DocumentCode
3004929
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
fYear
2013
fDate
26-28 Aug. 2013
Firstpage
301
Lastpage
305
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);
fLanguage
English
Publisher
ieee
Conference_Titel
Information and Automation (ICIA), 2013 IEEE International Conference on
Conference_Location
Yinchuan
Type
conf
DOI
10.1109/ICInfA.2013.6720314
Filename
6720314
Link To Document