ABSTRACT

Rolling resistance has become one of the key parameters that the vehicle industry is focusing on in their efforts to make vehicles more energy efficient. Rolling resistance is generally measured in steady state on a test drum that results in a higher rolling resistance than flat track measurements for the same test settings due to the curvature of the drum, which deforms the tire more. Therefore, the drum steady-state rolling resistance is commonly converted with Clark’s formula, as suggested in the rolling resistance measurement standards. Freudenmann et al. suggest an adjustment of Clark’s formula, claiming that it would improve the accuracy for steady-state conversions. The aim of this work is to compare non-steady-state drum and flat track measurements, performed at the same inflation pressure and tire temperature, to investigate whether Clark’s or Freudenmann’s formula can be used to convert the drum measurement to a corresponding flat track level when not in steady state. Non-steady-state measurements have been performed on both a test drum and a flat track. As expected, Freudenmann’s formula is not good for the conversion at non-steady-state settings because it was empirically developed for steady state. Clark’s formula works for non-steady-state conversions of measurements performed at the same tire temperature and inflation pressure. However, the dependency of rolling resistance on temperature is not the same in the drum and flat track measurements, causing a difference between the results that increases as the tire temperature decreases. Further research to improve Clark’s formula for non-steady-state measurements by including the effects of tire temperature would be beneficial.

ABSTRACT

A theory for multifield analysis of the consequences of sliding asperities on a rubber surface has been developed. The analysis considers (1) the multiaxial mechanical fields set up via the asperity contact, (2) the thermal field set up due to friction and heat generation, (3) the material property fields that evolve due to thermochemistry, and (4) the growth of crack precursors near the sliding interface. The theory considers two distinct Eulerian directions: the first direction considers strain history along streamlines oriented in the direction of sliding, and the second direction considers the progression of material toward the wear surface as material is removed. Considering the mechanical and thermal fields set up around an asperity, the theory simplifies what would otherwise be a large and complex analysis. The theory produces an estimate of the variation with depth of fatigue damage (residual life; as calculated via critical plane analysis), from which may be derived the wear rate for a particular surface sliding under a given set of conditions. Results of the calculation for two cases (two-dimensional sine wave and square wave asperity surfaces) with varying normal contact pressure are compared with Gent and Pulford’s blade abrader experiments.

ABSTRACT

This work addresses the challenge of modeling tire behavior for virtual vehicle development, particularly in untripped rollover scenarios where a vehicle rolls over solely due to the tire–road friction interface. Despite the existence of various models, limited attention has been given to tire testing methods that accurately represent rollover conditions. We propose a revised lateral tire testing method that uses data from rollover-critical driving tests to derive conditions for a flat-track test bench and that focuses on operational and thermal conditions. These data are used to parameterize empirical Magic Formula Tire 6.2 models, emphasizing the importance of accurate measurements for model parameterization. Component-level validation through comparison of measured and simulated tire reaction forces and moments demonstrates improved estimates of lateral forces, hence lateral friction coefficient, and overturning moments compared with models based on outdoor tire test data with limited operating conditions. At the full vehicle level, the proposed method significantly reduces the error in rollover key performance indicators based on wheel lift-off during Fishhook maneuvers. These findings are particularly relevant for battery electric sport utility vehicles with increased vehicle mass and wheel loads.

ABSTRACT

To address the challenges of lengthy development cycles and high testing costs in matching tire and vehicle mechanical characteristics, a fast and efficient virtual sampling method for tire mechanical properties is proposed. First, a detailed finite element model is established according to the material distribution diagram and material properties of the tire. Second, under the premise of ensuring simulation accuracy, structural simplifications and friction simplifications are applied to the detailed finite element model. A finite element friction subroutine is incorporated to accurately express the dynamic friction characteristics between the tire tread and the road surface. Then, the pure cornering and pure driving/braking mechanical characteristics of the tire are obtained through finite element simulations. With a high-precision combined-condition tire mechanical property prediction method, fast and accurate predictions of combined-condition forces and torques are made. Finally, the MF tire model is identified based on the data obtained from the finite element model and the prediction method. The results show that the finite element model can accurately obtain the lateral force, longitudinal force, and aligning moment of the tire in pure cornering and pure driving/braking, with an average accuracy of 93.4%, 88.4%, and 80.7%, respectively. Based on the pure condition data obtained from the tire finite element model, the mechanical properties under combined conditions are predicted, with average prediction accuracies of 92.82% for longitudinal force and 91.38% for lateral force. The predicted aligning torque exhibits a trend consistent with experimental results. The MF model is identified using the data from the tire finite element model and prediction method, achieving good accuracy for both forces and torques. The fast and efficient virtual sampling method for tire mechanical properties not only effectively shortens development cycles and reduces testing costs but also, by combining finite element models with predictive methods, enables the advancement of tire mechanical property development to the design stage, further enhancing the efficiency of virtual sampling for tire models.

