Sequential Monte Carlo based Technique for SOC Estimation of LiFePO4 Battery Pack for Electric Vehicles

被引：0
|
作者：
Zahid, Taimoor [1 ,2 ,3 ]
Qin, Feiyan [1 ,3 ]
Li, Weimin [1 ,2 ,4 ,5 ]
Xu, Kun [1 ,2 ,4 ]
Zhou, Yimin [1 ,4 ,5 ]
机构：
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
[2] Chinese Acad Sci, Jining Inst Adv Technol, Jilting, Peoples R China
[3] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen, Peoples R China
[4] Shenzhen Key Lab Elect Vehicle Powertrain Platfor, Shenzhen, Peoples R China
[5] Chinese Univ Hong Kong, Hong Kong, Hong Kong, Peoples R China
来源：
2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) | 2016年
基金：
中国国家自然科学基金;
关键词：
state of charge(SOC); open circuit voltage(OCV); Particle filter(PF); Lithium battery; STATE-OF-CHARGE;
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：
Battery energy storage management for electric vehicles and hybrid electric vehicles is the most critical enabling technology. A battery system is a complex electrochemical phenomenon whose performance degrades with ageing and the existence of varying material design. Moreover, it is very tedious and computationally very complex for monitoring and controlling the internal state of battery's electrochemical systems. This paper presents a particle filtering method for state of charge estimation of LiFePO4 battery. Using different open circuit voltages the state of charge is estimated with respect to the estimation accuracy and convergence. It was demonstrated that particle filter can be utilized to estimate the SOC for Li ion battery.
引用
收藏
页码：1308 / 1312
页数：5
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