Remaining Useful Life Prediction of LiFePO4 Battery Based on Particle Filter

被引：0
|
作者：
Geng, Fei [1 ]
Kang, Yong-zhe [1 ]
Li, Ze-yuan [1 ]
Zhang, Cheng-hui [1 ]
Duan, Bin [1 ]
机构：
[1] Shandong Univ, Sch Control Sci & Engn, Jinan, Shandong, Peoples R China
来源：
2017 CHINESE AUTOMATION CONGRESS (CAC) | 2017年
基金：
中国国家自然科学基金;
关键词：
LiFePO4; battery; remaining useful life; particle filter; Systematic Resampling; CAPACITY FADE; PROGNOSTICS; MECHANISM;
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：
LiFePO4 battery has been widely used in electric vehicles due to high safety and long cycle life. This paper firstly analyses the basic characteristics of LiFePO4 battery including the capacity and internal resistance. Secondly, particle filter (PF) algorithm is introduced to predict the remaining useful life (RUL) of LiFePO4 battery effectively. Based on the LiFePO4 battery life degradation data, the prediction accuracy of four kinds of resampling algorithm is analyzed and compared. The result of Systematic Resampling is the most close to real life end points, and Random Resampling has the lowest prediction accuracy. Therefore, Systematic Resampling is used to predict RUL. The results indicate that PF can efficiently predict RUL of LiFePO4 Battery.
引用
收藏
页码：1149 / 1153
页数：5
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