Method and device for identifying friction model parameters of rubber wheel

Abstract

The invention discloses a friction model parameter identification method and device for a rubber wheel. Comprising the following steps of: acquiring a joint mark area, multiple groups of simulation data and multiple groups of experiment data of the rubber wheel under multiple load conditions; determining the simulation precision of each load condition according to the grounding mark area under each load condition and the grounding mark area in the plurality of groups of simulation data; aiming at each working condition corresponding to the target load condition of which the precision reaches the standard, adjusting a grounding friction coefficient in the first simulation data based on the simulation data and the experimental data under the working condition, and obtaining simulation data obtained by simulating the rubber wheel according to the adjusted target grounding friction coefficient under the working condition; and performing parameter identification on the friction model based on the target grounding friction coefficients of the rubber wheel under the plurality of working conditions corresponding to each target load condition and the corresponding simulation data. The technical problem that the simulation result precision of the rubber wheel is low due to the fact that the friction model parameters of the rubber wheel are set to be fixed values in the related technology is solved.

Description

Technical Field

[0001] The present application relates to the technical field of tire mechanics simulation, and in particular to a method and device for identifying friction model parameters of a rubber wheel. Background Art

[0002] The friction characteristics between tires and the road have a significant impact on vehicle handling, wear, and other performance characteristics. The friction properties of the tire's tread compound significantly influence the tire's overall performance. Currently, in common tire finite element simulations, tire-road contact properties are often derived using fixed values. This significantly impacts the accuracy of some simulation methods, particularly in six-component force and wear simulations.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present application provide a method and apparatus for identifying friction model parameters of a rubber wheel, so as to at least solve the technical problem in the related art that the friction model parameters of the rubber wheel are set to fixed values, resulting in low accuracy of simulation results of the rubber wheel.

[0005] According to one aspect of an embodiment of the present application, a method for identifying friction model parameters of a rubber wheel is provided, comprising: obtaining multiple rubbing contact patch areas of the rubber wheel under multiple load conditions, and obtaining multiple sets of first simulation data and multiple sets of experimental data of the rubber wheel under multiple working conditions corresponding to each load condition, wherein the first simulation data at least includes: a simulated contact patch area and a contact friction coefficient; for each load condition, determining a simulation accuracy of the rubber wheel under the load condition based on the rubbing contact patch area corresponding to the load condition and the simulated contact patch areas in the multiple sets of first simulation data, and determining that the load condition is a target load condition when the simulation accuracy is higher than a preset simulation accuracy threshold ..., and determining that the load condition is a target load condition when the simulation accuracy is higher than a preset simulation accuracy threshold; for each load condition, determining a simulation accuracy of the rubber wheel under the load condition, and determining that the load condition is a target load condition For each working condition corresponding to each target load condition, the ground friction coefficient in the first simulation data is iteratively adjusted based on the first simulation data and experimental data of the rubber wheel under the working condition until a preset termination condition is met, thereby obtaining the target ground friction coefficient of the rubber wheel under the working condition, and obtaining second simulation data obtained by simulating the rubber wheel according to the corresponding target ground friction coefficient under the working condition, wherein the second simulation data at least includes: an average slip rate, an average ground pressure, and a speed; based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding second simulation data, the friction model of the rubber wheel is parameter identified to obtain model parameters of the friction model.

[0006] Optionally, multiple rubbing contact patch areas of the rubber wheel under multiple load conditions are obtained, including: equally dividing the entire circumference of the rubber wheel to obtain multiple parts to be rubbing; obtaining the initial rubbing contact patch areas of each of the multiple parts to be rubbing under multiple load conditions; under each load condition, calculating the average value of the initial rubbing contact patch areas of each of the multiple parts to be rubbing under the load condition, and using the average value as the rubbing contact patch area of ​​the rubber wheel under the load condition.

[0007] Optionally, multiple sets of first simulation data and multiple sets of experimental data of the rubber wheel under multiple working conditions corresponding to each load condition are obtained, including: determining multiple working conditions corresponding to each load condition, wherein the working condition parameters corresponding to the multiple working conditions are different, and the working condition parameters include at least one of the following: speed, slip angle, and sideslip angle; using a testing machine to test the rubber wheel under multiple working conditions corresponding to each load condition, and obtain experimental data corresponding to each working condition, wherein the experimental data include at least one of the following: experimental lateral force and slip distance; constructing a finite element model of the rubber wheel, and using each working condition, the ground friction coefficient corresponding to the working condition, and the experimental data as input parameters, using preset simulation software to perform sideslip simulation on the finite element model of the rubber wheel, and obtain first simulation data of the rubber wheel under each working condition, wherein the first simulation data also includes: simulated lateral force.

[0008] Optionally, the simulation accuracy of the rubber wheel under the load condition is determined based on the rubbing contact patch area corresponding to the load condition and the simulated contact patch area in multiple sets of first simulation data, including: determining a first number of working conditions corresponding to the load condition; determining a second number of working conditions corresponding to the simulated contact patch area in which the area error between the rubbing contact patch area corresponding to the load condition and the simulated contact patch area in multiple sets of first simulation data is less than a first error threshold; calculating the ratio of the second number to the first number, and using the ratio as the simulation accuracy of the rubber wheel under the load condition.

[0009] Optionally, after determining the simulation accuracy of the rubber wheel under the load condition, the method further includes: when the simulation accuracy of the rubber wheel under the load condition is not higher than a preset simulation accuracy threshold, adjusting the finite element model of the rubber wheel according to a preset model adjustment strategy, wherein the model adjustment strategy includes at least one of the following: adjusting the number of finite element units in the cross section of the rubber wheel, increasing the number of finite element units in the ground contact area of ​​the rubber wheel, and reducing the number of finite element units in the ground contact area away from the rubber wheel; again obtaining multiple sets of third simulation data of the rubber wheel under multiple working conditions corresponding to the load condition, and re-determining the simulation accuracy of the rubber wheel under the load condition based on the rubbing ground contact footprint area corresponding to the load condition and the simulated ground contact footprint area in the multiple sets of third simulation data.

