1. Introduction
New energy vehicles are environmentally friendly vehicles that can alleviate energy shortages and reduce greenhouse gas emissions. With countries around the world announcing the cessation of production and sales of fuel powered vehicles, new energy vehicles have gradually become the mainstream. Among them, electric vehicles are currently the most popular type. It is worth considering that the special power system of electric vehicles can also bring some new problems. For tires, electric vehicles of the same specification are heavier than fuel vehicles, which can lead to faster tire wear [
1].
Tire wear refers to the destruction of macromolecular chains or chemical bond between molecular chains of rubber layer caused by the sliding between tire and road. Wear can be divided into uniform wear and abnormal wear, with abnormal wear including uneven wear in the width direction and irregular wear in the circumferential direction [
2].
So far, research on wear has mainly focused on two aspects: predicting wear and controlling wear. There are three main methods for predicting wear. One is the theoretical analysis method, which predicts wear by establishing a mathematical model. Li et al. [
3] employed a combination of theoretical analysis and numerical simulation to develop a tire wear formula that incorporates temperature effects and vehicle dynamic characteristics. Huang et al. [
4] established a three-dimensional tire wear model based on the brush model as a function of road roughness and vehicle dynamic characteristics under the condition of camber angle. Wang et al. [
5] combined an improved ring model with the brush model to develop the tire wear model and analyzed the quantitative relationship between contact characteristics and wear. Chen et al. [
6] proposed a new three degree of freedom nonlinear vehicle dynamics model for multi axle steering vehicles, and calculated tire wear considering suspension, steering system, and toe in angle. Nguyen et al. [
7] considered the historical dependence and directionality of wear and established a rubber wear model, which was introduced into finite element analysis and verified through experimental data. Lepine et al. [
8] proposed an empirical tire wear model for heavy-duty multi axle vehicles based on route data and vehicle models. Nakajima et al. [
9] introduced a two-dimensional contact patch in the wear calculation, expanded the width direction, and predicted the wear process of tires under pure slip and combined slip conditions. Building upon the magic formula, Sakhnevych et al. [
10] extended its applicability by incorporating the effects of thermodynamics and wear state on tire mechanical properties. They developed a multiphysical magic formula.
The second is the experimental method. Stalnaker et al. [
11] obtained good consistency by comparing indoor drum measurement data with outdoor vehicle wear results. Runge et al. [
12] proposed a deeper understanding of the friction and wear mechanisms of tire tread based on micro-tread block wear experiments, emphasizing the importance of considering the real-time dynamic contact between the tire and the road surface.
The third method is finite element analysis (FEA). FEA is a numerical method used to obtain approximate solutions for engineering problems, which owes its development to the rapid advancement of computer technology and computer-aided engineering (CAE) techniques. Cho et al. [
13] considered the influence of contact pressure on the friction coefficient and used explicit finite element method to simulate tread wear of tires with complex patterns. Dionisio et al. [
14] and Li et al. [
15] used a finite element model to calculate the tread wear of tires containing only longitudinal groove patterns. Tamada et al. [
16] used the LS-DYNA to simulate wear on tires with complex tread patterns, taking into account uneven wear.
Controlling tire wear can be tackled from two angles: one is to consider the tire itself, while the other is to look at other parts of the vehicle such as the suspension. Implementing effective wear control measures can help to prolong the lifespan of tires and also minimize the impact of tire wear particles on the environment. Liang et al. [
17] used the skewness value of friction work in the grounding area to evaluate the tire’s uneven wear, and through FEA, optimized the running surface width and arc height, which effectively reduces the uneven wear of tires. Papaioannou et al. [
18] aimed to reduce tire wear and improve driving comfort and vehicle handling stability. The tire and suspension parameters have been optimized. Wang et al. [
19] adopted a virtual camber and optimal curved running surface method, effectively reducing the total wear of straddle-type monorail trains’ running wheel tires. Waquier et al. [
20] and Zhang et al. [
21] proposed improving the rubber material of the tire, which can increase the wear resistance of the tire.
Currently, there are two main methods for onboard evaluation of tire wear: machine vision and smart tires.
