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The use of accurate tire material properties is a major requirement for conducting a successful tire analysis using finite element method (FEM). Obtaining these material properties however poses a major challenge for tire modelers and researchers due to the complex nature of tire material and associated proprietary protections of constituent material properties by tire manufactures. In view of this limitation, a simple and effective procedure for generating tire materials data used in tire finite element analysis (FEA) is presented in this paper. All the tire test specimens were extracted from a tire product based on special considerations such as specimen dimension and shape, test standard, precondition of specimen and test condition for cords. The required material properties of tire rubber component, including hyperelasticity and viscoelasticity were obtained using simple uni-axial tension test.

Disc brake noise is recognized as a major problem of the automotive industry. Various experimental and numerical techniques have been developed to model the noisy brake and investigate possible solutions. Developing a virtual model of the disc brake which can accurately reproduce the behavior of the brake unit under different conditions is a considerable step forward towards reaching this goal. Among various aspects of the analytical model of a disc brake, application of the correct value of damping based on the material properties and functional frequency range of each component is a significant factor in ensuring correct prediction of the brake system behavior. Complex Eigenvalue Analysis is well established as a tool for predicting brake instabilities which can potentially lead to brake noise. However, it is known to over-predict instabilities i.e. predict instabilities which do not occur in the real brake system.

This paper presents a statistical characterisation of the effects of variations in vehicle parameters on vehicle road load data using a quarter vehicle as a case study. A model of a quarter vehicle test rig constructed from a commercial SUV is created in a multi-body dynamics (MBD) simulation environment to reproduce the real-life behaviour of the SUV. The model is thereafter validated by correlating the response data collected from both the model and laboratory test rig to the same road input. In order to ensure that only the effects of the variation of the vehicle parameters are captured, a time domain drive signal for a kerb strike road event on the physical vehicle is generated from the proving ground data collected during durability testing of the vehicle.

This research proposes the use of Artificial Neural Networks (ANN) to predict the road input for road load data generation for variants of a vehicle as vehicle parameters are modified. This is important to the design engineers while the vehicle variant is still in the initial stages of development, hence no prototypes are available and accurate proving ground data acquisition is not possible. ANNs are, with adequate training, capable of representing the complex relationships between inputs and outputs. This research explores the implementation of the ANN to predict road input for vehicle variants using a quarter vehicle test rig. The training and testing data for this research are collected from a validated quarter vehicle model.

The development of intelligent tire technology from concept to application covers multi-disciplinary fields. During the course of development, the computational method can play a significant role in understanding tire behavior, assisting in the design of the intelligent tire prototype system and in developing tire parameters estimation algorithm, etc. In this paper, a finite element tire model was adopted for developing a strain-based intelligent tire system. The finite element tire model was created considering the tire's composite structure and nonlinear properties of its constituent materials, and the FE model was also validated by physical tests. The FE model is used to study tire strain characteristics by steady state simulation for straight line rolling, traction and braking, as well as cornering. Tire loading conditions were estimated by feature extraction and data fitting.

Sloped medians provide a run-off area for errant vehicles so that they can be safely stopped off-road with or without barriers placed in the sloped median. However, in order to optimize the design of sloped medians and the containment barriers, it is essential to accurately model the behavior of vehicles on such sloped terrain surfaces. In this study, models of a vehicle fleet comprising a small sedan and a pickup truck and sloped terrain surface are developed in CarSim™ to simulate errant vehicle behavior on sloped median. Full-scale crash tests were conducted using the vehicle fleet driven across a 9.754 meters wide median with a 6:1 slope at speeds ranging from 30 to 70 km/h. Measured data such as the lateral accelerations of the vehicle as well as chassis rotations (roll and pitch) were synchronized with the vehicle motion obtained from the video data.

Nowadays, rolling resistance sits at the core of tyre development goals because of its considerable effect on the car’s fuel economy. In contrast to the experimental method, the finite element (FE) method offers an inexpensive and efficient estimation technique. However, the FE technique is yet to be a fully developed product particularly for rolling-resistance estimation. An assessment is conducted to study the role of material viscoelasticity representation in FE, in linear and non-linear forms, through the use of Prony series and parallel rheological framework (PRF) models, respectively, on the tyre’s rolling-resistance calculation and its accuracy. A unique approach was introduced to estimate the rolling resistance according to the tyre’s hysteresis energy coefficient.