LiFePO4 Battery Pack Capacity Estimation for Electric Vehicles Based on Unscented Kalman Filter

被引：0
|
作者：
Zhao, Lei [1 ]
Xu, Guoqing [1 ]
Li, Weimin [1 ]
Taimoor, Zahid [1 ]
Song, Zhibin [1 ]
机构：
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
来源：
2013 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2013年
关键词：
unscented Kalman filter(UKF); extended kalman filter (EKF); Thevenin model; state of charge(SOC); battery management system(BMS); OF-CHARGE ESTIMATION; LITHIUM-ION BATTERY; MANAGEMENT-SYSTEMS; STATE;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：301 / 305
页数：5
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