Due to the continuous advancement of machine vision technology, numerous efficient and accurate methods for calculating tire wear have emerged. Wang et al. [
22] processed the radial section image of the tire tread obtained from the laser plane to extract a single pixel centerline. The pixel coordinates of the centerline were then converted into world coordinates using calibration information. Contour curves of the tire’s radial section were obtained. Finally, an algorithm was developed to identify and locate tread grooves on the profile curve and calculate the depth of each groove. Zhu et al. [
23] proposed a method for image feature extraction and representation. They employed preprocessing and texture feature extraction techniques to analyze tire pattern wear images from a small sample database. They also utilized machine learning technology to establish mathematical models for wear degree estimation and develop a wear feature vector.
Smart tires utilize optical, strain, and acceleration sensors to capture dynamic response information from tires, enabling real-time wear detection. Zhang et al. [
24] proposed an intelligent tire information system that utilizes triaxial accelerometer data and strain gauge data to estimate tire wear. Li et al. [
25] presented an intelligent algorithm for predicting tire wear using an artificial neural network. The algorithm is based on the relationship among tire inflation pressure, load, tire wear, speed, and radial vibration frequency. In the above studies the research on tire wear mostly focuses on itself. However, in recent years, there has been a growing interest among researchers in exploring the impact of wear on the mechanical properties of tires. The most direct impact of tire wear is the change in tread height, which, as the only component of the vehicle that comes into contact with the ground, affects the mechanical properties of the tire. Todoroff et al. [
26] compared the wet grip performance of worn tires of the same model with new tires and explained the decrease in wet grip ability of worn tires through two aspects: rubber friction and water sliding mechanism. Wright et al. [
27] found that wear has an impact of approximately 10% on the longitudinal friction of tires. Becker et al. [
28] studied agricultural large lug tire and found that wear significantly affects the stiffness of the tires. Nantapuk et al. [
29] conducted a study comparing new tires with tires that had covered a distance of 50,000 km and observed an increase in the stiffness and energy absorption of the used tires.
Cornering characteristics are one of the important mechanical properties that seriously affect the handling stability. Lu et al. [
30] extended the wear conditions of the UniTire tire model and examined how different wear conditions affected cornering stiffness and aligning stiffness. The analysis was then compared with experimental data to validate the model’s accuracy. This research is important because it helps us better understand the impact of tire wear on tire performance, especially in terms of handling stability. Inspired by this study, this paper investigates the cornering properties of worn tires.
In this paper, a finite element model of the tire is first established for wear simulation, followed by secondary simulation of different worn tires obtained to obtain the lateral force and aligning torque for fitting cornering stiffness and aligning stiffness. Finally, a qualitative explanation is provided for the changes in the cornering characteristics of uneven wear tires from grounding characteristics. During the analysis, grounding characteristics are employed as intermediate variables to elucidate the impact of wear on cornering characteristics.
3. Wear Simulation
After establishing the finite element tire model, the subsequent step involves simulating wear. The tire wear simulation in ABAQUS utilizes the UMESHMOTION subroutine and ALE adaptive meshing. Specifically, the wear amount is calculated using the Archard model.
Figure 3 outlines the step-by-step procedure for conducting the simulation.
3.1. Archard’s Wear Law
This study exclusively focuses on the simulation of abrasive wear, which is widely recognized as the primary mechanism responsible for tire wear. Archard’s wear law, known as the most prevalent model for describing abrasive wear, is extensively employed by researchers investigating tire wear using ABAQUS. The Archard model, as a classical and well-established approach, provides a robust framework for understanding and analyzing wear on tires. Its main concept is to assume that wear is a linear function of the normal force and sliding distance at the contact interface [
31,
33].
where
is the wear volume of the tire tread material,
is the sliding distance,
is a nondimensional wear coefficient,
is the normal reaction force acting at the contact patch of the tire with the ground, and
is the material hardness.
The wear volume is a function of time and taking the derivative of Formula (1) with respect to time yields Formula (2).
where
is the volumetric material loss rate,
is the interface normal pressure,
is the interface area, and
is the interface slip rate.
