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This paper presents a tire slip-angle based speed control race driver model. In developing a chassis control system for enhancement of high-speed driving performance, analysis of the vehicle-driver interaction at limit handling is one of the main research issues. Thus, a driver model which represents driving characteristics in a racing situation is required to develop a chassis control system. Since a race driver drives a vehicle as fast as possible on a given racing line without losing control, the proposed driver model is developed to ensure a lateral stability. In racing situation, one of the reasons which cause the lateral instabilities is an excessive corner-entry speed. The lateral instability in that moment is hard to handle with only a steering control. To guarantee the lateral stability of the vehicle while maximizing a cornering speed, a desired speed is determined to retain a tire slip-angle that maximizes lateral tire forces without front tire saturation.

This paper describes a lateral disturbance estimator for an application to a Motor Driven Power Steering (MDPS)-based driving assistant system. A vehicle motion can be disturbed laterally by wind force or load from bank angle acting on the vehicle in the lateral direction. An MDPS-based driving assistant system can be used to reduce steering effort of a human driver in a driving situation with lateral disturbance. In designing the MDPS-based driving assistant system, the lateral wind disturbance should be estimated to determine an assistant torque. An estimator for the vehicle lateral disturbance estimation has been developed. The proposed estimator consists of two parts: a tire self-aligning torque estimator and the lateral disturbance estimator. The lateral disturbance estimator has been designed on the basis of a 2-DOF bicycle model with available sensor signals from the MDPS module. A numerical simulation has been conducted in order to evaluate the proposed estimator.

Research on HEV (hybrid electric vehicle) intracity buses has become a topic of interest because the well-known service routes of intracity buses and the frequent stop/go pattern make the energy management of the vehicle straightforward. Thus, the energy flow and the energy management of the intracity bus have been studied extensively in order to improve fuel economy. However, the HEV buses that have been studied previously were equipped with a single traction motor or with dual motors with the same capacity for the convenience of the equipment without considering the motoring or generating efficiency of the traction motor. Therefore, the energy flow from the engine/generator unit to the traction motor that has been optimized by many kinds of energy distribution strategies could not be transferred to the wheels in the most efficient manner. This paper investigates this aspect of the energy flow.

This paper presents an integrated chassis control method for vehicle stability under various road friction conditions without information on tire-road friction. For vehicle stability, vehicle with an integrated chassis control needs to cope with the various road friction conditions. One of the chassis control method under various road conditions is to determine and/or limit control inputs based on tire-road friction coefficient. The tire-road friction coefficient, however, is difficult to estimate and still a challenging task. The key idea for the proposed method without the estimation of the tire-road friction coefficient is to analyze and control vehicle states based on a tire slip angle - tire force phase plane, i.e. based on these vehicle responses: tire forces and tire slip angles of front/rear wheels.

This paper is concerned with the torque distribution problem including slip limitation and actuator fault tolerance to improve vehicle lateral stability and maneuverability of six-wheeled skid-steered vehicles. The torque distribution algorithm to distribute wheel torque to each wheel of a skid-steered vehicle consists of an upper level control layer, a lower level control layer and an estimation layer. The upper level control layer is designed to obtain longitudinal net force and desired yaw moment, while the lower level control layer determines distributed driving and braking torques to six wheels. The algorithm takes vehicle speed, slip ratio and tire load information from the estimation layer, as well as actuator fault information from each in-wheel motor controller unit.