be the longitudinal road surface adhesion coefficient. Based on the brush model, only c is used. x This allows the tire's longitudinal characteristics to be fully reflected, thus enabling self-learning of the tire model with relatively small computational resources.

[0106] In real-world operating conditions, tire stiffness varies due to factors such as wear, tire pressure, and temperature. Using the original brush model for road adhesion coefficient estimation would lead to significant errors. Therefore, tire characteristic self-learning has strong engineering application value.

[0107] Because this patent uses a brush model, only the tire longitudinal stiffness needs to be self-learned to correct the tire longitudinal dynamics model. The tire characteristic self-learning function is automatically activated each time the vehicle is powered on. After collecting a certain amount of valid data, an approximate value of the tire stiffness is obtained based on the least squares principle, and the brush model is updated. After completing the tire characteristic self-learning, the module will be inactive and will wait to restart the next time the vehicle is powered on. The specific implementation process is as follows: Figure 5 As shown.

[0108] The tire characteristic self-learning function can, when the road adhesion coefficient is unknown, rely on the principle that under low slip ratio conditions, the ratio of longitudinal force (estimated by a Kalman filter) to slip ratio is approximately equal to the tire's longitudinal stiffness (e.g., ...). Figure 6 As shown, the slope of line b represents the tire's longitudinal stiffness, and an approximate value for tire stiffness is obtained through fitting. Therefore, the selection of effective data must adhere to the following conditions:

[0109] 1. The longitudinal force must be within the specified range. If it is too large, the tire characteristic curve will gradually deviate from the slope representing stiffness, and the fitted slope will be significantly smaller than the actual tire stiffness. If it is too small, it will be affected by the inaccurate estimation of slip ratio under small slip conditions.

[0110] 2. Vehicle speed exceeds a certain threshold. When the vehicle speed is too low, the estimated slip ratio will contain a large error.

[0111] 3. The tire turning angle (or steering wheel turning angle) is less than a certain threshold. When the vehicle turns, under the influence of the lateral force, the maximum longitudinal utilization coefficient of the tire will decrease, which will cause the tire characteristic curve to gradually deviate from the slope representing stiffness, resulting in a smaller stiffness value obtained through self-learning.

[0112] 204a. In response to the longitudinal dynamic estimation conditions of the road adhesion coefficient triggered by the longitudinal characteristics of the tire, a brush model is constructed based on the longitudinal stiffness of the tire to characterize the relationship between the tire slip ratio, the road adhesion coefficient, the vertical load and the longitudinal force of the tire; based on the initial road adhesion coefficient, the estimated value of the tire longitudinal force is calculated according to the brush model; based on the torque applied to the tire, the tire moment of inertia, the radius of rotation and the estimated value of the tire longitudinal force, the estimated value of the tire speed is calculated.

[0113] The longitudinal dynamic estimation condition is that the equivalent stiffness value of the first tire is less than the first preset critical stiffness value. The equivalent stiffness value of the first tire is calculated based on the longitudinal force and slip ratio of the tire in the longitudinal characteristics of the tire.

[0114] Before executing the steps of this embodiment, it is necessary to determine whether the tire longitudinal characteristics trigger the longitudinal dynamic estimation condition of the road adhesion coefficient. When determining whether the tire longitudinal characteristics trigger the longitudinal dynamic estimation condition of the road adhesion coefficient, step 204a of this embodiment is further executed. Accordingly, when determining whether the tire longitudinal characteristics trigger the longitudinal dynamic estimation condition of the road adhesion coefficient, the specific steps of this embodiment may include: calculating the ratio of tire longitudinal force to tire slip ratio, and determining the ratio of tire longitudinal force to tire slip ratio as the first tire equivalent stiffness value; determining whether the first tire equivalent stiffness value is less than the first preset critical stiffness value, wherein the first preset critical stiffness value is the ratio of tire longitudinal force to tire slip ratio under the preset critical condition; if so, it is determined that the tire longitudinal characteristics trigger the longitudinal dynamic estimation condition of the road adhesion coefficient.

[0115] For embodiments of this disclosure, the longitudinal force F of the tire is solved according to the tire rotation model shown in the following formula. xi .

[0116]

[0117] In the formula, J and r are the moment of inertia and radius of rotation of the wheel, respectively, and T i The torque applied to the wheel.

[0118] The main purpose of the trigger condition judgment logic is to avoid obtaining a road surface adhesion coefficient estimate with large errors.

[0119] Firstly, considering that under small slip conditions, the vehicle state estimation algorithm and onboard sensor equipment limit the accuracy of slip ratio estimation. Furthermore, a low slip ratio indicates that the road surface provides sufficient longitudinal force and will not significantly affect vehicle handling stability. Therefore, a high-adhesion road adhesion coefficient estimate can be continuously output without triggering the estimation module. In summary, the estimation module is only triggered when the tire slip ratio exceeds a certain threshold.

