Estimation of Tire Stiffness Variation based on Adaptive Extended Kalman Filter of Suspension Systems and Its Application to Indirect Tire Pressure Monitoring System

This paper presents a new estimation method of tire stiffness based in improved Kalman filter of vehicle suspension control system. In recent years, the need for systems monitoring the current pressure in pneumatic tires has grown dramatically. Incorrect pressured tire will affect the handling performance, tire life time and fuel economy. For these reasons, tire pressure monitoring system(TPMS) is required to ensure the vehicle safety and ride quality. However, traditional TPMS requires a battery in each tire in order to power the sensor and circuits inside the tire and it has temperature dependent capacity problem. To overcome this problem, indirect methods are proposed. One of the promising indirect methods is the sensor fusion method from automotive control systems. In this study, adaptive extended Kalman filter(AEKF) approach is proposed to identify structural parameter, such as tire stiffness. Simulation results demonstrate that proposed approach is capable of estimating tire pressure based on experiment of relation between tire pressure and tire stiffness.