A modified Sage-Husa adaptive Kalman filter for state estimation of electric vehicle servo control system
Xingyu Wang, Anna Wang, Dazhi Wang, Yingjie Xiong, Bingxue Liang, Yufei Qi
Abstract
Kalman filter algorithm is widely used in state estimation of electric vehicles (EV) servo system as dynamic estimation of power system. In order to improve the estimation effect of noise interference in engineering practice, a scheme of Modified Sage-Husa adaptive Kalman filter (MSHAKF) combined with fuzzy clustering algorithm is proposed. The scheme processes the experimental data in a robust and adaptive way through the combination of constantly changing fuzzy clustering and MSHAKF, so as to better represent the dynamic characteristics of EV. The simulation results show that this method has higher estimation accuracy than the traditional method on the premise of unknown disturbance and noise characteristics in the system, and is more in line with the practical application of EV.