[0120] like Figure 6 As shown, if a fixed lower limit of the slip ratio (line a in the figure below) is used as the triggering condition, large estimation errors can easily occur under medium-to-high adhesion conditions because the tire characteristic curves under different adhesion coefficients are not clearly distinguishable (within the dashed box in the figure below). Therefore, improvements are needed. Considering that this module mainly estimates the road adhesion coefficient based on the tire longitudinal characteristic curves under different road adhesion coefficients, in order to obtain a more accurate estimate, it is necessary to make the tire characteristics under different road adhesion coefficients more clearly distinguishable. Therefore, a triggering strategy based on tire saturation (tire saturation is the ratio of the provided force to the maximum force that can be provided) is introduced. As shown in the figure below, line c is the sloping line representing the critical stiffness (critical stiffness is the ratio of longitudinal force to slip ratio under a set critical condition). When the tire equivalent stiffness value (equivalent stiffness is the ratio of longitudinal force to slip ratio under any condition) is less than the critical stiffness value, the tire saturation reaches the set threshold (the area to the right of line c). At this time, the distinction between tire characteristic curves under different road adhesion coefficients is more obvious, and a relatively accurate road adhesion coefficient value can be estimated. In addition, the saturation threshold set in this scheme can also ensure that the estimation module can be triggered before systems such as ABS and TCS, and the estimated road adhesion coefficient value can be used to assist in adjusting the vehicle control strategy.

[0121] 205a. Based on the estimated tire speed and the measured tire speed, calculate the road surface adhesion coefficient update rate. Based on the road surface adhesion coefficient update rate, iteratively update the initial road surface adhesion coefficient of the target vehicle to obtain the longitudinal maximum utilization adhesion coefficient. Perform compensation calculation on the longitudinal maximum utilization adhesion coefficient to obtain the first road surface adhesion coefficient estimate of the target vehicle.

[0122] In this embodiment of the disclosure, firstly, based on the initial road surface adhesion coefficient, the estimated value of the tire longitudinal force is solved according to the brush model, and then the estimated value of the tire speed is calculated; the difference between the estimated value and the measured value of the tire speed is used to form the road surface adhesion coefficient update rate according to a certain gain, and finally the estimated value of the road surface adhesion coefficient is iteratively updated. The specific iterative logic is shown in the following formula:

[0123] predict:

[0124] Iterative updates:

[0125] In the formula, ω i [k+1] represents the estimated and measured tire speeds at time k+1, respectively; T is the calculation period; and R is the iteration update rate.

[0126] The theoretically obtained estimate above represents the maximum longitudinal adhesion coefficient. However, when there is a large lateral force (lateral acceleration), this value deviates from the actual road adhesion coefficient and requires further compensation. The specific compensation strategy is as follows:

[0127]

[0128] The embodiment step 201b, which is parallel to the embodiment step 201a, is to obtain the second operating condition parameters of the target vehicle. The second operating condition parameters include at least the vehicle yaw rate, tire rotation angle, longitudinal speed, lateral speed, the total mass of the target vehicle, the distance from the center of gravity to the front axle and the rear axle, the height of the center of gravity, the wheel track, and the tire six-component force data.

[0129] 202b. Based on longitudinal velocity, lateral velocity, vehicle yaw rate and tire rotation angle, calculate the tire slip angle of each tire of the target vehicle. According to the axle load transfer relationship, as well as the total mass of the target vehicle, the distance from the center of gravity to the front and rear axles, the height of the center of gravity and the track width, calculate the vertical load of each tire of the target vehicle.

[0130] For the embodiments of this disclosure, the tire slip angle of each tire of the target vehicle can be solved according to the following formula.

[0131]

[0132] In the formula, α fl α fr α rl α rr These represent the sideslip angles of the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; v x v y These represent the longitudinal velocity and the lateral velocity, respectively; θ is the vehicle yaw rate; and δ is the tire rotation angle.

[0133] Secondly, the vertical loads of each tire of the target vehicle can be obtained by solving the axle load transfer relationship, and the specific expression is as follows.

[0134]

[0135]

[0136]

[0137]

[0138] In the formula, F zfl F zfr F zrl F zrr These represent the vertical loads on the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively; m is the total mass of the target vehicle; g and a are the vertical loads on the target vehicle.x a y These are gravitational acceleration, longitudinal acceleration, and lateral acceleration, respectively; f l r h represents the distance from the center of mass to the front and rear axles, respectively. g The height of the center of mass; t w This refers to the wheel track.

[0139] 203b. By fitting the six-component force data of the tires, the initial tire lateral stiffness of each tire of the target vehicle is obtained. Based on the data collected by the target vehicle during actual operation, tire feature self-learning is performed to update the initial tire lateral stiffness to the final tire lateral stiffness. The tire lateral force is calculated based on the measured values ​​of the total vehicle mass and lateral acceleration of the target vehicle.

[0140] In this embodiment, the lateral dynamic characteristics of the tire can be obtained from the tire's six-component force data and fitted using empirical models (magic formula, Utire model, Dugoff model, brush model, etc.). Considering the tire fitting accuracy, the complexity of the expression, and the resource consumption of the solution, the brush model is used to fit the tire's longitudinal characteristics and estimate the road adhesion coefficient. Its specific expression is as follows:

[0141]

[0142] In the formula, F y F z These represent the tire lateral force and vertical load, respectively, where α is the tire side slip, and c is the tire lateral force. y For tire lateral stiffness, μ y Let c be the lateral road surface adhesion coefficient. Based on the brush model, only c is used. y This fully reflects the longitudinal characteristics of the tire, which is very beneficial for tire characteristic self-learning and subsequent road adhesion coefficient estimation.

