Tire wear prediction method and device, electronic equipment and computer program product

Abstract

The invention discloses a tire wear prediction method and device, electronic equipment and a computer program product. The method comprises the steps that the longitudinal acceleration, the slip angle and road spectrum excitation data of a to-be-detected tire of a vehicle are obtained, the longitudinal acceleration is used for determining the tire longitudinal force of the to-be-detected tire, and the road spectrum excitation data is used for determining the tire normal force of the to-be-detected tire; determining slip rates corresponding to the tire longitudinal force and the tire normal force according to a preset slip rate table; the slip angle and the slip rate are analyzed by adopting a friction energy model under a preset composite slip condition, the friction energy density of the contact surface of the tire to be detected on the unit area is obtained, and the friction energy density is a direct physical representation of the wear rate; and predicting the wear of the tire to be detected according to the friction energy density. The technical problem of low prediction precision of tire wear in the prior art is solved.

Description

Technical Field

[0001] This application relates to the field of tire dynamics, and more specifically, to a method, apparatus, electronic device, and computer program product for predicting tire wear. Background Technology

[0002] Tire wear is a critical technical issue that cannot be ignored during the operation of heavy-duty vehicles. It not only directly affects tire life and vehicle operating economy, but has also become a significant source of non-exhaust particulate matter emissions from road traffic. Under heavy-duty conditions, vehicles often bear large axle loads and complex stress environments, resulting in significantly higher wear intensity than passenger cars. Therefore, tire wear prediction is of great practical significance in engineering development.

[0003] Currently, tire wear prediction methods in the industry mainly fall into two categories: empirical regression models and finite element simulation models. Empirical regression methods rely on a large amount of experimental data, establishing a correlation model between wear and variables such as load, speed, tire pressure, and temperature through statistical methods. Although low-cost and simple in structure, the applicability of these models is limited, and they lack sensitivity to changes in vehicle structure, road conditions, and random driving conditions, especially in characterizing the differentiated wear behavior between lateral ribs on the tire tread. The finite element method (FEA) can accurately solve for tread contact pressure, shear stress, and local slip distribution under static or quasi-static conditions, offering high theoretical accuracy. However, its modeling is complex and computationally intensive, making it difficult to meet the prediction needs under long-mileage, random excitation, and multi-factor coupling conditions, and it also struggles to achieve real-time or near-real-time data interaction with the vehicle dynamics model.

[0004] There is currently no effective solution to the problem of low accuracy in predicting tire wear in the existing technologies mentioned above. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and computer program product for predicting tire wear, in order to at least solve the technical problem of low prediction accuracy of tire wear in the prior art.

[0006] According to one aspect of the embodiments of this application, a method for predicting tire wear is provided, comprising: acquiring longitudinal acceleration, sideslip angle, and road spectrum excitation data of a tire to be tested of a vehicle, wherein the longitudinal acceleration is used to determine the longitudinal force of the tire to be tested, the sideslip angle represents the angle between the contact surface of the tire to be tested and the driving direction of the vehicle, and the road spectrum excitation data represents the road surface roughness and is used to determine the normal force of the tire to be tested; determining the slip ratio corresponding to the longitudinal force and the normal force of the tire according to a preset slip ratio table, wherein the preset slip ratio table is used to represent the mapping relationship between the longitudinal force and the normal force of the tire and the slip ratio, and the slip ratio is used to represent the relative movement of the contact surface of the tire to be tested and the ground; analyzing the sideslip angle and the slip ratio using a pre-set friction energy model under composite slip conditions to obtain the friction energy density per unit area of ​​the contact surface of the tire to be tested, wherein the friction energy density is a direct physical characterization of the wear rate; and predicting the wear of the tire to be tested based on the friction energy density.

[0007] Optionally, before acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested of the vehicle, the method further includes: acquiring the vehicle's driving state, wherein the vehicle driving state includes: straight-line state, normal turning state, and aggressive turning state; determining the slip angle of the tire to be tested on the steering axis of the vehicle based on the vehicle driving state; determining the slip angle of the tire to be tested on the non-steering axis of the vehicle based on the slip angle of the tire to be tested on the steering axis of the vehicle and a pre-set steering scaling factor between the steering axis and the non-steering axis, wherein the steering scaling factor is determined at least based on the geometric relationship between the steering axis and the non-steering axis on the vehicle and the towing structure between the steering axis and the non-steering axis.

[0008] Optionally, the non-steering axle includes a drive axle and a towing axle. Determining the slip angle of the tire to be tested on the non-steering axle of the vehicle based on the slip angle of the tire to be tested on the steering axle of the vehicle and a pre-set steering scaling factor between the steering axle and the non-steering axle includes: determining a first scaling factor based on the geometric relationship between the steering axle and the drive axle on the vehicle and the towing structure between the steering axle and the drive axle; determining the slip angle of the tire to be tested on the drive axle of the vehicle based on the product of the first scaling factor and the slip angle of the tire to be tested on the steering axle of the vehicle; determining a second scaling factor based on the geometric relationship between the steering axle and the towing axle on the vehicle and the towing structure between the steering axle and the towing axle; and determining the slip angle of the tire to be tested on the towing axle of the vehicle based on the product of the second scaling factor and the slip angle of the tire to be tested on the steering axle of the vehicle.

[0009] Optionally, after acquiring the longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire to be tested of the vehicle, the method further includes: acquiring the vehicle's driving state, wherein the vehicle driving state includes: straight-line state, normal turning state, and aggressive turning state; and determining the standard deviation of the fluctuation of the sideslip angle based on the vehicle driving state, wherein the standard deviation of the fluctuation is used to characterize the degree of random distribution of the sideslip angle under different vehicle driving states, and the standard deviation of the fluctuation of the sideslip angle in the straight-line state is smaller than the standard deviation of the fluctuation of the sideslip angle in the normal turning state, and smaller than the standard deviation of the fluctuation of the sideslip angle in the aggressive turning state.

[0010] Optionally, after acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested of the vehicle, the method further includes: acquiring the vehicle's total mass; determining the tire longitudinal force of the tire to be tested on the drive axle of the vehicle based on the product of the total mass and the longitudinal acceleration; determining the tire longitudinal force of the tire to be tested on the non-drive axle of the vehicle based on the tire longitudinal force of the tire to be tested on the drive axle of the vehicle and a pre-set normal scaling factor between the drive axle and the non-drive axle, wherein the normal scaling factor is determined at least based on the axle load ratio of the drive axle and the non-drive axle.

[0011] Optionally, after acquiring the longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire to be tested, the method further includes: analyzing the road spectrum excitation data using a pre-set vehicle dynamics model to determine the tire normal force of the tire to be tested caused by road undulations, wherein the vehicle dynamics model is used to describe the force influence of road surface roughness, vehicle mass changes, and vehicle suspension dynamics on the tire to be tested.

[0012] Optionally, after acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested, the method further includes: dividing the tread of the tire to be tested into multiple longitudinal rib pattern regions; and determining the tire normal force of each longitudinal rib pattern region based on the position of each longitudinal rib pattern region on the tread.

