Improvement of vehicle and tire model for accurate estimation of steering response and stability

Abstract

To predict steering response, this paper proposes a string tire model based on relaxation length and a vehicle model which is designed by using effective cornering force. The cornering force, lateral static and distortion characteristics of a tire are significant factors that describe steering response. Nevertheless, it is difficult to define the contribution of each factor when a tire’s cornering motion is evaluated through subjective assessment. A new concept of tire model, which adopts distortion stiffness( $K_D$ ), is defined. A modified vehicle dynamics model is adapted to represent tire force lag and converted cornering stiffness ( $C_{\alpha}$ ) based on effective cornering force. This integration scheme is proposed to provide a more accurate modelling for steering response performance. The integration model is confirmed by dynamic response and validated through comparison with real data. The test results demonstrate the ability of this approach to predict steering response. To predict stability, lateral tire force is fundamental factor that describes the stability for handling performance. Attempts to analysis the vehicle stability have been made through with various objective test method and some specific factors, such as yaw, lateral acceleration and roll angle. However it remains the difficulty to identify which axle has a lack of grip. Indoor tire force measurement system is widely used. However, it cannot represent a real road and vehicle conditions. In this paper, lateral tire forces are estimated and nonlinear tire model is proposed. Nonlinear lateral force estimation algorithms are described based on a vehicle and tire model using the discrete-time Extended Kalman-Busy Filter (EKBF). A vehicle model has an eleven degrees of freedom which contains lateral and wheel dynamics of tires. A nonlinear tire model is described based on estimation forces and the Magic Formula tire model. The proposed empirical model is validated through comparison with real lateral force that measured by test vehicle equipped steering robot and dynamic motion sensors.