[0143] like Figure 7 The diagram shows a flowchart for estimating the road surface adhesion coefficient based on a lateral dynamics model.

[0144] In real-world operating conditions, tire stiffness varies due to factors such as wear, tire pressure, and temperature. Using the original brush model for road adhesion coefficient estimation would lead to significant errors. Therefore, tire characteristic self-learning has strong engineering application value.

[0145] Because this patent uses a brush model, only self-learning the tire lateral stiffness is needed to correct the tire lateral dynamics model. The tire characteristic self-learning function is automatically activated each time the vehicle is powered on. After collecting a certain amount of valid data, an approximate value of the tire stiffness is obtained based on the least squares principle, and the brush model is updated. After completing tire characteristic self-learning, this module will be inactive and will wait to restart the next time the vehicle is powered on. The specific implementation process of tire characteristic self-learning under this module is as follows: Figure 8 As shown.

[0146] The tire characteristic self-learning function can, when the road surface adhesion coefficient is unknown, learn from the principle that under small sideslip conditions, the ratio of lateral force to sideslip angle is approximately equal to the tire's lateral stiffness (e.g., ...). Figure 9 As shown, the slope of line b (representing tire lateral stiffness) is used to fit an approximate value of tire stiffness. Therefore, the selection of effective data must meet the following conditions:

[0147] 1. Lateral acceleration must be within a specified range. If it is too large, the tire characteristic curve will gradually deviate from the slope representing stiffness, and the fitted slope will be significantly smaller than the actual tire stiffness. If it is too small, the estimation noise of the slip ratio will have a significant impact on the fitting result under small slip conditions.

[0148] 2. The longitudinal acceleration (or steering wheel angle) is less than a certain threshold. When the vehicle accelerates or brakes, the maximum lateral adhesion coefficient of the tire will decrease due to the influence of the longitudinal force, which will cause the tire characteristic curve to gradually deviate from the slope representing stiffness, resulting in a smaller stiffness value obtained through self-learning.

[0149] 204b. In response to the lateral dynamic estimation conditions of the road adhesion coefficient triggered by the tire lateral characteristics, a brush model is constructed based on the tire lateral stiffness to characterize the relationship between the tire slip angle, road adhesion coefficient, vertical load and tire lateral force; the estimated value of the tire lateral force is calculated based on the initial road adhesion coefficient and the brush model; the estimated value of the lateral acceleration is calculated based on the total mass of the target vehicle and the estimated value of the tire lateral force.

[0150] The lateral dynamics estimation condition is that the equivalent stiffness value of the second tire is less than the second preset critical stiffness value. The equivalent stiffness value of the second tire is calculated based on the tire lateral force and tire slip angle in the tire lateral characteristics.

[0151] Before executing the steps of this embodiment, it is necessary to determine whether the tire lateral features trigger the lateral dynamics estimation condition for the road adhesion coefficient. When determining whether the tire lateral features trigger the lateral dynamics estimation condition for the road adhesion coefficient, step 204b of this embodiment is further executed. Accordingly, when determining whether the tire lateral features trigger the lateral dynamics estimation condition for the road adhesion coefficient, the specific steps of this embodiment may include: calculating the ratio of the tire lateral force to the tire slip angle, and determining the ratio of the tire lateral force to the tire slip angle as the second tire equivalent stiffness value; determining whether the second tire equivalent stiffness value is less than the second preset critical stiffness value, wherein the second preset critical stiffness value is the ratio of the tire lateral force to the tire slip angle under the preset critical condition; if so, it is determined that the tire lateral features trigger the lateral dynamics estimation condition for the road adhesion coefficient.

[0152] The main purpose of the trigger condition judgment logic is to avoid obtaining a road surface adhesion coefficient estimate with large errors.

[0153] Firstly, considering that under small sideslip conditions, the estimation noise of the slip ratio has a significant impact on the fitting results due to limitations in the vehicle state estimation algorithm and onboard sensor equipment. Furthermore, a small sideslip angle indicates that the road surface provides sufficient lateral force and will not significantly affect vehicle handling stability. Therefore, a highly attached road adhesion coefficient estimate can be continuously output without triggering the estimation module. In summary, the estimation module is only triggered when the tire sideslip angle exceeds a certain threshold.

[0154] like Figure 9 As shown, if a fixed lower limit of the slip angle (line a in the figure below) is used as the trigger condition, large estimation errors can easily occur under medium-to-high adhesion conditions because the tire characteristic curves under different adhesion coefficients are not clearly distinguishable (within the dashed box in the figure below). Therefore, improvements are needed. Considering that this module mainly estimates the road adhesion coefficient based on the tire lateral characteristic curves under different road adhesion coefficients, in order to obtain a more accurate estimate, it is necessary to make the tire characteristics under different road adhesion coefficients more clearly distinguishable. Therefore, a triggering strategy based on tire saturation (tire saturation is the ratio of the provided force to the maximum force that can be provided) is introduced. As shown in the figure below, line c is the oblique line representing the critical stiffness (critical stiffness is the ratio of lateral force to slip angle under the set critical condition). When the tire equivalent stiffness value (equivalent stiffness is the ratio of lateral force to slip angle under any condition) is less than the critical stiffness value, the tire saturation reaches the set threshold (the area to the right of line c). At this time, the distinction between tire characteristic curves under different road adhesion coefficients is more obvious, and a relatively accurate road adhesion coefficient value can be estimated.