[0010] Optionally, based on the first simulation data and experimental data of the rubber wheel under the working condition, the ground friction coefficient in the first simulation data is iteratively adjusted until a preset termination condition is met to obtain the target ground friction coefficient of the rubber wheel under the working condition, including: determining the data error between the lateral force of the rubber wheel under the working condition and the experimental lateral force of the rubber wheel under the working condition; judging whether the data error is less than a second error threshold; when the data error is not less than the second error threshold, adjusting the friction coefficient of the rubber wheel under the working condition until the termination condition is met to obtain the target ground friction coefficient of the rubber wheel under the working condition, wherein the termination condition is that the data error between the simulated lateral force of the rubber wheel under the working condition and the experimental lateral force is less than the second error threshold.

[0011] Optionally, performing parameter identification on a friction model of the rubber wheel based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding second simulation data to obtain model parameters of the friction model includes: determining the friction model of the rubber wheel:

[0012]

[0013] Where μ represents the contact friction coefficient in the second simulation data, r v represents the average slip rate in the second simulation data, v represents the speed in the second simulation data, r p represents the average ground pressure in the second simulation data, p represents the target load condition in the second simulation data, c0, c1, c2, c3, and c4 are all model parameters to be identified in the friction model; based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding speed, average slip rate, and average ground pressure in the second simulation data, the least squares method is used to perform parameter identification on the friction model to obtain the model parameters of the friction model of the rubber wheel.

[0014] According to another aspect of the embodiment of the present application, a friction model parameter identification device for a rubber wheel is also provided, including: an acquisition module for acquiring multiple rubbing ground footprint areas of the rubber wheel under multiple load conditions, and acquiring multiple groups of first simulation data and multiple groups of experimental data of the rubber wheel under multiple working conditions corresponding to each load condition, wherein the first simulation data at least includes: a simulated ground footprint area and a ground friction coefficient; a determination module for determining, for each load condition, the simulation accuracy of the rubber wheel under the load condition based on the rubbing ground footprint area corresponding to the load condition and the simulated ground footprint area in the multiple groups of first simulation data, and determining that the load condition is a target load condition when the simulation accuracy is higher than a preset simulation accuracy threshold; adjusting A module is used to iteratively adjust the ground friction coefficient in the first simulation data based on the first simulation data and experimental data of the rubber wheel under the working condition for each working condition corresponding to each target load condition, until a preset termination condition is met, thereby obtaining the target ground friction coefficient of the rubber wheel under the working condition, and obtaining second simulation data obtained by simulating the rubber wheel according to the corresponding target ground friction coefficient under the working condition, wherein the second simulation data at least includes: average slip rate, average ground pressure, and speed; a parameter identification module is used to perform parameter identification on the friction model of the rubber wheel based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding second simulation data, thereby obtaining model parameters of the friction model.

[0015] According to another aspect of the embodiments of the present application, a computer program product is further provided. The computer program product includes: a computer program, wherein when the computer program is executed by a processor, the above-mentioned method for identifying friction model parameters of a rubber wheel is implemented.

[0016] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-mentioned method for identifying friction model parameters of a rubber wheel through the computer program.

[0017] In an embodiment of the present application, the imprinted contact patch area of ​​the rubber wheel under multiple load conditions, as well as multiple sets of first simulation data and multiple sets of experimental data for different working conditions corresponding to each load condition, are collected; the imprinted contact patch area under the same load condition is compared with the simulated contact patch area within the multiple sets of first simulation data to evaluate the simulation accuracy of the rubber wheel under each load condition; for each working condition under a target load condition where the simulation accuracy exceeds a preset threshold, the contact friction coefficient under that working condition is iteratively adjusted based on the first simulation data and experimental data under that working condition until a preset termination condition is met, thereby obtaining the target contact friction coefficient, and obtaining second simulation data obtained by simulating the rubber wheel under the working condition according to the corresponding target contact friction coefficient. Finally, using the target friction coefficient under each working condition corresponding to each target load condition and the corresponding second simulation data, a friction model of the rubber wheel is parameterized using a mathematical optimization method to obtain model parameters of the friction model. Therefore, the present application verifies the accuracy of the simulation model through experimental data. By closely integrating simulation and experiment, the proposed method improves the accuracy of friction model parameter identification, overcoming the limitations of traditional methods for identifying friction coefficients. This method achieves the technical effect of highly accurate identification of friction model parameters, thereby improving the accuracy of tire mechanics simulation. This also addresses the technical issue in related technologies where the friction model parameters for rubber wheels are set to fixed values, resulting in low-accuracy simulation results for rubber wheels. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a flow chart of an optional method for identifying friction model parameters of a rubber wheel according to an embodiment of the present application;

[0020] Figure 2 This is a schematic diagram of an optional division of the portion to be printed according to an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of an optional finite element model of a rubber wheel according to an embodiment of the present application;

[0022] Figure 4 1 is a schematic structural diagram of an optional device for identifying friction model parameters of a rubber wheel according to an embodiment of the present application;

[0023] Figure 5 It is a schematic structural diagram of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] To enable those skilled in the art to better understand the solution of this application, the following will clearly and completely describe the technical solution in the embodiments of this application in combination with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0025] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of this application are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments of this application described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] To better understand the embodiments of this application, the following first translates and explains some nouns or terms that appear in the description process of the embodiments of this application:

[0027] Slip angle: used to describe the angle formed between the vehicle traveling direction and the wheel rim direction.

[0028] Side slip: refers to that during the vehicle driving process, due to the lateral inclination of the route, lateral wind or centrifugal force during curve driving, etc., a lateral force F is generated along the axle direction of the vehicle center of gravity. Because the wheels are elastic, when the lateral force F does not reach the maximum frictional force between the wheel and the ground, the lateral force F causes the tire to deform, making the wheel tilt, resulting in the wheel driving direction deviating from the predetermined driving route. This phenomenon is called the side slip phenomenon of the tire.