[0013] According to another aspect of the embodiments of this application, a tire wear prediction device is also provided, comprising: an acquisition module, configured to acquire longitudinal acceleration, slip angle, and road spectrum excitation data of a tire to be tested of a vehicle, wherein the longitudinal acceleration is used to determine the longitudinal force of the tire to be tested, the slip angle represents the angle between the contact surface of the tire to be tested and the driving direction of the vehicle, and the road spectrum excitation data represents the road surface roughness and is used to determine the normal force of the tire to be tested; and a determination module, configured to determine the correspondence between the longitudinal force and the normal force of the tire according to a preset slip ratio table. The slip ratio is defined as follows: a preset slip ratio table is used to represent the mapping relationship between the longitudinal force and normal force of the tire and the slip ratio, and the slip ratio is used to represent the relative movement of the contact surface of the tire under test with the ground; an analysis module is used to analyze the sideslip angle and the slip ratio using a preset friction energy model under composite slip conditions to obtain the friction energy density of the contact surface of the tire under test per unit area, wherein the friction energy density is a direct physical characterization of the wear rate; and a prediction module is used to predict the wear of the tire under test based on the friction energy density.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the above-described method for predicting tire wear.

[0015] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the above-described tire wear prediction method.

[0016] The embodiments described above acquire longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire to be tested, enabling accurate calculation of the slip ratio corresponding to the longitudinal and normal forces of the tire. The slip ratio is a direct indicator of the relative movement between the tire contact surface and the ground, and its magnitude directly affects the tire's wear rate. A frictional energy model under composite slip conditions is used to convert the sideslip angle and slip ratio into frictional energy density per unit area on the tire contact surface. Frictional energy density essentially reflects the degree of energy dissipation due to friction on the tire contact surface during driving, and is a physical representation of the tire wear rate. By predicting tire wear based on frictional energy density, the technical effect of significantly improving the accuracy of tire wear prediction is achieved, thereby solving the problem of low prediction accuracy in existing technologies for tire wear. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0018] Figure 1 A hardware block diagram of an electronic device for implementing a method for predicting tire wear is shown.

[0019] Figure 2 This is a flowchart of a method for predicting tire wear according to an embodiment of this application;

[0020] Figure 3 This is a schematic diagram of a preset slip ratio table according to an embodiment of this application;

[0021] Figure 4 This is a schematic diagram of a quarter-car suspension model according to an embodiment of this application;

[0022] Figure 5 This is a schematic diagram of a finite element simulation of the imprint of multiple longitudinal rib pattern regions according to an embodiment of this application;

[0023] Figure 6 This is a schematic diagram of tread rib-level wear according to an embodiment of this application;

[0024] Figure 7 This is a schematic diagram of a tire wear prediction device according to an embodiment of this application. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Attempts have been made to couple driving conditions with wear mechanisms from a vehicle dynamics perspective. For example, a quarter-car model is used in conjunction with the friction energy formula to analyze the vertical load changes caused by road excitation and study the wear variation from a suspension design perspective. However, the focus of such research is still mainly on the impact of vertical load fluctuations on wear, failing to systematically consider the combined effects of multiple factors commonly present in real-world vehicle driving, such as longitudinal driving force input, steering slip angle changes, and road spectrum excitation. Furthermore, the models are mostly based on single tires, making it difficult to reflect the dynamic coupling, load transfer, and multi-axle force coordination among the steering axle, drive axle, and trailer axle in multi-axle vehicles. Simultaneously, existing methods generally lack analytical mapping between friction energy and vehicle dynamic conditions, and do not possess the ability to independently extrapolate wear for different rib areas of the tire tread, making it impossible to conduct detailed analysis of actual phenomena such as lateral wear and premature wear of the central rib. Therefore, although such research has some reference value for tire wear mechanisms, the interpretability, scalability, and engineering applicability of its models remain significantly insufficient.

[0028] Therefore, a systematic method has yet to be developed that can rapidly calculate composite slip friction energy analytically based on vehicle dynamics response and simultaneously provide refined predictions of wear distribution at different wheel positions and tire rib areas in multi-axle vehicles. Especially under random driving conditions, where longitudinal acceleration, sideslip angle sequences, and road surface excitation all exhibit time-dependent and statistically distributed characteristics, existing methods struggle to accurately capture the evolution of wear accumulation over time. Therefore, it is necessary to propose a novel wear prediction method that couples vehicle dynamics and tire friction energy to overcome the limitations of existing technologies and improve the accuracy and engineering applicability of wear prediction for heavy-duty vehicles under complex conditions.

[0029] To address the problems existing in related technologies, embodiments of this application provide a method for predicting tire wear, which can be implemented in... Figure 1 The computer terminal shown is explained below.

[0030] The methods and embodiments provided in this application can be executed on mobile terminals, computer terminals, or similar computing devices. Figure 1 A hardware block diagram of an electronic device (or computer terminal, or mobile device) for implementing a method to predict tire wear is shown. Figure 1 As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (shown as 102a, 102b, ..., 102n in the figure) 102 (processor 102 may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 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 a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] It should be noted that the aforementioned one or more processors 102 and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be embodied, in whole or in part, in software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be integrated, in whole or in part, into any other element within the computer terminal 10 (or mobile device). As involved in the embodiments of this application, the data processing circuits serve as a processor control mechanism (e.g., selection of a variable resistor termination path connected to an interface).

[0032] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the tire wear prediction method in this embodiment. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the above-mentioned application vulnerability detection method. The memory 104 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 instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0033] The transmission device 106 is used to receive or send data via a network. Specific examples of the network described above may include a wireless network provided by the communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0034] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10 (or mobile device).

[0035] It should be noted here that, in some optional embodiments, the above... Figure 1 The computer terminal shown may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that... Figure 1This is only one instance of a specific particular instance, and is intended to illustrate the types of components that may exist in the aforementioned computer terminal.

[0036] In the above operating environment, this application provides an embodiment of a method for predicting tire wear. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0037] Figure 2 This is a flowchart of a method for predicting tire wear according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps:

[0038] Step S202: Obtain the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested of the vehicle. The longitudinal acceleration is used to determine the longitudinal force of the tire to be tested, the slip angle represents the angle between the contact surface of the tire to be tested and the driving direction of the vehicle, and the road spectrum excitation data represents the road surface roughness and is used to determine the tire normal force of the tire to be tested.

[0039] Step S204: Determine the slip ratio corresponding to the tire longitudinal force and the tire normal force according to the preset slip ratio table. The preset slip ratio table is used to represent the mapping relationship between the tire longitudinal force and the tire normal force and the slip ratio. The slip ratio is used to represent the relative movement of the contact surface of the tire to be tested and the ground.

[0040] Step S206: The sideslip angle and slip ratio are analyzed using a pre-set friction energy model under composite slip conditions to obtain the friction energy density per unit area of ​​the contact surface of the tire under test. The friction energy density is a direct physical characterization of the wear rate.

[0041] Step S208: Based on the friction energy density, predict the wear of the tire to be tested.

[0042] The embodiments described above acquire longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire to be tested, enabling accurate calculation of the slip ratio corresponding to the longitudinal and normal forces of the tire. The slip ratio is a direct indicator of the relative movement between the tire contact surface and the ground, and its magnitude directly affects the tire's wear rate. A frictional energy model under composite slip conditions is used to convert the sideslip angle and slip ratio into frictional energy density per unit area on the tire contact surface. Frictional energy density essentially reflects the degree of energy dissipation due to friction on the tire contact surface during driving, and is a physical representation of the tire wear rate. By predicting tire wear based on frictional energy density, the technical effect of significantly improving the accuracy of tire wear prediction is achieved, thereby solving the problem of low prediction accuracy in existing technologies for tire wear.