[0155] 205b. Based on the estimated value of lateral acceleration and the measured value of lateral acceleration, calculate the road surface adhesion coefficient update rate. Based on the road surface adhesion coefficient update rate, iteratively update the initial road surface adhesion coefficient of the target vehicle to obtain the maximum lateral utilization adhesion coefficient. Compensate the maximum lateral utilization adhesion coefficient to obtain the second road surface adhesion coefficient estimate of the target vehicle.

[0156] In the embodiments of this disclosure, when estimating the road adhesion coefficient based on longitudinal dynamics, the road adhesion coefficient can be iterated by measuring the rotational speed of each tire of the target vehicle. Therefore, a road adhesion coefficient can be estimated for each tire of each target vehicle. However, when estimating the road adhesion coefficient based on lateral dynamics, there is no measurable physical quantity that is one-to-one related to the lateral force of each tire. The road adhesion coefficient can only be iterated based on the lateral acceleration. Therefore, only a road adhesion coefficient value can be obtained from the whole vehicle level, rather than a road adhesion coefficient value corresponding to each of the four tires.

[0157] Based on the initial road adhesion coefficient μ y The estimated lateral forces of each tire of the target vehicle are obtained by solving the brush model. The estimated value of lateral acceleration is obtained based on the three-degree-of-freedom model of the vehicle.

[0158]

[0159] For embodiments of this disclosure, based on the estimated value of lateral acceleration Compared with the measured value a y The difference between them is used to form the road surface adhesion coefficient update rate according to a certain gain, and finally the estimated value of the road surface adhesion coefficient is updated iteratively.

[0160]

[0161] The estimated value is theoretically the maximum lateral adhesion coefficient. However, when there is a large longitudinal force (longitudinal acceleration), there is a certain deviation between this value and the road adhesion coefficient, which needs to be further compensated. The specific compensation strategy is as follows.

[0162]

[0163] 206. Determine the final estimated value of the road surface adhesion coefficient for the target vehicle based on the first estimated value of the road surface adhesion coefficient and / or the second estimated value of the road surface adhesion coefficient.

[0164] As one possible approach, in response to the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the tire longitudinal characteristics, after calculating the first road adhesion coefficient estimate based on the tire longitudinal characteristics, the first road adhesion coefficient estimate of the target vehicle can be directly determined as the final road adhesion coefficient estimate of the target vehicle.

[0165] As one possible approach, in response to the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the tire longitudinal characteristics, after calculating the first road adhesion coefficient estimate based on the tire longitudinal characteristics, the second road adhesion coefficient estimate of the target vehicle can be directly determined as the final road adhesion coefficient estimate of the target vehicle.

[0166] As one possible implementation, in response to the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the tire's longitudinal characteristics, and the longitudinal dynamic estimation condition of the road adhesion coefficient triggered simultaneously by the tire's longitudinal characteristics, the steps in this embodiment can be to fuse the calculation of the first road adhesion coefficient estimate and the second road adhesion coefficient estimate to obtain the final road adhesion coefficient estimate of the target vehicle. Accordingly, when fusing the calculation of the first and second road adhesion coefficient estimates to obtain the final road adhesion coefficient estimate of the target vehicle, the specific steps in this embodiment may include: normalizing and calculating the confidence levels of the first and second road adhesion coefficient estimates based on the tire slip ratio and tire sideslip angle; summing the products of the first and second road adhesion coefficient estimates and their corresponding confidence levels to determine the final road adhesion coefficient estimate of the target vehicle.

[0167] In this embodiment of the disclosure, the longitudinal / lateral fusion module obtains a fused road surface adhesion coefficient based on the triggering state of the longitudinal / lateral modules, the estimated road surface adhesion coefficient, and the confidence level of the estimated value. There are four main fusion scenarios:

[0168] 1. The longitudinal estimation module was triggered, but the lateral estimation module was not triggered.

[0169] In this case, the fusion module outputs the road adhesion coefficient value based on the longitudinal dynamic estimation.

[0170] 2. The longitudinal estimation module was not triggered, but the lateral estimation module was triggered.

[0171] In this case, the fusion module outputs the road adhesion coefficient value based on the lateral dynamics estimation.

[0172] 3. Both the longitudinal estimation module and the lateral estimation module are triggered.

[0173] In this case, the fusion module performs fusion based on the confidence levels of the road adhesion coefficient estimates obtained from longitudinal and lateral dynamics. Since both modules estimate the road adhesion coefficient using tire characteristic curves under different adhesion coefficients, theoretically, a larger slip ratio / side slip angle results in greater differentiation between tire characteristic curves under different road adhesion coefficients, leading to higher accuracy in the estimated road adhesion coefficient value. Therefore, μ can be determined based on the degree of slip ratio / side slip angle.x_com and μ y_com The confidence level. The specific fusion strategy is as follows:

[0174]

[0175] In the formula, μ fusion_i α represents the road adhesion coefficient at the four wheels obtained by fusion. f This refers to the front axle side slip angle;

[0176] μ x_com_i This represents the road adhesion coefficient of the four wheels estimated based on the tire longitudinal dynamics model; s norm and α norm These are the normalized coefficients for slip ratio and sideslip angle, respectively. i and α f Unify them to the same order of magnitude.