[0029] Ordinary Least Squares: a common method to solve the curve fitting problem. Its basic idea is to make [[ID=1十七]]where is a set of linearly independent functions selected in advance, and α k represents the undetermined coefficients (k = 1, 2,..., m, m < n). The fitting criterion is to make y i (i = 1, 2,..., n) and f(x i ) the sum of the squares of the distances δ i the smallest, which is called the least squares criterion.

[0030] Embodiment 1

[0031] According to an embodiment of the present application, a method for identifying friction model parameters of a rubber wheel is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] Figure 1 FIG. 1 is a flow chart of a method for identifying friction model parameters of a rubber wheel according to an embodiment of the present application. Figure 1 As shown, the method includes the following steps:

[0033] Step S102 : obtaining a plurality of contact patch areas of the rubber wheel under a plurality of load conditions, and obtaining a plurality of sets of first simulation data and a plurality of sets of experimental data of the rubber wheel under a plurality of working conditions corresponding to each load condition.

[0034] In the technical solution provided by the above-mentioned step S102, the above-mentioned rubber wheel is a tire of a specific type for testing and analyzing friction characteristics, which includes but is not limited to automobile tires, motorcycle tires or industrial rubber wheels, etc. The load condition refers to the vertical pressure (such as 20N, 45N, 75N, 95N, etc.) that the rubber wheel bears. The change of this condition can significantly affect the contact state and friction characteristics of the rubber wheel and the road surface. Therefore, the rubbing contact footprint area is to measure the actual area data of the load wheel in contact with the ground after the rubber wheel is compacted according to different load conditions. In addition, for each load condition, a variety of (test) working conditions can be designed, wherein the working condition refers to the specific working conditions of the rubber wheel during testing and simulation, which include but are not limited to parameters such as speed, sideslip angle, slip angle, so the working condition parameters corresponding to different working conditions are different. The experimental data is obtained by using a testing machine to test the rubber wheel under multiple working conditions corresponding to each load condition, which is used for subsequent comparative analysis with the simulation data; and the first simulation data is data obtained by using finite element software to perform rotation simulation of the rubber wheel under multiple working conditions corresponding to each load condition, and the simulation data at least includes: simulated ground contact patch area and ground friction coefficient.

[0035] In step S104, for each load condition, a simulation accuracy of the rubber wheel under the load condition is determined based on the rubbing contact patch area corresponding to the load condition and the simulated contact patch areas in the plurality of sets of first simulation data. When the simulation accuracy is higher than a preset simulation accuracy threshold, the load condition is determined to be a target load condition.

[0036] In the technical solution provided in step S104 above, the simulation accuracy corresponding to the load condition (i.e., the accuracy and reliability of the finite element simulation) is evaluated by comparing the error rate or degree of deviation between the contact patch area of ​​the rubbing under the specific load condition and the simulated contact patch area within the multiple sets of first simulation data obtained by simulation. If the simulation accuracy under the specific load condition exceeds a preset simulation accuracy threshold, this indicates that the finite element simulation accurately reflects the actual contact state of the rubber wheel under the load condition, that is, the simulation results are consistent with or close to the experimental results. In this case, the load condition can be considered a target load condition that meets the accuracy requirements.

[0037] Step S106, for each working condition corresponding to each target load condition, iteratively adjust the ground friction coefficient in the first simulation data based on the first simulation data and experimental data of the rubber wheel under the working condition until a preset termination condition is met, thereby obtaining the target ground friction coefficient of the rubber wheel under the working condition, and obtaining second simulation data obtained by simulating the rubber wheel according to the corresponding target ground friction coefficient under the working condition.

[0038] In the technical solution provided in step S106 above, after the target load condition is determined, for each operating condition corresponding to the target load condition, an offset or error is calculated based on the comparison of the simulation data (i.e., predicted value) with the experimental data (i.e., reference standard) under that operating condition to evaluate the accuracy of the current friction coefficient. The ground contact friction coefficient is then adjusted based on the evaluation result until the error between the simulation result and the experimental data meets a preset accuracy or convergence condition, thereby obtaining the target ground contact friction coefficient for the rubber wheel under that operating condition. Next, second simulation data is obtained by simulating the rubber wheel under that operating condition according to the corresponding target ground contact friction coefficient. The second simulation data includes at least the average slip rate and the average ground contact pressure.

[0039] Step S108 , performing parameter identification on the friction model of the rubber wheel based on the target ground contact friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding second simulation data, to obtain model parameters of the friction model.

[0040] In the technical solution provided in step S108 above, the target ground friction coefficient for the rubber tire under multiple operating conditions corresponding to each target load condition and the corresponding second simulation data are used as input to the rubber tire friction model. A mathematical optimization algorithm is then used to find the optimal combination of model parameters. The identified model parameters can reflect the frictional mechanical properties of the rubber material, including but not limited to the effects of pressure and slip on the friction coefficient. The resulting model parameters can be applied to the friction model of tire finite element simulations, significantly improving the accuracy and reliability of tire performance simulations, such as six-component force and wear, and providing a scientific basis for tire design and optimization.

[0041] Based on the scheme defined by the above steps S102 to S108, it can be known that in the scheme of the present application, the accuracy of the simulation model is verified by experimental data, and the model parameters obtained by identification based on the close combination of simulation and experiment can accurately reflect the friction characteristics of the rubber wheel under various working conditions, thereby improving the identification accuracy of the friction model parameters, overcoming the limitations of the traditional method for identifying the friction coefficient, and achieving the technical effect of high-precision identification of the friction parameters of the friction model, thereby achieving the purpose of improving the accuracy and reliability of tire mechanics simulation.