[0043] The tire wear prediction method provided in the above embodiments of this application improves the applicability of the model, enabling it to adapt to different vehicle types and complex road conditions; it solves the problem of dynamic coupling of multi-axle vehicles, making tire wear prediction more comprehensive; it reduces computational costs and improves prediction efficiency and engineering practicality; thus, it can significantly improve the accuracy and efficiency of tire wear prediction, providing important technical support for tire management of heavy-duty vehicles and has broad application prospects.

[0044] In step S202 above, the longitudinal acceleration, sideslip angle and road spectrum excitation data can be obtained by the vehicle's tire under real driving conditions, for example, by real vehicle road test data; or they can be obtained by simulation through a stochastic process model.

[0045] It should be noted that the randomness and diversity of real driving conditions are important sources of differences in wear. Therefore, operating conditions such as longitudinal acceleration, sideslip angle and road spectrum excitation data can be generated based on statistical models and simulated through stochastic process models to ensure that the input has stability and scalability.

[0046] Alternatively, longitudinal acceleration, sideslip angle, and road spectrum excitation data can be obtained by loading CAN data, using industry driving cycles (such as CHDC, WHVC), or using driver model output.

[0047] Optionally, when simulating longitudinal acceleration, sideslip angle, and road spectrum excitation data through a stochastic process model, the operating conditions such as longitudinal acceleration, sideslip angle, and road spectrum excitation data can be generated based on the vehicle's operating characteristics.

[0048] Alternatively, regardless of the input source, as long as acceleration, sideslip angle, and road excitation data can be provided, it is acceptable.

[0049] It should be noted that the longitudinal acceleration of the tire under test is mainly affected by the acceleration and deceleration of the vehicle. Therefore, the longitudinal acceleration of the tire under test can also represent the longitudinal acceleration of the vehicle.

[0050] In step S202 above, longitudinal acceleration and sideslip angle can be sequential data representing the vehicle's driving process. For example, a vehicle will accelerate and decelerate during driving, and the longitudinal acceleration sequence can represent the longitudinal acceleration of the tire under test at each stage. As another example, as the road changes, the vehicle will not always travel in a straight line, nor will it always turn at a certain angle. Therefore, the sideslip angle of the vehicle's tire will also change, and the sideslip angle sequence can represent the sideslip angle of the tire under test at each stage.

[0051] Optionally, the longitudinal acceleration sequence can be constructed using a random acceleration signal generation module, wherein the constructed acceleration is sampled from a truncated normal distribution based on a set mean and standard deviation, and smoothed using a first-order filter to eliminate unreasonable high-frequency fluctuations. Example parameters are as follows:

[0052] Mean: μ = 0.05 m / s²;

[0053] Standard deviation: σ = 1.5 m / s²;

[0054] Cutoff interval: [-2, 2] m / s²;

[0055] Sampling time step: Δt = 0.1 s;

[0056] Total duration: T=1000s.

[0057] Optionally, the slip angle sequence can be generated using a slip angle generation module. The slip angle of the steering axle is determined by the vehicle's driving state, while the slip angles of the drive axle and trailer axle are generated based on the vehicle's geometry and the towing structure, and are calculated through linear or nonlinear mapping. This slip angle sequence provides the lateral force input for each wheel position, laying the foundation for subsequent composite slip calculations.

[0058] In step S202 above, the road spectrum excitation data describes the roughness characteristics of the road surface and is typically used in simulation analysis of vehicle dynamics and tire performance. This data can be obtained from field measurements or generated from standard models, such as the A to D levels of road spectra defined in the ISO 8608 standard, each corresponding to different road surface qualities. Each level of road spectrum has its specific power spectral density (PSD) function, which can be used to simulate road surfaces with different roughness and frequency components.

[0059] As an optional example, for the longitudinal acceleration sequence and the sideslip angle sequence, tire wear prediction can be performed for the corresponding stage based on the acceleration and sideslip angle in the sequence, and then the tire wear prediction results of multiple stages in the sequence are accumulated to obtain the total tire wear of the tire to be tested during the entire driving process.

[0060] The embodiments described above in this application, by introducing longitudinal acceleration, sideslip angle and road spectrum excitation data driven by real working conditions, overcome the limitations of traditional empirical models that cannot adapt to different vehicles and complex road conditions, enabling the model to capture the impact of actual driving behavior on tire force and wear.

[0061] In step S204 above, the preset slip ratio table can be a "longitudinal force-radial force (i.e., normal force)-slip ratio table" exported in advance using vehicle system dynamics simulation software.

[0062] Figure 3This is a schematic diagram of a preset slip ratio table according to an embodiment of this application, such as... Figure 3 As shown, the slip ratio corresponding to the tire longitudinal force and tire normal load can be found in the preset slip ratio table.

[0063] In step S206 above, the friction energy model can be used to determine the sideslip angle. Coupled with the slip ratio s, the shear strain and equivalent slip distance within the contact area (i.e., ground contact area) between the tire and the road surface are obtained. Furthermore, the frictional energy density generated per unit ground contact area per revolution of the tire under test was determined. .

[0064] As an optional example, in vehicle braking The frictional energy model includes at least:

[0065] ;

[0066] ;

[0067] .

[0068] As an optional example, in vehicle driving The frictional energy model includes at least:

[0069] ;

[0070] ;

[0071] .

[0072] As an alternative example, when obtaining the combined shear stress Subsequently, the frictional energy model also includes:

[0073] ;

[0074] .

[0075] It should be noted that s is the slip ratio. Side slip angle, For longitudinal shear stress, It is the transverse shear stress. Let x be the resultant shear stress, and x be the distance from the foremost point of the imprint. The coefficient of sliding friction is This is the longitudinal equivalent slip stiffness coefficient. C is the lateral equivalent slip stiffness coefficient, l is the combined equivalent slip stiffness coefficient, and l is the imprint length. This is the length of the tread viscous zone. Let qz(x) be the vertical stress. This is the tread slip distance. Frictional energy density.

[0076] It should be noted that this frictional energy density represents the amount of energy dissipated by the local rubber during the grounding process, and is a direct physical characterization of the wear rate.

[0077] Optionally, after obtaining the frictional energy density, it can be multiplied by a coefficient k (which represents factors such as driving environment and tread material) to obtain the tread thickness worn per unit distance. Therefore, within each step (unit distance), the wear is gradually subtracted from the current tread thickness to achieve dynamic evolution of wear with mileage.

[0078] In the above embodiments of this application, the longitudinal slip ratio is calculated based on the force state of each wheel position, and the shear stress under the combined action of the longitudinal slip ratio and the sideslip angle is substituted into the composite slip friction energy model (i.e., the friction energy model) to obtain the slip distance and friction energy density per unit ground contact area, thereby realizing the conversion of vehicle force into energy dissipation of the tread material.

[0079] Optionally, the mapping relationship between wear and energy can be parametrically replaced according to different material properties.

