Performance Analysis of Particle Filter for SOC Estimation of LiFePO4 Battery Pack for Electric Vehicles

被引：0
|
作者：
Zahid, Taimoor [1 ,2 ,3 ]
Xu, Guoqing [1 ,4 ]
Li, Weimin [1 ,3 ,4 ]
Zhao, Lei [1 ,4 ]
Xu, Kun [1 ,4 ]
机构：
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
[2] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen, Peoples R China
[3] Chinese Acad Sci, Jining Inst Adv Technol, Jining, Peoples R China
[4] Shenzhen Key Lab Elect Vehicle Powertrain Platfor, Shenzhen, Peoples R China
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2014年
关键词：
battery management system (BMS); state of charge(SOC); particle filter (PT); extended kalman filter(EKF); Thevenin equivalent circuit model; open circuit voltage (OCV); 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：
For the development of an innovative battery management system an accurate and a reliable technique for state of charge estimation plays a significant role due to the arduous operation environments land for ensuring the safety and reliability of battery operations for a hybrid/electric vehicle. This paper illustrates an online SOC estimation method of a LiFePo4 battery for applications in electric vehicles by using a particle filter. Additionally, a five comparison experiments with different open circuit voltage curves exhibits that the particle filter is a promising alternative, even if it is computationally more demanding then extended kalman filter.
引用
收藏
页码：1061 / 1065
页数：5
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