[0042] The following describes the steps of the friction model parameter identification method for the rubber wheel in combination with a specific implementation process.

[0043] As an optional implementation, in the technical solution provided in step S102 above, multiple contact footprint areas of the rubber wheel under multiple load conditions may be obtained by the following method, including:

[0044] Step 1: Divide the entire circumference of the rubber wheel into equal parts to obtain multiple sections to be printed. For example, the surface of the rubber wheel can be divided into 8 or more sectors, each of which serves as a test section for the printing experiment, ensuring that all angles and areas of the rubber wheel surface are covered.

[0045] Step 2: Obtain the initial contact footprint area of ​​each of the multiple to-be-printed portions under multiple load conditions. That is, for each divided to-be-printed portion, obtain the actual contact area data of the to-be-printed portion with the ground under different load conditions.

[0046] For example, use the vulcanized rubber wheel as the test wheel of the testing machine and clean the surface of the rubber wheel. Select a large enough grid paper, where each cell in the grid paper is 1mm*1mm, and use tape to stick one edge of the grid paper on the frosting disc, such as Figure 2 As shown. Next, evenly apply ink on the part of the rubber wheel to be printed (generally 1 / 8 of the entire circumference of the rubber wheel), and compact the part to be printed on the testing machine according to different loads, and then measure the ground footprint area under different load conditions. Then, remove the grid paper with the tire print, use a pen to draw the outline of the ground footprint of the part to be printed along the edge of the print, and then count the number of units. When the footprint area contained in each cell exceeds half of the cell's own area, the cell is counted when calculating the footprint area. On the contrary, when it is less than half, the cell is discarded when calculating the footprint area. Through this method, the static ground footprint area of ​​each part to be printed under different load conditions is finally calculated.

[0047] Step 3: Under each load condition, calculate the average value of the initial rubbing contact patch areas of the multiple parts to be rubbing under the load condition, and use the average value as the rubbing contact patch area of ​​the rubber wheel under the load condition.

[0048] In addition to the above-mentioned solutions for calculating the rubbing footprint area, those skilled in the art may also use other technical solutions to calculate the rubbing footprint area of ​​the rubber wheel under different load conditions. For example, those skilled in the art may make changes to the above-mentioned implementation solutions, which shall also be within the scope of protection of the present invention.

[0049] As another optional implementation, in the technical solution provided in step S102 above, multiple sets of first simulation data and multiple sets of experimental data of the rubber wheel under multiple working conditions corresponding to each load condition can be obtained by the following method, including:

[0050] Step 1: Determine multiple operating conditions corresponding to each load condition. Each of the multiple operating conditions corresponds to different operating parameters, and the operating parameters include at least one of the following: speed (longitudinal speed, lateral speed), slip angle (Side Slip Angle), and sideslip angle.

[0051] Step 2: Use a testing machine to test the rubber wheel under multiple working conditions corresponding to each load condition to obtain experimental data corresponding to each working condition, wherein the experimental data includes at least one of the following: experimental lateral force and slip distance.

[0052] Step 3: Construct a finite element model of the rubber wheel, and use each working condition, the corresponding ground friction coefficient and experimental data as input parameters, use the preset simulation software to perform lateral simulation on the finite element model of the rubber wheel, and obtain the first simulation data of the rubber wheel under each working condition, wherein the first simulation data also includes: simulated lateral force.

[0053] For example, the pre-ground rubber wheel is installed on the testing machine, and the working condition parameters of multiple working conditions corresponding to each load condition are set on the testing machine, as shown in Table 1 below.

[0054] Table 1

[0055]

[0056] Next, the testing machine is controlled to perform a rotation test on the pre-ground rubber wheel according to the set parameters. Sensors are used to collect experimental data under various operating conditions, including but not limited to experimental lateral force and slip distance. Finally, based on the format and processing method of the experimental data output by the testing machine, an experimental data processing program can be written to modify the original path of different experimental data to quickly process the required results.

[0057] It should be noted that if the same rubber wheel is used for multiple test conditions, it is necessary to cool the rubber wheel for a period of time (generally 30 minutes) before using it in the next test condition. In addition, if each test condition is to be tested twice, the final test result can be the average of the two test results.

[0058] Then, determine the physical size information of the vulcanized rubber wheel (including the outer diameter, inner diameter and thickness of the rubber wheel). Based on the physical size information, use finite element software to establish a finite element model of the rubber wheel, and set the corresponding material properties according to the type of rubber wheel. Figure 3 The finite element model shown in Figure 2 shows the initial contact friction coefficient between the disc and the rubber wheel set during the simulation of the finite element module under different working conditions.

[0059] Finally, each working condition, the corresponding ground friction coefficient and experimental data are used as input parameters of the simulation stage, and the preset simulation software is used to perform side deviation simulation on the finite element model of the rubber wheel to obtain the first simulation data of the rubber wheel under each working condition, =.

[0060] As an optional implementation, in the technical solution provided in the above step S104, the simulation accuracy of the rubber wheel under load conditions can be quantitatively evaluated according to the following method, including:

[0061] Step 1: Determine the first number of load cases corresponding to the load conditions.

[0062] Step 2: Determine a second number of operating conditions corresponding to simulated contact patch areas where an area error between the rubbing contact patch area corresponding to the load condition and the simulated contact patch areas in the plurality of sets of first simulation data is less than a first error threshold.

[0063] Step 3: Calculate the ratio of the second quantity to the first quantity and use this ratio as the simulation accuracy of the rubber wheel under the load condition. The higher the ratio, the more consistent the simulation results are with the experimental data under this load condition, and the higher the simulation accuracy.

[0064] Furthermore, when the simulation accuracy under the load condition is higher than a preset simulation accuracy threshold, the load condition is determined to be a target load condition.