[0080] As an optional embodiment, before acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested of the vehicle, the method further includes: acquiring the vehicle's driving state, wherein the vehicle driving state includes: straight state, normal turning state, and aggressive turning state; determining the slip angle of the tire to be tested on the steering axis of the vehicle based on the vehicle driving state; determining the slip angle of the tire to be tested on the non-steering axis of the vehicle based on the slip angle of the tire to be tested on the steering axis of the vehicle and a pre-set steering scaling factor between the steering axis and the non-steering axis, wherein the steering scaling factor is determined at least based on the geometric relationship between the steering axis and the non-steering axis on the vehicle and the towing structure between the steering axis and the non-steering axis.

[0081] In the embodiments described above, during vehicle operation, the vehicle's driving state can be categorized into straight-line state, normal turning state, and aggressive turning state. Since the vehicle's steering axis controls its direction of travel, the slip angle of the tire to be tested on the steering axis can be determined more accurately based on the vehicle's driving state. The tire to be tested on the non-steering axis is driven by the tire to be tested on the steering axis to perform follow-up steering. Therefore, the slip angle of the tire to be tested on the non-steering axis needs to be affected by the slip angle of the tire to be tested on the steering axis. Considering the geometric relationship between the steering axis and the non-steering axis on the vehicle, as well as the towing structure between them, a steering scaling coefficient between the steering axis and the non-steering axis is preset. Based on this steering scaling coefficient, the slip angle of the tire to be tested on the non-steering axis is further calculated and determined. This allows the slip angles of different wheel positions to reasonably reflect the dynamic characteristics of the vehicle under actual driving conditions. Based on the accurately obtained slip angles of the tires to be tested at each wheel position, wear prediction of each tire to be tested can be accurately performed, achieving a significant improvement in the accuracy of tire wear prediction and solving the problem of low accuracy in tire wear prediction in the prior art.

[0082] The embodiments described above in this application can more accurately calculate the lateral force of the tire under different driving conditions, thereby optimizing the calculation of composite slip friction energy, forming a tire wear prediction that is closer to reality, and improving the accuracy of wear prediction.

[0083] As an alternative example, the steering scaling factor can be further adjusted based on the specific vehicle's suspension characteristics, tire type, and road conditions to adapt to different vehicle platforms and operating conditions, ensuring the accuracy of wear prediction and engineering practicality.

[0084] It should be noted that under normal turning conditions, the tire slip angle is small, and the vehicle is less likely to slip during the turning process (that is, the tire is less likely to experience slip friction); under aggressive turning conditions, the tire slip angle is large, and the vehicle is more likely to slip during the turning process (that is, the tire is more likely to experience slip friction, such as drifting). Therefore, different vehicle driving conditions will result in different tire pressures, and the slip angle of the tire being tested on the non-steering axle will also differ from that of the tire being tested on the steering axle.

[0085] Optionally, the above embodiments of this application can be applied to a wider range of vehicle driving scenarios, improving the adaptability and prediction accuracy of the model.

[0086] As an optional embodiment, the non-steering axle includes a drive axle and a trailing axle. Based on the sideslip angle of the tire to be detected on the steering axle of the vehicle and a preset steering scaling coefficient between the steering axle and the non-steering axle, determining the sideslip angle of the tire to be detected on the non-steering axle of the vehicle includes: determining a first scaling coefficient based on the geometric relationship between the steering axle and the drive axle on the vehicle and the trailing structure between the steering axle and the drive axle; and determining the sideslip angle of the tire to be detected on the drive axle of the vehicle based on the product of the first scaling coefficient and the sideslip angle of the tire to be detected on the steering axle of the vehicle; determining a second scaling coefficient based on the geometric relationship between the steering axle and the trailing axle on the vehicle and the trailing structure between the steering axle and the trailing axle; and determining the sideslip angle of the tire to be detected on the trailing axle of the vehicle based on the product of the second scaling coefficient and the sideslip angle of the tire to be detected on the steering axle of the vehicle.

[0087] In the above embodiment of the present application, the non-steering axle includes a drive axle and a trailer axle. The sideslip angle of the tire on the steering axle and the sideslip angle of the tire on the non-steering axle form a dynamic association based on the vehicle geometric relationship and the trailing structure. Based on the geometric layout and the trailing structure among the steering axle, the drive axle, and the trailer axle on the vehicle, considering the relative position and the trailer following characteristics between the steering axle and the drive axle or the trailer axle, the dynamic changes in the sideslip angles among the steering axle, the drive axle, and the trailer axle can be accurately captured, and the force characteristics of the tires under various working conditions such as turning, accelerating, and decelerating of the vehicle can be systematically evaluated. Furthermore, the accuracy and reliability of tire wear prediction are improved, achieving the technical effect of significantly enhancing the accuracy of tire wear prediction and solving the technical problem of low prediction accuracy of tire wear in the prior art.

[0088] Optionally, the sideslip angle of the tire to be detected on the steering axle is: α(t); the sideslip angle of the tire to be detected on the drive axle is: α_d(t)=k1α(t), where 0.1 < k1 < 0.3 and k1 is the first scaling coefficient; the sideslip angle of the tire to be detected on the trailer axle is: α_t(t)=-k2α(t), where 0.05 < k2 < 0.2 and k2 is the second scaling coefficient.

[0089] It should be noted that for the trailer axle, considering its reverse sideslip behavior during turning, the second scaling coefficient k2 is introduced to ensure that its relationship with the sideslip angle of the steering axle is α_t(t)=-k2α(t).

[0090] Optionally, the method for determining the steering scaling coefficient of the sideslip angle can also be optimized according to multi-body dynamics modeling, vehicle measured data, or other advanced simulation technologies, which can further enhance the adaptability to different vehicle platforms and driving behaviors and improve the engineering practicality of the prediction model.

[0091] It should be noted that any method for calculating the steering scaling factor that helps to reveal the physical relationship between the tire slip angle and the vehicle's dynamic response can effectively couple the composite slip friction energy with the vehicle's driving state, thereby promoting the development of wear prediction towards a more accurate and efficient direction.

[0092] Optionally, in practical applications, by dynamically adjusting the steering scaling factor, the tire slip angle can be accurately estimated under different driving modes and road conditions, providing a solid dynamic basis for tire wear prediction, thereby making the prediction results closer to real-world performance.

[0093] The embodiments described above in this application, through a refined side slip angle scaling strategy, not only consider the dynamic coupling between tires, but also take into account the differences in force between the steering axis and the non-steering axis, so that the prediction model can more realistically reflect the driving characteristics of multi-axle vehicles on complex roads.

[0094] The above embodiments of this application can adjust the calculation rules of the steering scaling factor according to different vehicle types or driving conditions, for example, by using more complex multibody dynamics models or on-site measured data to refine the steering scaling factor of the sideslip angle.

[0095] It should be noted that regardless of the method used to determine the steering scaling factor, as long as a physical mapping from vehicle driving state to tire force and wear difference can be achieved, it should be considered an equivalent implementation of this invention. This comprehensive consideration from macroscopic dynamics to microscopic material energy conversion greatly improves the accuracy and reliability of tire wear prediction, providing strong data support and technical means for vehicle tire management and maintenance.

