Title :
LiFePO4 battery pack capacity estimation for electric vehicles based on unscented Kalman filter
Author :
Lei Zhao ; Guoqing Xu ; Weimin Li ; Taimoor, Zahid ; Zhibin Song
Author_Institution :
Shenzhen Inst. of Adv. Technol., Shenzhen, China
Abstract :
As is known to all, an accurate on-line estimation of the battery capacity is important for forecasting the EV driving range. But because of the different driving environment and the property of the battery, it is hard to estimate the capacity of the battery pack. This paper presents an unscented Kalman filtering method to estimate the state of charge of LiFePO4 battery pack. Five comparison experiments with different open circuit voltage curves shows that the unscented Kalman filter has a better performance than extended kalman filter.
Keywords :
Kalman filters; battery powered vehicles; iron compounds; lithium compounds; phosphorus compounds; secondary cells; EV driving range; LiFePO4; battery pack capacity estimation; electric vehicles; unscented Kalman filter; Batteries; Computational modeling; Estimation; Integrated circuit modeling; Kalman filters; Noise; System-on-chip; Thevenin model; battery management system(BMS); extended kalman filter (EKF); state of charge(SOC); unscented Kalman filter(UKF);
Conference_Titel :
Information and Automation (ICIA), 2013 IEEE International Conference on
Conference_Location :
Yinchuan
DOI :
10.1109/ICInfA.2013.6720314