ABSTRACT

To predict and prevent uneven tire wear in addition to a reduction of overall tire wear, it is essential to estimate not only the total amount of wear but also how the wear is distributed across the tire width. This requires knowledge of the frictional power distribution in the tire contact patch, which is the basis for calculating tire wear using a wear law. Usually, only 3D structural tire models can generate such distributed contact results. However, they involve high computational costs and cannot be used for comprehensive optimization of a vehicle’s suspension system with respect to tire wear characteristics. Hence, this contribution presents a methodology on how to accelerate the prediction of the frictional power distribution using two components: The structural tire model is replaced by an empirical tire model that on its own is not able to generate distributed contact results. Therefore, an artificial neural network is trained to predict the desired contact results from the kinematic quantities calculated by the empirical tire model. In the initial training phase, both components are fitted to data generated by the original complex tire model. After training, the empirical tire model can replace the structural tire model in vehicle simulations, resulting in significantly shorter calculation times. The simulation results are fed into the artificial neural network, which predicts the frictional power distributions over the tire width with negligible additional effort. Overall, the methodology reduces calculation time for the prediction of tire wear based on virtual test drives to approximately 25% of the time needed when using structural tire models.

ABSTRACT

Measuring normal and tangential forces on tires is crucial for enhancing tire performance under various road conditions and environmental settings. The measurement of these forces has been challenging because of limitations in sensing technology related to rigidity, durability, and sensitivity. This research introduces an innovative method that utilizes flexible sensors made of ionic liquid to address the limitations. Through the utilization of the distinctive properties of ionic liquids, such as their flexibility, enhanced sensitivity, and exceptional stability, a multilayer sensor has been manufactured. This sensor consists of carbon nanotube electrodes, ionic liquid dispersed in polymer creating a pressure-sensitive layer, and polymer insulating layers. The focus of this study is on the development of the sensor and understanding how its output changes under varying normal and tangential forces. Preliminary testing has shown that the sensor exhibits distinct and measurable responses to both force components, highlighting its potential for accurate force differentiation. The research evaluates the sensor’s performance and efficacy under varied force conditions, demonstrating its capability to accurately measure both normal and tangential forces. Such measurements are vital for the development of intelligent tires, offering deeper insights into critical tire parameters such as braking or traction force coefficients, contact patch characteristics, vehicle dynamics, and road surface conditions, leading to the improvement of safety, efficiency, and performance of tires.

ABSTRACT

Finite element analysis has become a standard tool in the engineer’s toolbox for tire and vehicle development work, with the goal of simulations replacing early prototypes and tests becoming a reality. However, 50 years ago this was just a glimmer on the horizon, with most of the fundamental model features and solution methodologies used in today’s tire simulations, which are now taken for granted as routine, not existing in the finite element codes. Hence, tire researchers in the early 1970s began developing and incorporating technologies into the codes so that basic tire mechanics could be represented, allowing increasing levels of tire performance simulations to be performed. The current model and simulation capabilities have been built upon this foundation of research and applications to give engineers a wide range of tire performance solutions that can be entrusted to virtually develop tires and vehicles for production. This article reviews the early years of the development and application of tire finite element analyses, giving current users of the method an understanding of the foundational work that was done and the main people involved in its development.

ABSTRACT

To develop applicable friction and wear models on tire scale, reliable test data are required. Consequently, friction tests on block level are requested because the distribution of contact pressure as well as slip velocity is nearly homogeneous at the contact surface of the sliding rubber block. However, wear mechanism and energy intensity levels of sliding rubber blocks and rolling rubber wheels or tires differ significantly. Consequently, linking both sliding and rolling frictional abrasion is required; thus, a wear model for rubber material is introduced to consider both deformation slip and sliding. The model input for sliding friction and resulting wear rate is derived from linear friction test experiments using sliding rubber blocks at different loading. A unique and sophisticated re-mesh algorithm ensures proper mesh modification due to abrasion of the structure. A wear energy evolution approach is developed to consider low abrasion with small sliding distances to predict wear at rolling rubber wheels. The simulation framework of abrasion modeling is successfully validated using laboratory abrasion tests.

ABSTRACT

This study investigates and provides a framework for addressing a reported tire- sourced cabin noise in the mid-frequency range of 300–500 Hz for an electric vehicle (EV). Noise, vibration, and harshness (NVH) of EVs present new challenges compared with internal combustion engine vehicles because of significant design changes between the two vehicle types. In turn, the tire–road interaction noise becomes a more significant source of cabin noise in EVs. In this regard, some prominent EV manufacturers recently reported relatively high measured cabin noise, particularly in the mid-frequency range of 300–500 Hz. Identifying the root cause(s) of noise in that range is a challenging task, as both tire structure–borne and airborne sources are contributors in that frequency range of elevated noise.

The current work presents the results of an experimental modal analysis to provide insight into some potential sources of the reported noise for an all-season tire/wheel assembly designed for an EV. The subsequent parametric simulations, conducted via the tire–vehicle finite element model, evaluate some of the mitigation solutions for the reported noise.