[0065] However, when the simulation accuracy of the rubber wheel under a specific load condition is not higher than a preset simulation accuracy threshold, the finite element model of the rubber wheel can be adjusted according to a preset model adjustment strategy, wherein the model adjustment strategy includes but is not limited to: adjusting the number of finite element units in the cross section of the rubber wheel, increasing the number of finite element units in the ground contact area of ​​the rubber wheel, reducing the number of finite element units in the ground contact area away from the rubber wheel, etc.; then, obtaining multiple sets of third simulation data of the rubber wheel under multiple working conditions corresponding to the load condition again, and re-determining the simulation accuracy of the rubber wheel under the load condition based on the rubbing ground contact footprint area corresponding to the load condition and the simulated ground contact footprint area in the multiple sets of third simulation data until the simulation accuracy of the rubber wheel under the load condition is higher than the simulation accuracy threshold.

[0066] After all load conditions are set to target load conditions that meet the simulation accuracy requirements through the above steps, for each working condition corresponding to each target load condition, the contact friction coefficient in the first simulation data under the working condition may be adjusted in the following manner, including:

[0067] The first step is to determine the error between the simulated lateral force and the experimental lateral force under the same conditions. Specifically, the simulated lateral force is compared with the experimental lateral force obtained under the same conditions and the error between the two values ​​(either absolute value or percentage) is calculated. This error reflects the deviation between the model prediction and the actual measurement.

[0068] Step 2: Determine whether the data error is less than the second error threshold (i.e., the criterion for simulation accuracy);

[0069] Step 3: If the data error is no less than the second error threshold, the friction coefficient setting of the current model fails to accurately reflect the actual friction characteristics under the working condition. Therefore, the friction coefficient of the rubber wheel under the working condition can be adjusted through an iterative algorithm, such as by increasing or decreasing the friction coefficient value or optimizing the functional expression of the friction coefficient, in order to reduce the data error until the termination condition is met and the target ground contact friction coefficient of the rubber wheel under the working condition is obtained. The termination condition is that the data error between the simulated lateral force and the experimental lateral force of the rubber wheel under the working condition is less than the second error threshold.

[0070] Furthermore, multiple sets of second simulation data can be obtained by simulating the rubber wheel under various operating conditions corresponding to the target load conditions and the target ground friction coefficient corresponding to the operating conditions. The second simulation data includes at least: average ground pressure and average slip rate. The average ground pressure and average slip rate of the rubber wheel are the averages of the ground pressure and slip rate at each node on the rubber wheel. By combining experiments and simulations, the average ground pressure and average slip rate of the rubber wheel during rotation can be obtained, thus overcoming the technical problem that current testing machines cannot accurately measure the average ground pressure and average slip rate of rubber wheels.

[0071] Furthermore, in the technical solution provided in step S108, parameter identification of the friction model of the rubber wheel can be achieved through the following steps, including:

[0072] Step 1: Determine the friction model of the rubber wheel:

[0073]

[0074] Where μ represents the contact friction coefficient in the second simulation data, r v represents the average slip rate in the second simulation data, v represents the speed in the second simulation data, r p represents the average ground pressure in the second simulation data, p represents the target load condition in the second simulation data, c0, c1, c2, c3, and c4 are all model parameters to be identified in the friction model, and the above-mentioned e represents an exponent.

[0075] Step 2: Based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the speed, average slip rate and average ground pressure in the corresponding second simulation data, the least squares method is used to perform parameter identification on the friction model to obtain model parameters of the friction model of the rubber wheel.

[0076] Therefore, the friction model parameter identification method for the rubber wheel provided by the above steps has the following technical advantages compared to the existing method of setting the model parameters of the friction model to fixed values:

[0077] (1) The embodiment of the present application combines experimental data and finite element simulation results to iteratively adjust the friction coefficient in the simulation results under various working conditions corresponding to different loads, thereby ensuring that the simulation results of the rubber wheel model are closer to the performance of the actual tire, providing a more reliable data basis for the parameter identification of the tire model, and ensuring that the model parameters of the friction model identified based on this are more accurate.

[0078] (2) The embodiments of the present application can dynamically adjust the model parameters of the friction model according to changes in actual working conditions, so that the final friction model can better reflect the friction characteristics between the tire and the ground under real conditions, thereby increasing the flexibility and applicability of the model.

[0079] (3) The embodiments of the present application are universal for identifying friction model parameters under different tires, different materials, and different working conditions, and can be extended to other types of tires or more complex working environments by adjusting the experimental conditions and simulation models, greatly expanding the applicability of the solution.

[0080] Example 2

[0081] According to an embodiment of the present application, a device for identifying the friction model parameters of a rubber wheel is provided for implementing the method for identifying the friction model parameters of a rubber wheel in embodiment 1. Figure 4 As shown, the friction model parameter identification device of the rubber wheel at least includes: an acquisition module 42, a determination module 44, an adjustment module 46 and a parameter identification module 48, wherein:

[0082] an acquisition module 42 for acquiring a plurality of contact patch areas of the rubber wheel under a plurality of load conditions, and acquiring a plurality of sets of first simulation data and a plurality of sets of experimental data of the rubber wheel under a plurality of working conditions corresponding to each of the load conditions, wherein the first simulation data includes at least: a simulated contact patch area and a contact friction coefficient;

[0083] a determination module 44 configured to determine, for each load condition, a simulation accuracy of the rubber wheel under the load condition based on the rubbing contact patch area corresponding to the load condition and the simulated contact patch areas in the plurality of sets of first simulation data, and determine the load condition as a target load condition when the simulation accuracy exceeds a preset simulation accuracy threshold;

[0084] An adjustment module 46 is configured to iteratively adjust the ground friction coefficient in the first simulation data for each working condition corresponding to each target load condition based on the first simulation data and experimental data of the rubber wheel under the working condition until a preset termination condition is satisfied, thereby obtaining the target ground friction coefficient of the rubber wheel under the working condition, and obtaining second simulation data obtained by simulating the rubber wheel according to the corresponding target ground friction coefficient under the working condition;

[0085] The parameter identification module 48 is configured to perform parameter identification on the friction model of the rubber wheel based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding second simulation data to obtain model parameters of the friction model.