Assuming that the wear of the tire is uniformly distributed and continuous in the circumferential direction, the expression for the wear volume of the tread per unit time is
where
is the time, and
is the current configuration position.
Due to the use of the Eulerian steady-state transport procedure, the above equation can be written as a time independent expression:
where
is a position along the streamline, and
is the width of the stream ribbon at position.
According to the physical meaning of material volume wear rate, the volume wear rate can also be represented by the wear rate
of the node material:
Equating Formulas (4) and (5) in a discrete form results in the following expression:
Assuming that the circumferential node wear rate on the tread is constant, the expression for wear rate can be obtained.
3.2. ALE and UMESHMOTION Subroutine
The ALE technique combines the advantages of pure Lagrangian and pure Eulerian analysis, which can maintain high-quality mesh during the simulation without changing the topology of the mesh. This reduces the possibility of non-convergence in the tire wear simulation process. The UMESHMOTION is an ABAQUS subroutine developed based on Fortran language, which can call utility routines (GetVRN, GetNODETOELEMCONN, GetVRMAVGATNODE) to obtain node information for calculating wear rate. The wear rate calculation is based on the Archard wear model, and the wear direction of internal nodes is determined by the average of the element facet normals near the node, while the node at the tread corners is directly defined by the vector along the edge of the tread [
31].
3.3. Simulation Operating Condition
The wear results are determined by the operating conditions of the tire. Different wear conditions can be generated by setting different inflation pressure, loads, time, and loading angles. Tire operating conditions exhibit significant variability in real-world scenarios. In this study, our objective was to simulate wear under various typical operating conditions, while considering the influence of the time factor. The specific operating conditions are shown in
Table 3. Conditions 1 and 3 are similar, except for the difference in operating time, and they represent pure lateral slip conditions. Conditions 2 and 5 are also similar, except for the difference in operating time, and they represent pure camber conditions. The purpose of including different operating times is to capture the varying degrees of wear under specific operating conditions. As for condition 4, it was intentionally designed to represent a scenario of high load and pure longitudinal slip. The coefficient of friction between the tire and the road is consistently set to 0.95, irrespective of the operating conditions. Modifying the key words in the INP file allows for changes in the operating conditions.
Based on these five operating conditions, five wear simulations were carried out on an un-worn 205/55R16 radial tire.
3.4. Result
To obtain accurate wear results, a path was created along the y-direction at the center of the contact area in the ABAQUS visualization to obtain the coordinates of the tread nodes before and after wear, and the wear depth was calculated. The wear category obtained from condition 1 is A, and so on, a total of five wear categories A, B, C, D, and E were obtained. The specific wear amounts for each level are shown in the
Figure 4.
The non-uniformity of the wear depth at the tread nodes is evident. Comparing the wear caused by different working conditions reveals that the wear amount of A and B are relatively lower, while the wear amount of C, D, and E are significantly higher. Moreover, the impact of different working conditions on tire wear varies greatly. However, regardless of the operating conditions, the wear peak of the tire tread nodes is located at the shoulder due to the peak of the contact pressure in that region.
In the measurement of tire wear, researchers commonly utilize the method of measuring tire groove depth and calculating its average value. This approach is adopted because it is challenging to obtain wear data from areas other than the grooves during physical tire wear evaluation. One limitation of this method is the limited number of data points available for measurement.
Finite element analysis can provide the wear amount for each tread node, which can then be used to assess the overall wear level by calculating the average of all node wear values. Although this method appears reasonable, it has certain limitations. In particular, tire grooves divide the tread into distinct zones, and each zone may exhibit varying wear patterns. The conventional approach of utilizing the overall average fails to consider these differences, necessitating a more precise evaluation method.
In this paper, a new tire wear evaluation system is presented, drawing inspiration from the methodology proposed by Wang et al. [
34]. The method involves dividing the tire tread into three distinct areas: the outer shoulder, crown, and inner shoulder, as shown in the
Figure 5. Subsequently, the wear in each area is analyzed.