[0096] As an optional embodiment, after acquiring the longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire to be tested of the vehicle, the method further includes: acquiring the vehicle's driving state, wherein the vehicle driving state includes: straight-line state, normal turning state, and aggressive turning state; and determining the standard deviation of the sideslip angle for random fluctuations based on the vehicle driving state, wherein the standard deviation of the fluctuation is used to characterize the degree of random distribution of the sideslip angle under different vehicle driving states, and the standard deviation of the sideslip angle in the straight-line state is smaller than the standard deviation of the sideslip angle in the normal turning state, and smaller than the standard deviation of the sideslip angle in the aggressive turning state.

[0097] In the embodiments described above, after acquiring the longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire under test, the method further includes analyzing the vehicle's driving state, which can be straight-line driving, normal turning, or aggressive turning. Based on the different driving states, the standard deviation of the sideslip angle's random fluctuations can be determined. This standard deviation quantifies the degree of random distribution of the sideslip angle under different driving states. Specifically, in straight-line driving, the standard deviation of the sideslip angle's fluctuation is smaller than that in normal turning, and the standard deviation in normal turning is smaller than that in aggressive turning. This design can more accurately reflect the actual changes in the vehicle's sideslip angle under different driving conditions, thereby improving the accuracy and reliability of tire wear prediction. By distinguishing the degree of random fluctuation of the slip angle under different driving conditions, the dynamic response of the tire during vehicle driving can be simulated more realistically. Especially under steering and heavy-load cornering conditions, the impact of increased slip angle and fluctuation on tire wear can be fully considered. This helps to reveal the complex relationship between vehicle driving mode and tire wear, providing a more detailed and scientific basis for tire optimization design and vehicle performance improvement. In turn, it improves the accuracy and reliability of tire wear prediction, achieving a significant improvement in the accuracy of tire wear prediction and solving the technical problem of low prediction accuracy of existing technologies.

[0098] As an alternative example, the "state segment method" can be used to describe different vehicle driving states, such as: state 0 (straight ahead), state 1 (normal turn), state 2 (small radius heavy tail turn). The length of each segment follows a geometric distribution, with the straight ahead segment being the longest and the heavy tail segment being the shortest.

[0099] The vehicle steering behavior generation method based on the state segment method in the above embodiments of this application discretizes the continuous driving steering behavior into a finite number of steering states in the absence of real driver input, and describes the continuous process of each state in a "segment" manner, and then constrains the statistical characteristics and evolution law of the steering angle by the state.

[0100] As an optional example, vehicle driving conditions are divided into several typical states, each corresponding to a steering intensity level, including:

[0101] State 0: Straight-line or minor correction driving state;

[0102] State 1: Normal turning state;

[0103] State 2: Sharp turn or small radius turn;

[0104] The above states do not represent specific instantaneous steering angle values, but are used to characterize the strength level and statistical characteristics of the frequency of steering behavior within a certain driving distance.

[0105] As an optional tax rate, a different random amplitude scale (standard deviation σ) is provided for each vehicle's driving state. The statistical mapping relationship from state to steering angle is as follows:

[0106] The straight-line state (state 0) corresponds to a small steering angle fluctuation, with a small amplitude scale (σ=1.1).

[0107] A typical turning state (state 1) corresponds to a moderate steering angle, with a moderate amplitude scale (σ=2).

[0108] The aggressive turning state (state 2) corresponds to a larger steering angle and is more likely to reach the limit range. The amplitude scale is very large (σ=8).

[0109] Finally, high-frequency mutations are suppressed by smoothing filtering.

[0110] Alternatively, the relationship between the sideslip angle and driving conditions can also be expressed by other functional forms, as long as they can reflect the influence of driving conditions on the random fluctuations of the sideslip angle, they fall within the scope of protection of this invention. This method of simulating sideslip angle fluctuations based on driving conditions provides a more comprehensive mechanical perspective for tire wear assessment, which helps to achieve refined management of tire wear in engineering practice.

[0111] As an optional embodiment, after acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested of the vehicle, the method further includes: acquiring the vehicle's total mass; determining the tire longitudinal force of the tire to be tested on the vehicle's drive axle based on the product of the total mass and the longitudinal acceleration; and determining the tire longitudinal force of the tire to be tested on the vehicle's non-drive axle based on the tire longitudinal force of the tire to be tested on the vehicle's drive axle and a pre-set normal scaling factor between the drive axle and the non-drive axle, wherein the normal scaling factor is determined at least based on the axle load ratio of the drive axle and the non-drive axle.

[0112] In the embodiments described above, the longitudinal force of the tire to be tested on the drive axle of the vehicle is the product of the vehicle's total mass and longitudinal acceleration. For non-drive wheels, considering their smaller traction, the total driving force is distributed through a set axle load ratio and a normal scaling factor for tire-road adhesion to determine the longitudinal force of the tires on the non-drive axle. Thus, after acquiring the vehicle's longitudinal acceleration, sideslip angle, and road spectrum excitation data, not only is the longitudinal force of the tires on the drive axle calculated, but also the longitudinal force of the tires on the non-drive axle is determined based on the axle load ratio and tire-road adhesion. This step ensures that the model can comprehensively reflect the force characteristics of the vehicle at different axle positions. This longitudinal force distribution strategy based on vehicle dynamics response ensures the accuracy of the force state at each wheel position, providing a reliable foundation for subsequent calculations of composite slip friction energy, thereby improving the accuracy and reliability of tire wear prediction. It achieves a significant improvement in the accuracy of tire wear prediction, solving the problem of low prediction accuracy in existing technologies.

[0113] Optionally, the longitudinal force distribution mode can be parametrically adjusted to adapt to different vehicle models and operating conditions. For example, more complex first-order or higher-order filters can be used to smooth the acceleration signal, or the axle load ratio and scaling factor can be dynamically adjusted based on real-time vehicle data. This enhances the flexibility and applicability of this application in predicting tire wear, enabling it to better serve tire wear prediction and performance evaluation for heavy-duty vehicles.

[0114] As an optional embodiment, after acquiring the longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire to be tested, the method further includes: analyzing the road spectrum excitation data using a pre-set vehicle dynamics model to determine the tire normal force of the tire to be tested caused by road undulations, wherein the vehicle dynamics model is used to describe the force effects of road surface roughness, vehicle mass changes, and vehicle suspension dynamics on the tire to be tested.

[0115] In the embodiments described above, after acquiring the longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire under test, a pre-set vehicle dynamics model is further used to analyze the road spectrum excitation data. This model focuses on describing the influence of road surface unevenness, vehicle mass changes, and suspension dynamics on the forces acting on the tire under test, especially the tire normal force. Through this dynamics model, the fluctuation of the tire normal load caused by road undulations can be accurately calculated, providing key force state parameters for subsequent friction energy calculations. This ensures that the model can accurately capture the changes in tire load bounce effect during actual vehicle operation, enhancing the model's physical consistency and engineering applicability. Consequently, it improves the accuracy and reliability of tire wear prediction, achieving a significant improvement in the accuracy of tire wear prediction and solving the problem of low prediction accuracy in existing technologies.

[0116] Alternatively, the vehicle dynamics model can be a quarter-car suspension model.