[0177] 4. Neither the longitudinal estimation module nor the lateral estimation module was triggered.

[0178] In this case, the fusion module outputs a larger set road adhesion coefficient value, which means that the road surface is a high-adhesion road surface by default.

[0179] In summary, based on the tire-road adhesion coefficient estimation method provided in this disclosure, a tire self-learning strategy is proposed, which effectively solves the problem of the high dependence of tire model accuracy on the tire dynamics estimation strategy. In addition, a brush model is used for fitting, and the tire stiffness can be fitted with less computation using the least squares method, thus fully representing the tire characteristics. By pre-determining whether the longitudinal characteristics of the tire trigger the longitudinal dynamics estimation condition of the road adhesion coefficient, and whether the lateral characteristics of the tire trigger the lateral dynamics estimation condition of the road adhesion coefficient, it can be ensured that the tire-road adhesion coefficient is estimated only when the tire slip ratio and / or the tire sideslip angle are greater than a certain threshold, thereby avoiding the prediction deviation under small slip conditions. It can also solve the estimation error caused by the low discrimination of the tire characteristic curve before the slip ratio and sideslip angle reach a sufficiently large level under high adhesion road conditions. Furthermore, a road adhesion coefficient estimation strategy based on longitudinal and lateral coupling is proposed, which can estimate the road adhesion coefficient from both longitudinal dynamics and lateral dynamics perspectives, and fuse them according to the triggering conditions of the two modules and the confidence level of the estimated values.

[0180] Based on the above Figure 1 , Figure 3 The specific implementation of the method shown in this embodiment provides a tire road surface adhesion coefficient estimation device, such as... Figure 10 As shown, the device includes: a first determining module 31, a first calculating module 32, a second calculating module 33, and a second determining module 34;

[0181] The first determining module 31 is used to determine the longitudinal and lateral features of each tire of the target vehicle. The longitudinal features of the tire include at least tire slip ratio, tire longitudinal force, vertical load, and tire longitudinal stiffness. The lateral features of the tire include at least tire slip angle, tire lateral force, vertical load, and tire lateral stiffness. The longitudinal and lateral stiffness of the tire are obtained through tire feature self-learning.

[0182] The first calculation module 32 is used to respond to the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the longitudinal characteristics of the tire, and to calculate the estimated value of the first road adhesion coefficient based on the longitudinal characteristics of the tire. The longitudinal dynamic estimation condition is that the equivalent stiffness value of the first tire is less than the first preset critical stiffness value. The equivalent stiffness value of the first tire is calculated based on the longitudinal force of the tire and the tire slip ratio in the longitudinal characteristics of the tire.

[0183] The second calculation module 33 is used to respond to the lateral dynamic estimation condition of the road adhesion coefficient triggered by the tire lateral features, and calculate the second road adhesion coefficient estimate based on the tire lateral features. The lateral dynamic estimation condition is that the second tire equivalent stiffness value is less than the second preset critical stiffness value. The second tire equivalent stiffness value is calculated based on the tire lateral force and tire slip angle in the tire lateral features.

[0184] The second determining module 34 is used to determine the final road adhesion coefficient estimate of the target vehicle based on the first road adhesion coefficient estimate and / or the second road adhesion coefficient estimate.

[0185] In specific application scenarios, the first determining module 31 is specifically configured to acquire the first operating condition parameters of the target vehicle. The first operating condition parameters include at least the measured values ​​of vehicle speed and tire rotation speed, the total mass of the target vehicle, the distance from the center of gravity to the front and rear axles, the height of the center of gravity, the wheelbase, and the tire six-component force data; calculate the tire slip ratio of each tire of the target vehicle based on the measured values ​​of vehicle speed and tire rotation speed; calculate the vertical load of each tire of the target vehicle based on the axle load transfer relationship, as well as the total mass of the target vehicle, the distance from the center of gravity to the front and rear axles, the height of the center of gravity, and the wheelbase; obtain the initial tire longitudinal stiffness of each tire of the target vehicle by fitting the tire six-component force data, and update the initial tire longitudinal stiffness to the final tire longitudinal stiffness based on the tire feature self-learning based on the data collected during the actual operation of the target vehicle; and calculate the tire longitudinal force based on the torque applied to the tire, the tire moment of inertia, the rotation radius, and the tire rotation speed.

[0186] In specific application scenarios, such as Figure 11 As shown, the device also includes: a third determining module 35, a first judging module 36, and a fourth determining module 37;

[0187] The third determining module 35 is configured to calculate the ratio of tire longitudinal force to tire slip ratio and determine the ratio of tire longitudinal force to tire slip ratio as the first tire equivalent stiffness value.

[0188] The judgment module 36 is configured to judge whether the equivalent stiffness value of the first tire is less than the first preset critical stiffness value, wherein the first preset critical stiffness value is the ratio of the longitudinal force of the tire to the tire slip ratio under the preset critical condition.

