Title :
Estimation of state of charge of battery based on Extended Kalman Filtering
Author :
Chen Huangjie ; Ma Yan ; Zhao Haiyan
Author_Institution :
Dept. of Control Sci. & Eng., Jilin Univ., Changchun, China
Abstract :
Estimation of state of charge (SOC) is the key technique for electric vehicle power management system. A simple battery nonlinear equivalent model under AMESim is built. By using Extended Kalman Filter method, battery SOC is estimated on-line through optimal estimation in the sense of minimum-variance when battery current fluctuates wildly. Co-simulation result shows that Extended Kalman Filter method which can estimate SOC with error less than 1.8% has high accuracy.
Keywords :
Kalman filters; electric vehicles; estimation theory; system-on-chip; battery SOC; battery nonlinear equivalent model; electric vehicle power management system; extended Kalman filter method; extended Kalman filtering; minimum-variance; state of charge of battery; Batteries; Electronic mail; Estimation; Kalman filters; Software packages; System-on-chip; AMESim; Electric Vehicle; Extended Kalman Filtering; State of Charge (SOC);
Conference_Titel :
Control Conference (CCC), 2013 32nd Chinese
Conference_Location :
Xi´an