[0086] The functions of each module of the friction model parameter identification device for a rubber wheel are described below in conjunction with a specific implementation process.

[0087] Optionally, the acquisition module 42 may acquire multiple contact footprint areas of the rubber wheel under multiple load conditions by the following method, including:

[0088] Step 1: Divide the entire circumference of the rubber wheel into equal parts to obtain multiple sections to be printed. For example, the surface of the rubber wheel can be divided into 8 or more sectors, each of which serves as a test section for the printing experiment, ensuring that all angles and areas of the rubber wheel surface are covered.

[0089] Step 2: Obtain the initial contact footprint area of ​​each of the multiple to-be-printed portions under multiple load conditions. That is, for each divided to-be-printed portion, obtain the actual contact area data of the to-be-printed portion with the ground under different load conditions.

[0090] Step 3: Under each load condition, calculate the average value of the initial rubbing contact patch areas of the multiple parts to be rubbing under the load condition, and use the average value as the rubbing contact patch area of ​​the rubber wheel under the load condition.

[0091] In addition to the above-mentioned solutions for calculating the rubbing footprint area, those skilled in the art may also use other technical solutions to calculate the rubbing footprint area of ​​the rubber wheel under different load conditions. For example, those skilled in the art may make changes to the above-mentioned implementation solutions, which shall also be within the scope of protection of the present invention.

[0092] Optionally, the acquisition module 42 may acquire multiple sets of first simulation data and multiple sets of experimental data of the rubber wheel under multiple working conditions corresponding to each load condition by the following method, including:

[0093] Step 1: Determine multiple operating conditions corresponding to each load condition. Each of the multiple operating conditions corresponds to different operating parameters, and the operating parameters include at least one of the following: speed (longitudinal speed, lateral speed), slip angle (Side Slip Angle), and sideslip angle.

[0094] Step 2: Use a testing machine to test the rubber wheel under multiple working conditions corresponding to each load condition to obtain experimental data corresponding to each working condition, wherein the experimental data includes at least one of the following: experimental lateral force and slip distance.

[0095] Step 3: Construct a finite element model of the rubber wheel, and use each working condition, the corresponding ground friction coefficient and experimental data as input parameters, use the preset simulation software to perform lateral simulation on the finite element model of the rubber wheel, and obtain the first simulation data of the rubber wheel under each working condition, wherein the first simulation data also includes: simulated lateral force.

[0096] Furthermore, the determination module 44 may quantitatively evaluate the simulation accuracy of the rubber wheel under load conditions according to the following method, including:

[0097] Step 1: Determine the first number of load cases corresponding to the load conditions.

[0098] Step 2: Determine a second number of operating conditions corresponding to simulated contact patch areas where an area error between the rubbing contact patch area corresponding to the load condition and the simulated contact patch areas in the plurality of sets of first simulation data is less than a first error threshold.

[0099] Step 3: Calculate the ratio of the second quantity to the first quantity and use this ratio as the simulation accuracy of the rubber wheel under the load condition. The higher the ratio, the more consistent the simulation results are with the experimental data under this load condition, and the higher the simulation accuracy.

[0100] Therefore, when the simulation accuracy under the load condition is higher than a preset simulation accuracy threshold, the load condition is determined to be the target load condition.

[0101] In addition, the friction model parameter identification device for the rubber wheel provided in the embodiment of the present application also includes: a model adjustment module, and when the simulation accuracy of the rubber wheel under specific load conditions is not higher than a preset simulation accuracy threshold, the model adjustment module can adjust the finite element model of the rubber wheel according to a preset model adjustment strategy, wherein the model adjustment strategy includes but is not limited to: adjusting the number of finite element units in the cross section of the rubber wheel, increasing the number of finite element units in the ground contact area of ​​the rubber wheel, reducing the number of finite element units in the ground contact area away from the rubber wheel, etc.

[0102] Next, the acquisition module 42 may again acquire multiple sets of third simulation data of the rubber wheel under multiple working conditions corresponding to the load condition.

[0103] Then, the determination module 44 can re-determine the simulation accuracy of the rubber wheel under the load condition based on the rubbing contact patch area corresponding to the load condition and the simulated contact patch areas in the multiple sets of third simulation data until the simulation accuracy of the rubber wheel under the load condition is higher than the simulation accuracy threshold.

[0104] Furthermore, after all load conditions are set to target load conditions that meet the simulation accuracy requirements through the above operations, the adjustment module 46 may adjust the ground friction coefficient of each working condition corresponding to each target load condition according to the following method, including:

[0105] The first step is to determine the error between the simulated lateral force and the experimental lateral force under the same conditions. Specifically, the simulated lateral force is compared with the experimental lateral force obtained under the same conditions and the error between the two values ​​(either absolute value or percentage) is calculated. This error reflects the deviation between the model prediction and the actual measurement.

[0106] Step 2: Determine whether the data error is less than the second error threshold (i.e., the criterion for simulation accuracy);

[0107] Step 3: If the data error is no less than the second error threshold, the friction coefficient setting of the current model fails to accurately reflect the actual friction characteristics under the working condition. Therefore, the friction coefficient of the rubber wheel under the working condition can be adjusted through an iterative algorithm, such as by increasing or decreasing the friction coefficient value or optimizing the functional expression of the friction coefficient, in order to reduce the data error until the termination condition is met and the target ground contact friction coefficient of the rubber wheel under the working condition is obtained. The termination condition is that the data error between the simulated lateral force and the experimental lateral force of the rubber wheel under the working condition is less than the second error threshold.