[0189] The fourth determining module 37 is configured to determine the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the longitudinal characteristics of the tire if the equivalent stiffness value of the first tire is less than the first preset critical stiffness value.

[0190] In a specific application scenario, the first calculation module 32 is specifically configured to: construct a brush model based on the tire's longitudinal stiffness to characterize the relationship between tire slip ratio, road surface adhesion coefficient, vertical load, and tire longitudinal force; calculate the estimated value of tire longitudinal force based on the initial road surface adhesion coefficient and the brush model; calculate the estimated value of tire rotation speed based on the torque applied to the tire, tire moment of inertia, rotation radius, and the estimated value of tire longitudinal force; calculate the road surface adhesion coefficient update rate based on the estimated value and the measured value of tire rotation speed; iteratively update the initial road surface adhesion coefficient of the target vehicle based on the road surface adhesion coefficient update rate to obtain the longitudinal maximum utilization adhesion coefficient; and perform compensation calculation on the longitudinal maximum utilization adhesion coefficient to obtain the first road surface adhesion coefficient estimate of the target vehicle.

[0191] In specific application scenarios, the first determining module 31 is specifically configured to acquire the second operating condition parameters of the target vehicle. The second operating condition parameters include at least the vehicle yaw rate, tire rotation angle, longitudinal velocity, lateral velocity, vehicle mass, distance from the center of gravity to the front and rear axles, center of gravity height, track width, and tire six-component force data. Based on the longitudinal velocity, lateral velocity, vehicle yaw rate, and tire rotation angle, the tire slip angle of each tire of the target vehicle is calculated. According to the axle load transfer relationship, as well as the vehicle mass, distance from the center of gravity to the front and rear axles, center of gravity height, and track width of the target vehicle, the vertical load of each tire of the target vehicle is calculated. By fitting the tire six-component force data, the initial tire lateral stiffness of each tire of the target vehicle is obtained, and tire feature self-learning is performed based on the data collected during the actual operation of the target vehicle to update the initial tire lateral stiffness to the final tire lateral stiffness. The tire lateral force is calculated based on the measured values ​​of the vehicle mass and lateral acceleration of the target vehicle.

[0192] In specific application scenarios, such as Figure 11 As shown, the device also includes: a fifth determining module 38, a second judging module 39, and a sixth determining module 310;

[0193] The fifth determining module 38 is configured to calculate the ratio of tire lateral force to tire slip angle and determine the ratio of tire lateral force to tire slip angle as the second tire equivalent stiffness value.

[0194] The second judgment module 39 is configured to judge whether the equivalent stiffness value of the second tire is less than the second preset critical stiffness value, wherein the second preset critical stiffness value is the ratio of the tire lateral force to the tire slip angle under the preset critical condition.

[0195] The sixth determining module 310 is configured to determine the lateral dynamic estimation condition for the tire lateral feature triggering the road adhesion coefficient if the equivalent stiffness value of the second tire is less than the second preset critical stiffness value.

[0196] In a specific application scenario, the second calculation module 33 is specifically configured to: construct a brush model based on tire lateral stiffness to characterize the relationship between tire slip angle, road surface adhesion coefficient, vertical load, and tire lateral force; calculate the estimated value of tire lateral force based on the brush model according to the initial road surface adhesion coefficient; calculate the estimated value of lateral acceleration based on the total mass of the target vehicle and the estimated value of tire lateral force; calculate the road surface adhesion coefficient update rate based on the estimated value of lateral acceleration and the measured value of lateral acceleration; iteratively update the initial road surface adhesion coefficient of the target vehicle based on the road surface adhesion coefficient update rate to obtain the lateral maximum utilization adhesion coefficient; and perform compensation calculation on the lateral maximum utilization adhesion coefficient to obtain the second road surface adhesion coefficient estimate of the target vehicle.

[0197] In specific application scenarios, the second determining module 34 is specifically configured to determine the first road surface adhesion coefficient estimate of the target vehicle as the final road surface adhesion coefficient estimate of the target vehicle; or, determine the second road surface adhesion coefficient estimate of the target vehicle as the final road surface adhesion coefficient estimate of the target vehicle; or, fuse the first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate to obtain the final road surface adhesion coefficient estimate of the target vehicle.

[0198] In a specific application scenario, the second determining module 34 is specifically configured to calculate the confidence levels of the first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate based on the tire slip ratio and tire side slip angle; and to sum the products of the first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate with the corresponding confidence levels to determine the final road surface adhesion coefficient estimate of the target vehicle.

[0199] It should be noted that other corresponding descriptions of the functional units involved in the tire road surface adhesion coefficient estimation device provided in this embodiment can be found in [reference needed]. Figure 1 and Figure 2 The corresponding descriptions in [the document] will not be repeated here.

[0200] Based on the above, Figure 1 and Figure 2 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 and Figure 2 The method shown.

[0201] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0202] Based on the above, Figure 1 and Figure 3 The method shown, and Figure 10 and Figure 11 To achieve the above objectives, this application also provides an electronic device, which can be configured on the end side of a vehicle (such as an electric vehicle), as shown in the virtual device embodiment. The device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to implement the above-described virtual device embodiment. Figure 1 and Figure 3 The method shown.