[0117] Figure 4 This is a schematic diagram of a quarter-car suspension model according to an embodiment of this application, as shown below. Figure 4 As shown, the ISO 8608 A–D level road spectrum model is used to generate road surface roughness sequences based on power spectral density (PSD). Road spectrum excitation data is applied as input to a quarter-vehicle suspension model to calculate the fluctuations in tire normal load (i.e., tire normal force) caused by road surface undulations. This suspension model comprehensively considers the spring stiffness and damping coefficients of the suspension, sidewall, and tread. The time-varying tire normal load (i.e., tire normal force) is obtained by solving the nonlinear second-order system equations, as shown in the following calculation method:

[0118] ;

[0119] ;

[0120] ;

[0121] .

[0122] in, For road spectrum, For vehicle body quality, For unsprung mass, This refers to tire mass (which changes dynamically with tire wear). For suspension springs, For suspension damping; For tire sidewall springs, For tire sidewall damping; For tire tread springs 1. Tread damping; , and These represent the vertical displacements of the suspended mass, the unsuspended mass, and the tire mass, respectively. Active suspension control force: Since this model uses a passive control suspension, the value is 0. For the initial static load, This is the normal load (i.e., the tire normal force).

[0123] It should be noted that this normal load (i.e., tire normal force) includes the load fluctuation effect during actual driving, enabling the model to describe the influence of road surface unevenness, vehicle mass changes, and suspension dynamics on tire forces.

[0124] The embodiments described above in this application establish a measurement dynamics model of a multi-axle vehicle based on the working condition input. By using longitudinal dynamics and a quarter-vehicle model, the longitudinal force and normal load (i.e., normal force) of each wheel position are calculated, thereby obtaining the force state of the tire during the entire driving process, providing a basis for subsequent friction energy calculation.

[0125] Alternatively, the vehicle dynamics model can also employ a half-vehicle model, a seven-DOF vehicle model, or a multibody dynamics model (such as Adams) to improve the accuracy of describing steering, couplings, and load transfer.

[0126] It should be noted that by using whole-vehicle dynamic response calculation, the load transfer between each wheel position of a multi-axle vehicle, the longitudinal driving force, and the influence of road surface undulations on the tire loading process can be accurately described, thereby avoiding the defect of single-wheel models in the prior art that cannot reflect the dynamic coupling of multiple axles.

[0127] It should be noted that replacing the dynamic model does not change the core idea of ​​this application. As long as the forces such as Fx, Fy, and Fz can be obtained, it falls within the protection scope of this application.

[0128] The embodiments described above utilize vehicle dynamics models to meticulously analyze the dynamic response of tires under different road conditions, including how tires respond to road surface unevenness and how vehicle mass and suspension dynamics interact with the tires, thereby affecting their normal force. These analytical results are crucial for understanding the tire wear mechanism in actual use and help to more accurately predict tire wear under various driving conditions. Therefore, by implementing this dynamic model analysis step, a more comprehensive and accurate tire wear prediction method can be provided, effectively overcoming the limitations of existing technologies, such as the inability to accurately reflect the dynamic coupling of multi-axle vehicles and the wear differences in different areas of the tire tread. In summary, the dynamic model analysis in this embodiment lays a solid physical foundation for refined tire wear prediction, helping to ensure tire lifespan and vehicle operational safety.

[0129] As an optional embodiment, after acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested, the method further includes: dividing the tread of the tire to be tested into multiple longitudinal rib pattern regions; and determining the tire normal force of each longitudinal rib pattern region based on its position on the tread.

[0130] In the embodiments described above, when the tread of the tire to be tested is divided into multiple longitudinal rib pattern areas, the vehicle dynamic response calculation will determine the tire normal force borne by each longitudinal rib based on its specific position on the tread. The implementation of this technical solution means that longitudinal acceleration, slip angle, and road spectrum excitation data are further refined to different rib areas of the tread, thereby making the force analysis of each part of the tire more accurate. By integrating the calculation results of the friction energy density of each longitudinal rib pattern area, the accumulated wear amount of each area over time or mileage can be obtained, thus accurately describing the wear differences between different areas. This method not only improves the accuracy of wear prediction but also effectively captures the wear trend between the central rib and shoulder ribs of the tread, providing more detailed data support for tire wear assessment and vehicle operating condition optimization, thereby improving the accuracy and reliability of tire wear prediction and achieving a significant improvement in the accuracy of tire wear prediction, solving the technical problem of low prediction accuracy in existing technologies.

[0131] Figure 5 This is a schematic diagram of a finite element simulation of the imprint of multiple longitudinal rib pattern regions according to an embodiment of this application, such as... Figure 5 As shown, to achieve a localized description of tread wear, this application divides the tread laterally into multiple longitudinal rib patterns (ribs, for example, 5 ribs). The ground pressure ratio of each rib is obtained through finite element simulation, measured imprints, or empirical pressure models, and the frictional energy density is calculated independently for each rib. Finally, in each step, the wear amount is subtracted from the remaining thickness of the rib to form a cumulative wear curve, realizing the dynamic evolution of wear with mileage.

[0132] Optionally, the position of the longitudinal rib area and the distribution of normal forces can be adjusted according to specific tire structure and operating conditions to adapt to different tire types and driving conditions, further enhancing the versatility and flexibility of this application. Of course, this adjustment does not change the core principle of this invention, namely, predicting tire wear based on real mechanical mechanisms, thereby ensuring the physical consistency and engineering applicability of the prediction results.

[0133] The embodiments described above in this application, combined with the distribution of tread ground pressure, divide the tread into multiple rib regions, calculate the corresponding frictional energy density for each rib, and obtain the wear amount accumulated over time or mileage based on the energy-wear mapping relationship, thereby reflecting the differentiated wear characteristics between different regions of the tread. Based on the wear evolution calculation results, the wear amount of each wheel position of a multi-axle vehicle, the wear distribution of each rib of the tread, and the trend of wear change with mileage are output, ultimately realizing the overall prediction of vehicle tire wear, realizing rib-level wear description, and improving the ability to understand and analyze the differences in tread wear.

[0134] It should be noted that as long as the rib partitioning method and the pressure distribution model can achieve partitioned energy calculation, it belongs to the equivalent scheme of this application.

[0135] Figure 6 This is a schematic diagram of tread rib-level wear according to an embodiment of this application, as shown below. Figure 6 As shown, the wear trend of each longitudinal rib pattern area of ​​the tire tread with mileage is obtained through program processing.

[0136] As an optional example, using the above-described operating condition inputs, dynamic response calculations, composite slip solutions, and rib partition energy integration, this application can output:

[0137] 1) Wear curves for different wheel positions (steering wheel, drive wheel, trailer wheel).

[0138] 2) Differential wear distribution of each rib (central rib, shoulder rib, etc.).

[0139] 3) The evolution trend of tire tread wear with mileage obtained through program processing.

[0140] The above embodiments of this application can realistically reproduce the wear patterns of heavy-duty vehicles, such as the highest wear on the drive wheels and the tendency for uneven wear on the central rib, proving the physical consistency and engineering applicability of this model.