The wear amounts of the tire were processed based on the division of the tread. Several indicators were calculated to evaluate the wear distribution of the tire, which include: the outer shoulder wear (), representing the average wear amount of the outer shoulder nodes; the inner shoulder wear (), indicating the average wear amount of the inner shoulder nodes; the crown wear (), showing the average wear amount of the crown nodes; and the global wear (), which is the average wear amount of all tread nodes.
Furthermore, an evaluation indicator
was proposed to quantify the difference in wear between the two shoulder regions. The calculation method of this evaluation index is given by Formula (8).
Wcd is used to differentiate between crown wear and global wear. Equation (9) is used for its calculation.
By utilizing the above indicators, the wear condition of the tire can be evaluated from multiple perspectives. The specific wear indicators are shown in
Table 4.
According to previous research, wear test results on 205/55R16 tires installed in real vehicles indicate an average wear of 1.41 mm per 10,000 km. In this paper, the wear rate is about 1.30 mm per 10,000 km. Hence, it is demonstrated that the wear results fall within the range of reliability. This result further validates the accuracy of the wear simulation process described in this paper.
The calculation methods for each wear indicator indicate that wg, wd, and wcd are derived as secondary calculations from wc, wos, and wis. Additionally, the small value of wcd suggests a relatively minor discrepancy between crown wear and global wear. Therefore, wg and wd can effectively capture the majority of wear information.
Based on the analysis of wear amounts, two groups were formed: Group I consists of samples A and B, while Group II consists of samples C, D, and E. The global wear in Group I is smaller than that in Group II. The difference in the global wear and wear in the crown within each group is relatively small and can be considered as equal. However, there is a significant difference in wear between the outer and inner shoulders, with the maximum difference being 82.86%. The reason for this phenomenon is that under the selected working conditions, there is no significant difference in the distribution of contact pressure in the crown, but there is a significant difference in the tire shoulders. At the same time, the wear amount calculated by the Archard model is directly proportional to the contact pressure, leading to a difference in wear between the two shoulders of a tire, commonly known as uneven wear.
4. Cornering Characteristics of Worn Tires
To investigate the impact of wear on cornering characteristics, a simulation analysis was conducted using ABAQUS/Standard on five types of worn tires (A–E) and one unworn tire (F). The simulation involved varying the slip angle within the range of −2 to 2 degrees, while maintaining a constant speed of 70 km/h and applying different loads of 2410.5 N, 4821 N, and 7231.5 N. Meanwhile, the effects of camber and longitudinal slip were not considered. The coefficient of friction between the tire and the road was specified as 0.95. The simulation results were then visualized using the ABAQUS Visualization module to generate plots of the lateral force and aligning torque curves. The detailed simulation results are shown in
Figure 6.
In order to provide a clearer description of the changes in lateral force and aligning torque under different wear states, fitting is performed for the lateral force and slip angle, as well as the aligning torque and slip angle. This allows for the extraction of parameters that represent the curve’s stiffness, namely the cornering stiffness and aligning stiffness.
The cornering stiffness is the lateral force generated by a unit slip angle, and the calculation formula is
The aligning stiffness is defined as the aligning torque generated by the unit slip angle, and the calculation formula is
The cornering stiffness and aligning stiffness of each tire under different loads are shown in the
Figure 7.
By comparing the cornering stiffness and aligning stiffness under different loads, we not only confirmed the conclusion similar to that in [
30], which indicates that both cornering stiffness and aligning stiffness increase proportionally with tire wear, but also found that the magnitude of the changes varies under different loads. Furthermore, an even more interesting observation was made. When the load is consistent and the
is the same, the effect of
on cornering stiffness is relatively small, while it has a significant impact on the aligning stiffness. The trend of this effect is that
is inversely proportional to the aligning stiffness. At low loads, the aligning stiffness exhibits a maximum difference of 8.0%, and even tire E shows a slightly lower aligning stiffness than the unworn tire. When subjected to rated load, the maximum difference of the aligning stiffness increases to 8.6%. Finally, at high loads, the maximum difference of the aligning stiffness decreases to 7.6%.