[0203] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0204] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0205] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0206] Based on the aforementioned electronic device, this application also provides a vehicle, which may specifically include the aforementioned electronic device. The vehicle may specifically be an electric vehicle, etc.

[0207] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. By applying the solution of this embodiment, by pre-determining whether the longitudinal characteristics of the tire trigger the longitudinal dynamic estimation condition of the road surface adhesion coefficient, and whether the lateral characteristics of the tire trigger the lateral dynamic estimation condition of the road surface adhesion coefficient, it can be ensured that the tire road surface adhesion coefficient is estimated only when the tire slip ratio and / or tire sideslip angle are greater than a certain threshold. This can avoid the adhesion coefficient deviation caused by the estimation error of slip ratio and sideslip angle under small slip and small sideslip conditions, and can also solve the adhesion coefficient estimation error under high adhesion and insufficient excitation conditions.

[0208] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0209] The above are merely specific embodiments of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for estimating tire road surface adhesion coefficient, characterized in that, include: Determine the longitudinal and lateral characteristics of each tire of the target vehicle. The longitudinal characteristics include at least tire slip ratio, tire longitudinal force, vertical load, and tire longitudinal stiffness. The lateral characteristics include at least tire slip angle, tire lateral force, vertical load, and tire lateral stiffness. The longitudinal and lateral stiffness are obtained through tire feature self-learning. In response to the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the tire longitudinal characteristics, a first road adhesion coefficient estimate is calculated based on the tire longitudinal characteristics, wherein the longitudinal dynamic estimation condition is that the first tire equivalent stiffness value is less than a first preset critical stiffness value, and the first tire equivalent stiffness value is calculated based on the tire longitudinal force and the tire slip ratio in the tire longitudinal characteristics. In response to the lateral dynamics estimation condition of the road adhesion coefficient triggered by the tire lateral features, a second road adhesion coefficient estimate is calculated based on the tire lateral features, wherein the lateral dynamics estimation condition is that the second tire equivalent stiffness value is less than the second preset critical stiffness value, and the second tire equivalent stiffness value is calculated based on the tire lateral force and the tire slip angle in the tire lateral features; The final estimated value of the road surface adhesion coefficient for the target vehicle is determined based on the first estimated value of the road surface adhesion coefficient and / or the second estimated value of the road surface adhesion coefficient.

2. The method according to claim 1, characterized in that, Determine the tire slip ratio, tire longitudinal force, vertical load, and tire longitudinal stiffness for each tire of the target vehicle, including: Obtain the first operating condition parameters of the target vehicle. The first operating condition parameters include at least the measured values ​​of vehicle speed, tire rotation speed, total vehicle mass, distance from the center of gravity to the front and rear axles, center of gravity height, wheelbase, and tire force data. The tire slip ratio of each tire of the target vehicle is calculated based on the measured values ​​of the vehicle speed and the tire rotation speed. Based on the axle load transfer relationship, as well as the total mass of the target vehicle, the distance from the center of gravity to the front and rear axles, the height of the center of gravity, and the wheelbase, the vertical load of each tire of the target vehicle is calculated. By fitting the six-component force data of the tire, the initial longitudinal stiffness of each tire of the target vehicle is obtained, and the tire feature self-learning is performed based on the data collected by the target vehicle during actual operation to update the initial longitudinal stiffness of the tire to the final longitudinal stiffness. Calculate the longitudinal force of the tire based on the torque applied to the tire, the tire's moment of inertia, the radius of rotation, and the tire's rotational speed.

3. The method according to claim 2, characterized in that, The method further includes: Calculate the ratio of the tire longitudinal force to the tire slip ratio, and determine the ratio of the tire longitudinal force to the tire slip ratio as the first tire equivalent stiffness value; Determine whether the equivalent stiffness value of the first tire is less than the first preset critical stiffness value, wherein the first preset critical stiffness value is the ratio of the longitudinal force of the tire to the tire slip ratio under the preset critical condition; If so, then determine the longitudinal dynamic estimation conditions for the tire longitudinal characteristics triggering the road adhesion coefficient.

4. The method according to claim 3, characterized in that, The calculation of the first road surface adhesion coefficient estimate based on the tire longitudinal characteristics includes: Based on the aforementioned tire longitudinal stiffness, a brush model is constructed to characterize the relationship between tire slip ratio, road adhesion coefficient, vertical load, and tire longitudinal force. Based on the initial road surface adhesion coefficient, the estimated value of the tire longitudinal force is calculated according to the brush model; The estimated value of the tire rotation speed is calculated based on the torque applied to the tire, the tire moment of inertia, the radius of rotation, and the estimated value of the tire longitudinal force. Based on the estimated tire speed and the measured tire speed, the road surface adhesion coefficient update rate is calculated. Based on the road surface adhesion coefficient update rate, the initial road surface adhesion coefficient of the target vehicle is iteratively updated to obtain the longitudinal maximum utilization adhesion coefficient. The longitudinal maximum utilization adhesion coefficient is compensated to obtain the first road surface adhesion coefficient estimate of the target vehicle.