[0108] Furthermore, adjustment module 46 can also obtain multiple sets of second simulation data obtained by simulating the rubber wheel under various operating conditions corresponding to the target load conditions and in accordance with the target ground friction coefficient corresponding to each operating condition. The second simulation data includes at least average ground pressure and average slip rate, where the average ground pressure and average slip rate of the rubber wheel are the averages of the ground pressure and slip rate at each node on the rubber wheel. By combining experiments and simulations, the average ground pressure and average slip rate of the rubber wheel during rotation can be obtained, thereby overcoming the technical problem that current testing machines cannot accurately measure the average ground pressure and average slip rate of rubber wheels.

[0109] Finally, the parameter identification module 48 can implement parameter identification of the friction model of the rubber wheel through the following steps, including:

[0110] Step 1: Determine the friction model of the rubber wheel:

[0111]

[0112] Where μ represents the contact friction coefficient in the second simulation data, r v represents the average slip rate in the second simulation data, v represents the speed in the second simulation data, r p represents the average ground pressure in the second simulation data, p represents the target load condition in the second simulation data, c0, c1, c2, c3, and c4 are all model parameters to be identified in the friction model, and the above-mentioned e represents an exponent.

[0113] Step 2: Based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the speed, average slip rate and average ground pressure in the corresponding second simulation data, the least squares method is used to perform parameter identification on the friction model to obtain model parameters of the friction model of the rubber wheel.

[0114] It should be noted that the modules in the friction model parameter identification device for the rubber wheel in the embodiment of the present application correspond one-to-one to the implementation steps of the friction model parameter identification method for the rubber wheel in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be elaborated on here.

[0115] Example 3

[0116] According to an embodiment of the present application, a computer program product is further provided. The computer program product includes a computer program. When the computer program is executed by a processor, the method for identifying friction model parameters of a rubber wheel in embodiment 1 is implemented.

[0117] According to an embodiment of the present application, a non-volatile storage medium is further provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the friction model parameter identification method of the rubber wheel in Example 1 by running the computer program.

[0118] According to an embodiment of the present application, a processor is further provided, which is used to run a computer program, wherein the computer program executes the friction model parameter identification method of the rubber wheel in Example 1 when running.

[0119] According to an embodiment of the present application, an electronic device is further provided, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the friction model parameter identification method of the rubber wheel in Example 1 through the computer program.

[0120] Specifically, when the computer program is executed, the following steps are executed: obtaining multiple rubbing contact patch areas of the rubber wheel under multiple load conditions, and obtaining multiple sets of first simulation data and multiple sets of experimental data of the rubber wheel under multiple working conditions corresponding to each load condition, wherein the first simulation data at least includes: a simulated contact patch area and a contact friction coefficient; for each load condition, determining a simulation accuracy of the rubber wheel under the load condition based on the rubbing contact patch area corresponding to the load condition and the simulated contact patch areas in the multiple sets of first simulation data, and determining the load condition as a target load condition when the simulation accuracy is higher than a preset simulation accuracy threshold; for each working condition corresponding to each target load condition, iteratively adjusting the contact friction coefficient in the first simulation data based on the first simulation data and the experimental data of the rubber wheel under the working condition until a preset termination condition is satisfied, thereby obtaining a target contact friction coefficient of the rubber wheel under the working condition, and obtaining second simulation data obtained by simulating the rubber wheel according to the corresponding target contact friction coefficient under the working condition; and performing parameter identification on a friction model of the rubber wheel based on the target contact friction coefficient of the rubber wheel under the multiple working conditions corresponding to each target load condition and the corresponding second simulation data to obtain model parameters of the friction model.

[0121] As an optional implementation, the electronic device may be in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 5 The hardware structure block diagram of an electronic device for implementing a method for identifying friction model parameters of a rubber wheel is shown. Figure 5 As shown, the electronic device 50 may include one or more (502a, 502b, ..., 502n are shown in the figure) processors 502 (the processor 502 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 504 for storing data, and a transmission device 506 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 5 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 5 More or fewer components than shown, or with Figure 5 Different configurations shown.

[0122] It should be noted that the one or more processors 502 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the electronic device 50. As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0123] The memory 504 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the friction model parameter identification method for the rubber wheel in the embodiment of the present application. The processor 502 executes various functional applications and data processing by running the software programs and modules stored in the memory 504, that is, implementing the vulnerability detection method for the above-mentioned application. The memory 504 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 504 may further include memory remotely located relative to the processor 502, and these remote memories may be connected to the electronic device 50 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0124] The transmission device 506 is used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by the communications provider of the electronic device 50. In one embodiment, the transmission device 506 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 506 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0125] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 50 .

[0126] The serial numbers of the above embodiments are for description only and do not represent the advantages or disadvantages of the embodiments.

[0127] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0129] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0130] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0131] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program code.

[0132] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for identifying friction model parameters of a rubber wheel, characterized in that: include: Acquire multiple contact patch areas of the rubber wheel under multiple load conditions, and acquire multiple sets of first simulation data and multiple sets of experimental data of the rubber wheel under multiple working conditions corresponding to each of the load conditions, wherein the first simulation data at least includes: a simulated contact patch area and a contact friction coefficient; For each load condition, determining a simulation accuracy of the rubber wheel under the load condition based on a contact patch area corresponding to the load condition and simulated contact patch areas in the plurality of sets of the first simulation data, and determining the load condition as a target load condition when the simulation accuracy is greater than a preset simulation accuracy threshold; For each working condition corresponding to each target load condition, iteratively adjusting the ground friction coefficient in the first simulation data based on the first simulation data and experimental data of the rubber wheel under the working condition until a preset termination condition is satisfied, thereby obtaining the target ground friction coefficient of the rubber wheel under the working condition, and obtaining second simulation data obtained by simulating the rubber wheel under the working condition according to the corresponding target ground friction coefficient, wherein the second simulation data includes at least: an average slip rate, an average ground pressure, and a speed; Parameter identification is performed on the friction model of the rubber wheel based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding second simulation data to obtain model parameters of the friction model.