[0141] The embodiments described above in this application, by constructing modules such as operating condition input, dynamic response, composite slip friction energy calculation, and rib-level wear evolution, realize a clear physical link from the actual driving conditions of the vehicle to the local wear of the tread, thereby overcoming the problems of poor model applicability, difficulty in multi-axis coupling, insufficient rib accuracy, and low computational efficiency in the prior art. A wear prediction method based on the coupling of vehicle dynamics and tire friction energy is adopted. Through the construction of operating condition input modules, dynamic response modules, composite slip friction energy models, and rib-level wear evolution models, the physical link mapping from the vehicle driving conditions to the local wear of the tread is realized. Furthermore, through the composite slip friction energy model, the shear stress caused by longitudinal slip ratio and sideslip angle is uniformly converted into energy density, making the wear prediction originate from physical dissipation rather than empirical fitting, overcoming the problems of large computational load and difficulty in long-range simulation of finite element models. At the same time, dividing the tread into multiple rib regions and performing energy partitioning calculations effectively solves the limitations of existing methods that cannot be refined to the rib level and cannot describe details such as uneven wear.

[0142] This implementation provides a complete and reproducible method flow. Each module (acceleration generation, sideslip angle generation, dynamic model, friction energy model, pressure distribution model, etc.) can be replaced by various alternative methods. As long as the physical link of "random working condition → force calculation → friction energy → wear evolution" is satisfied, it belongs to the equivalent implementation form of this application.

[0143] The embodiments described above enable wear prediction based on real mechanical mechanisms, significantly improving computational efficiency while maintaining model accuracy. This allows for rapid prediction of wear differences between different wheel positions (steering wheels, drive wheels, and trailer wheels) and tread rib areas. Simulation results show that this application can accurately reproduce the common wear trend of highest wear on drive wheels, followed by steering wheels, and lowest wear on trailer wheels, and can quantitatively distinguish the wear difference between the central rib and the shoulder rib. This method has a clear structure and well-defined physical mechanism, good scalability, and can support wear assessment needs for new vehicle path conditions, different tire structures, and multi-axle vehicle platforms.

[0144] Figure 7 This is a schematic diagram of a tire wear prediction device according to an embodiment of this application, as shown below. Figure 2 As shown, the device includes: an acquisition module 72, used to acquire the longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire under test of the vehicle, wherein the longitudinal acceleration is used to determine the longitudinal force of the tire under test, the sideslip angle represents the angle between the contact surface of the tire under test and the driving direction of the vehicle, and the road spectrum excitation data represents the road surface unevenness and is used to determine the normal force of the tire under test; a determination module 74, used to determine the slip ratio corresponding to the longitudinal force and the normal force of the tire based on a preset slip ratio table, wherein the preset slip ratio table is used to represent the mapping relationship between the longitudinal force and the normal force of the tire and the slip ratio, and the slip ratio is used to represent the relative movement between the contact surface of the tire under test and the ground; an analysis module 76, used to analyze the sideslip angle and slip ratio using a pre-set friction energy model under composite slip conditions to obtain the friction energy density per unit area of ​​the contact surface of the tire under test, wherein the friction energy density is a direct physical characterization of the wear rate; and a prediction module 78, used to predict the wear of the tire under test based on the friction energy density.

[0145] The embodiments described above acquire longitudinal acceleration, sideslip angle, and road spectrum excitation data of the tire to be tested, enabling accurate calculation of the slip ratio corresponding to the longitudinal and normal forces of the tire. The slip ratio is a direct indicator of the relative movement between the tire contact surface and the ground, and its magnitude directly affects the tire's wear rate. A frictional energy model under composite slip conditions is used to convert the sideslip angle and slip ratio into frictional energy density per unit area on the tire contact surface. Frictional energy density essentially reflects the degree of energy dissipation due to friction on the tire contact surface during driving, and is a physical representation of the tire wear rate. By predicting tire wear based on frictional energy density, the technical effect of significantly improving the accuracy of tire wear prediction is achieved, thereby solving the problem of low prediction accuracy in existing technologies for tire wear.

[0146] It should be noted that the tire wear prediction device can be used to execute the tire wear prediction method in the embodiments of the present invention. Therefore, the relevant explanations in the above tire wear prediction method also apply to the tire wear prediction device, and will not be repeated here.

[0147] It should be noted that each module in the above-mentioned tire wear prediction device can be a program module (for example, a set of program instructions to implement a certain function) or a hardware module. For the latter, it can be manifested in the following forms, but is not limited to them: each of the above modules is manifested as a processor, or the functions of each of the above modules are implemented by a processor.

[0148] As an optional embodiment, the device further includes: a first acquisition submodule, configured to acquire the vehicle's driving state before acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested, wherein the vehicle driving state includes: straight-line state, normal turning state, and aggressive turning state; a first determination submodule, configured to determine the slip angle of the tire to be tested on the vehicle's steering axis based on the vehicle driving state; and a second determination submodule, configured to determine the slip angle of the tire to be tested on the vehicle's non-steering axis based on the slip angle of the tire to be tested on the vehicle's steering axis and a pre-set steering scaling factor between the steering axis and the non-steering axis, wherein the steering scaling factor is determined at least based on the geometric relationship between the steering axis and the non-steering axis on the vehicle and the towing structure between the steering axis and the non-steering axis.

[0149] As an optional embodiment, the non-steering axle includes a drive axle and a towing axle. The second determining submodule includes: a first determining unit, configured to determine a first scaling factor based on the geometric relationship between the steering axle and the drive axle on the vehicle and the towing structure between the steering axle and the drive axle; and to determine the slip angle of the tire to be detected on the drive axle of the vehicle based on the product of the first scaling factor and the slip angle of the tire to be detected on the steering axle of the vehicle; and a second determining unit, configured to determine a second scaling factor based on the geometric relationship between the steering axle and the towing axle on the vehicle and the towing structure between the steering axle and the towing axle; and to determine the slip angle of the tire to be detected on the towing axle of the vehicle based on the product of the second scaling factor and the slip angle of the tire to be detected on the steering axle of the vehicle.

[0150] As an optional embodiment, the device further includes: a second acquisition submodule, used to acquire the vehicle driving state after acquiring the longitudinal acceleration, sideslip angle and road spectrum excitation data of the tire to be tested of the vehicle, wherein the vehicle driving state includes: straight state, normal turning state and aggressive turning state; and a third determination submodule, used to determine the standard deviation of the fluctuation of the sideslip angle based on the vehicle driving state, wherein the standard deviation of the fluctuation is used to characterize the degree of random distribution of the sideslip angle under different vehicle driving states, and the standard deviation of the fluctuation of the sideslip angle in the straight state is smaller than the standard deviation of the fluctuation of the sideslip angle in the normal turning state and smaller than the standard deviation of the fluctuation of the sideslip angle in the aggressive turning state.

[0151] As an optional embodiment, the device further includes: a third acquisition submodule, used to acquire the vehicle mass after acquiring the longitudinal acceleration, slip angle and road spectrum excitation data of the tire to be tested; a fourth determination submodule, used to determine the tire longitudinal force of the tire to be tested on the drive axle of the vehicle based on the product of the vehicle mass and the longitudinal acceleration; and a fifth determination submodule, used to determine the tire longitudinal force of the tire to be tested on the non-drive axle of the vehicle based on the tire longitudinal force of the tire to be tested on the drive axle of the vehicle and a pre-set normal scaling factor between the drive axle and the non-drive axle, wherein the normal scaling factor is determined at least based on the axle load ratio of the drive axle and the non-drive axle.

