Real-Time Estimation of Inertial Parameter for Lightweight Electric Vehicle Using Dual Kalman Filter

Real-time and accurate knowledge of important vehicle states and inertia parameters in vehicle dynamics control systems is very essential for improving vehicle handling stability and active safety, especially for lightweight electric vehicle where effects of variation in inertial parameters for vehicle stability performance become much more pronounced owing to the drastic change in vehicle weights and body geometry size. This paper presents a parallel dual extended Kalman filter to estimate vehicle states and inertia parameters including the mass of car body and moment of inertia by utilizing real-time measurements in sensor-wheel motor and lateral acceleration. Simulation with sinusoidal steering-like manoeuvre is carried out. Simulation results that the proposed estimation method can effectively estimate the vehicle states and inertia parameters.