2. The method according to claim 1, characterized in that Obtain multiple contact patch areas of the rubber wheel under various load conditions, including: The entire circumference of the rubber wheel is equally divided to obtain a plurality of portions to be printed; Obtaining the initial contact footprint area of ​​each of the plurality of portions to be stamped under the plurality of load conditions; Under each load condition, an average value of the initial rubbing contact patch areas of each of the plurality of to-be-rubbing portions under the load condition is calculated, and the average value is used as the rubbing contact patch area of ​​the rubber wheel under the load condition.

3. The method according to claim 1, characterized in that Acquiring multiple sets of first simulation data and multiple sets of experimental data of the rubber wheel under multiple working conditions corresponding to each load condition, including: Determining a plurality of operating conditions corresponding to each of the load conditions, wherein the operating condition parameters corresponding to the plurality of operating conditions are different, and the operating condition parameters include at least one of the following: speed, slip angle, and sideslip angle; Using a testing machine, the rubber wheel is tested under multiple working conditions corresponding to each load condition to obtain experimental data corresponding to each working condition, wherein the experimental data includes at least one of the following: experimental lateral force and sliding distance; A finite element model of the rubber wheel is constructed, and each working condition, the ground friction coefficient corresponding to the working condition, and experimental data are used as input parameters. The finite element model of the rubber wheel is simulated for lateral deviation using preset simulation software to obtain first simulation data of the rubber wheel under each working condition, wherein the first simulation data also includes: simulated lateral force.

4. The method according to claim 1, wherein Determining the simulation accuracy of the rubber wheel under the load condition based on the rubbing contact patch area corresponding to the load condition and the simulated contact patch areas in the plurality of sets of the first simulation data includes: determining a first number of operating conditions corresponding to the load condition; determining a second number of operating conditions corresponding to simulated footprint areas in which an area error between the rubbing footprint area corresponding to the load condition and the simulated footprint areas in the plurality of sets of the first simulation data is less than a first error threshold; A ratio of the second quantity to the first quantity is calculated, and the ratio is used as a simulation accuracy of the rubber wheel under the load condition.

5. The method according to claim 1, characterized in that After determining the simulation accuracy of the rubber wheel under the load condition, the method further includes: When the simulation accuracy of the rubber wheel under the load condition is not higher than a preset simulation accuracy threshold, adjusting the finite element model of the rubber wheel according to a preset model adjustment strategy, wherein the model adjustment strategy includes at least one of the following: adjusting the number of finite element units in a cross section of the rubber wheel, increasing the number of finite element units in a contact area of ​​the rubber wheel, and reducing the number of finite element units in a contact area away from the rubber wheel; A plurality of sets of third simulation data of the rubber wheel under a plurality of working conditions corresponding to the load condition are obtained again, and the simulation accuracy of the rubber wheel under the load condition is re-determined based on the rubbing contact patch area corresponding to the load condition and the simulated contact patch area in the plurality of sets of third simulation data.

6. The method according to claim 3, characterized in that Iteratively adjusting the contact friction coefficient in the first simulation data based on the first simulation data and the experimental data of the rubber wheel under the working condition until a preset termination condition is satisfied, thereby obtaining a target contact friction coefficient of the rubber wheel under the working condition, including: determining a data error between the lateral force of the rubber wheel under the working condition and an experimental lateral force of the rubber wheel under the working condition; Determining whether the data error is less than a second error threshold; When the data error is not less than the second error threshold, the friction coefficient of the rubber wheel under the working condition is adjusted until the termination condition is met, thereby obtaining a target ground contact friction coefficient of the rubber wheel under the working condition. The termination condition is that the data error between the simulated lateral force and the experimental lateral force of the rubber wheel under the working condition is less than the second error threshold.

7. The method according to claim 1, characterized in that Parameter identification of the friction model of the rubber wheel is performed based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding second simulation data to obtain model parameters of the friction model, including: Determine the friction model of the rubber wheel: Where μ represents the contact friction coefficient in the second simulation data, r v represents the average slip rate in the second simulation data, v represents the speed in the second simulation data, r p represents the average ground contact pressure in the second simulation data, p represents the target load condition in the second simulation data, and c0, c1, c2, c3, and c4 are all model parameters to be identified in the friction model; Based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the speed, average slip rate, and average ground pressure in the corresponding second simulation data, the least squares method is used to perform parameter identification on the friction model to obtain model parameters of the friction model of the rubber wheel.

8. A friction model parameter identification device for a rubber wheel, characterized in that: include: an acquisition module, configured to acquire a plurality of contact patch areas of the rubber wheel under a plurality of load conditions, and acquire a plurality of sets of first simulation data and a plurality of sets of experimental data of the rubber wheel under a plurality of working conditions corresponding to each of the load conditions, wherein the first simulation data at least includes: a simulated contact patch area and a contact friction coefficient; a determination module configured to determine, for each load condition, a simulation accuracy of the rubber wheel under the load condition based on a rubbing contact patch area corresponding to the load condition and simulated contact patch areas in the plurality of sets of first simulation data, and determine the load condition as a target load condition when the simulation accuracy is greater than a preset simulation accuracy threshold; an adjustment module, configured to iteratively adjust, for each working condition corresponding to each target load condition, a contact friction coefficient within the first simulation data of the rubber wheel under the working condition based on the first simulation data and experimental data, until a preset termination condition is satisfied, thereby obtaining a target contact friction coefficient of the rubber wheel under the working condition, and obtaining second simulation data obtained by simulating the rubber wheel under the working condition according to the corresponding target contact friction coefficient; A parameter identification module is used to perform parameter identification on the friction model of the rubber wheel based on the target ground friction coefficient of the rubber wheel under multiple working conditions corresponding to each target load condition and the corresponding second simulation data to obtain model parameters of the friction model.

9. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the method for identifying friction model parameters of a rubber wheel according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the friction model parameter identification method of the rubber wheel according to any one of claims 1 to 7 through the computer program.

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