[0152] As an optional embodiment, the device further includes: a sixth determining submodule, used to analyze the road spectrum excitation data using a pre-set vehicle dynamics model after acquiring the longitudinal acceleration, sideslip angle and road spectrum excitation data of the tire to be tested of the vehicle, and to determine the tire normal force of the tire to be tested caused by road undulations, wherein the vehicle dynamics model is used to describe the force effects of road surface roughness, vehicle mass changes and vehicle suspension dynamics on the tire to be tested.

[0153] As an optional embodiment, the device further includes: a division submodule, used to divide the tread of the tire under test into multiple longitudinal rib pattern regions after acquiring the longitudinal acceleration, slip angle and road spectrum excitation data of the tire under test of the vehicle; and a seventh determination submodule, used to determine the tire normal force of each longitudinal rib pattern region based on the position of each longitudinal rib pattern region on the tread.

[0154] This application also provides an electronic device, which includes a memory and a processor, wherein the memory is used to store program instructions; the processor is connected to the memory and is used to execute steps of the method for predicting tire wear in various embodiments of this application.

[0155] This application also provides a non-volatile storage medium including a stored computer program, wherein the device containing the non-volatile storage medium executes the steps of the tire wear prediction method in various embodiments of this application by running the computer program.

[0156] This application also provides a computer program product, including computer instructions that, when executed by a processor, implement the steps of the tire wear prediction method in various embodiments of this application.

[0157] This application also provides a computer program that, when executed by a processor, implements the steps of the tire wear prediction method in various embodiments of this application.

[0158] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0159] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0161] The units described as separate components may or may not be physically separate. The 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 can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0163] If the integrated unit is implemented as 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 this application, in essence, or the part that contributes to related technologies, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0164] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for predicting tire wear, characterized in that, include: The longitudinal acceleration, slip angle, and road spectrum excitation data of the tire under test of the vehicle are acquired. The longitudinal acceleration is used to determine the longitudinal force of the tire under test, the slip angle represents the angle between the contact surface of the tire under test and the driving direction of the vehicle, and the road spectrum excitation data represents the road surface roughness and is used to determine the tire normal force of the tire under test. Based on a preset slip ratio table, the slip ratios corresponding to the tire longitudinal force and the tire normal force are determined. The preset slip ratio table is used to represent the mapping relationship between the tire longitudinal force and the tire normal force and the slip ratio. The slip ratio is used to represent the relative movement of the contact surface of the tire under test with the ground. The sideslip angle and the slip ratio are analyzed using a pre-set friction energy model under composite slip conditions to obtain the friction energy density per unit area of ​​the contact surface of the tire under test, wherein the friction energy density is a direct physical characterization of the wear rate. Based on the frictional energy density, the wear of the tire under test is predicted.

2. The method according to claim 1, characterized in that, Before acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire under test of the vehicle, the method further includes: The vehicle's driving status is obtained, including: straight-line state, normal turning state, and sharp turning state. Based on the vehicle's driving state, determine the slip angle of the tire to be tested on the vehicle's steering axle; The slip angle of the tire to be tested on the non-steering axle of the vehicle is determined based on the slip angle of the tire to be tested on the steering axle of the vehicle and a pre-set steering scaling factor between the steering axle and the non-steering axle, wherein the steering scaling factor is determined at least based on the geometric relationship between the steering axle and the non-steering axle on the vehicle and the towing structure between the steering axle and the non-steering axle.

3. The method according to claim 2, characterized in that, The non-steering axle includes a drive axle and a towing axle. Based on the slip angle of the tire to be tested on the vehicle's steering axle and a pre-set steering scaling factor between the steering axle and the non-steering axle, the slip angle of the tire to be tested on the vehicle's non-steering axle is determined to include: Based on the geometric relationship between the steering shaft and the drive shaft on the vehicle, and the towing structure between the steering shaft and the drive shaft, a first scaling factor is determined; and based on the product of the first scaling factor and the slip angle of the tire to be tested on the steering shaft of the vehicle, the slip angle of the tire to be tested on the drive shaft of the vehicle is determined. Based on the geometric relationship between the steering shaft and the towing shaft on the vehicle, and the towing structure between the steering shaft and the towing shaft, a second scaling factor is determined; and based on the product of the second scaling factor and the slip angle of the tire to be tested on the steering shaft of the vehicle, the slip angle of the tire to be tested on the towing shaft of the vehicle is determined.

4. The method according to claim 1, characterized in that, After acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested on the vehicle, the method further includes: The vehicle's driving status is obtained, including: straight-line state, normal turning state, and sharp turning state. Based on the vehicle's driving state, the standard deviation of the fluctuation of the sideslip angle is determined. The standard deviation of the fluctuation is used to characterize the degree of random distribution of the sideslip angle under different vehicle driving states. The standard deviation of the fluctuation of the sideslip angle in the straight-line state is smaller than the standard deviation of the fluctuation of the sideslip angle in the normal turning state and smaller than the standard deviation of the fluctuation of the sideslip angle in the intense turning state.

5. The method according to claim 1, characterized in that, After acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested on the vehicle, the method further includes: Obtain the overall vehicle weight; Based on the product of the vehicle mass and the longitudinal acceleration, the longitudinal force of the tire to be tested on the drive axle of the vehicle is determined; The longitudinal force of the tire to be tested on the tire on the drive axle of the vehicle is determined based on the longitudinal force of the tire on the drive axle of the vehicle and a pre-set normal scaling factor between the drive axle and the non-drive axle, wherein the normal scaling factor is determined at least based on the axle load ratio of the drive axle and the non-drive axle.

6. The method according to claim 1, characterized in that, After acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested on the vehicle, the method further includes: The road spectrum excitation data is analyzed using a pre-set vehicle dynamics model to determine the tire normal force of the tire under test caused by road undulations. The vehicle dynamics model is used to describe the influence of road surface roughness, vehicle mass changes and vehicle suspension dynamics on the force of the tire under test.

7. The method according to claim 1, characterized in that, After acquiring the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested on the vehicle, the method further includes: The tread of the tire to be tested is divided into multiple longitudinal rib pattern areas; The tire normal force of each longitudinal rib pattern area is determined based on the position of each longitudinal rib pattern area on the tire tread.

8. A device for predicting tire wear, characterized in that, include: The acquisition module is used to acquire the longitudinal acceleration, slip angle, and road spectrum excitation data of the tire to be tested of the vehicle. The longitudinal acceleration is used to determine the longitudinal force of the tire to be tested, the slip angle represents the angle between the contact surface of the tire to be tested and the driving direction of the vehicle, and the road spectrum excitation data represents the road surface roughness and is used to determine the tire normal force of the tire to be tested. The determination module is used to determine the slip ratio corresponding to the longitudinal force and the normal force of the tire based on a preset slip ratio table. The preset slip ratio table is used to represent the mapping relationship between the longitudinal force and the normal force of the tire and the slip ratio. The slip ratio is used to represent the relative movement of the contact surface of the tire to be tested with the ground. The analysis module is used to analyze the sideslip angle and the slip ratio using a pre-set friction energy model under composite slip conditions, and to obtain the friction energy density per unit area of ​​the contact surface of the tire under test, wherein the friction energy density is a direct physical characterization of the wear rate. The prediction module is used to predict the wear of the tire to be tested based on the friction energy density.

9. An electronic device, characterized in that, include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the tire wear prediction method according to any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the tire wear prediction method according to any one of claims 1 to 7.

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