5. The method according to claim 1, characterized in that, Determine the tire slip angle, tire lateral force, vertical load, and tire lateral stiffness for each tire of the target vehicle, including: Obtain the second operating condition parameters of the target vehicle. The second operating condition parameters include at least the vehicle yaw rate, tire rotation angle, longitudinal speed, lateral speed, total mass of the target vehicle, distance from the center of gravity to the front and rear axles, center of gravity height, track width, and tire force data. Based on the longitudinal velocity, the lateral velocity, the vehicle yaw rate, and the tire rotation angle, calculate the tire slip angle of each tire of the target vehicle. Based on the axle load transfer relationship, the total mass of the target vehicle, the distance from the center of gravity to the front and rear axles, the height of the center of gravity, and the wheelbase, the vertical load of each tire of the target vehicle is calculated. By fitting the six-component force data of the tire, the initial tire lateral stiffness of each tire of the target vehicle is obtained, and the tire feature self-learning is performed based on the data collected by the target vehicle during actual operation to update the initial tire lateral stiffness to the final tire lateral stiffness. Calculate the tire lateral force based on the measured values ​​of the target vehicle's total mass and lateral acceleration.

6. The method according to claim 5, characterized in that, The method further includes: Calculate the ratio of the tire lateral force to the tire slip angle, and determine the ratio of the tire lateral force to the tire slip angle as the second tire equivalent stiffness value; Determine whether the equivalent stiffness value of the second tire is less than the second preset critical stiffness value, wherein the second preset critical stiffness value is the ratio of the tire lateral force to the tire slip angle under the preset critical condition; If so, then determine the lateral dynamic estimation conditions for the tire lateral characteristics triggering the road adhesion coefficient.

7. The method according to claim 6, characterized in that, The calculation of the second road surface adhesion coefficient estimate based on the tire lateral characteristics includes: Based on the tire lateral stiffness, a brush model is constructed to characterize the relationship between tire slip angle, road adhesion coefficient, vertical load and tire lateral force. Based on the initial road surface adhesion coefficient, the estimated value of the tire lateral force is calculated according to the brush model; Based on the total mass of the target vehicle and the estimated value of the tire lateral force, calculate the estimated value of the lateral acceleration. Based on the estimated value of the lateral acceleration and the measured value of the lateral acceleration, the road surface adhesion coefficient update rate is calculated. Based on the road surface adhesion coefficient update rate, the initial road surface adhesion coefficient of the target vehicle is iteratively updated to obtain the maximum lateral utilization adhesion coefficient. The second road surface adhesion coefficient of the target vehicle is estimated by calculating the compensation for the maximum lateral adhesion coefficient.

8. The method according to claim 1, characterized in that, Determining the estimated road surface adhesion coefficient of the target vehicle based on the first estimated road surface adhesion coefficient and / or the second estimated road surface adhesion coefficient includes: The first road surface adhesion coefficient estimate of the target vehicle is determined as the final road surface adhesion coefficient estimate of the target vehicle; or, The second road surface adhesion coefficient estimate of the target vehicle is determined as the final road surface adhesion coefficient estimate of the target vehicle; or, The first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate are combined to obtain the final road surface adhesion coefficient estimate of the target vehicle.

9. The method according to claim 8, characterized in that, The process of fusing the first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate to obtain the final road surface adhesion coefficient estimate for the target vehicle includes: Based on the tire slip ratio and the tire sideslip angle, the confidence levels of the first road surface adhesion coefficient estimate and the second road surface adhesion coefficient estimate are calculated using normalization. The final estimated road surface adhesion coefficient of the target vehicle is determined by summing the products of the first and second road surface adhesion coefficient estimates and their corresponding confidence levels.

10. A device for estimating tire road surface adhesion coefficient, characterized in that, include: The first determining module is used to determine the longitudinal and lateral characteristics of each tire of the target vehicle. The longitudinal characteristics of the tire include at least tire slip ratio, tire longitudinal force, vertical load, and tire longitudinal stiffness. The lateral characteristics of the tire include at least tire slip angle, tire lateral force, vertical load, and tire lateral stiffness. The longitudinal stiffness and the lateral stiffness of the tire are obtained through tire feature self-learning. The first calculation module is used to respond to the longitudinal dynamic estimation condition of the road adhesion coefficient triggered by the tire longitudinal characteristics and calculate a first road adhesion coefficient estimate based on the tire longitudinal characteristics. The longitudinal dynamic estimation condition is that the first tire equivalent stiffness value is less than a first preset critical stiffness value. The first tire equivalent stiffness value is calculated based on the tire longitudinal force and the tire slip ratio in the tire longitudinal characteristics. The second calculation module is used to respond to the lateral dynamic estimation condition of the road adhesion coefficient triggered by the tire lateral features, and to calculate a second road adhesion coefficient estimate based on the tire lateral features. The lateral dynamic estimation condition is that the second tire equivalent stiffness value is less than a second preset critical stiffness value. The second tire equivalent stiffness value is calculated based on the tire lateral force and the tire slip angle in the tire lateral features. The second determining module is used to determine the final road adhesion coefficient estimate of the target vehicle based on the first road adhesion coefficient estimate and / or the second road adhesion coefficient estimate.

11. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

12. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-9.

13. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-9.

14. A vehicle comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.

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