A review on prognostics approaches for remaining useful life of lithium-ion battery

被引:34
|
作者
Su, C. [1 ]
Chen, H. J. [1 ]
机构
[1] Southeast Univ, Sch Mech Engn, Nanjing 211189, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
UNSCENTED KALMAN FILTER; CHARGE ESTIMATION; HEALTH ESTIMATION; NEURAL-NETWORK; WIENER-PROCESS; STATE; MODEL; PERFORMANCE; PREDICTION; ALGORITHM;
D O I
10.1088/1755-1315/93/1/012040
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Lithium-ion (Li-ion) battery is a core component for various industrial systems, including satellite, spacecraft and electric vehicle, etc. The mechanism of performance degradation and remaining useful life (RUL) estimation correlate closely to the operating state and reliability of the aforementioned systems. Furthermore, RUL prediction of Li-ion battery is crucial for the operation scheduling, spare parts management and maintenance decision for such kinds of systems. In recent years, performance degradation prognostics and RUL estimation approaches have become a focus of the research concerning with Li-ion battery. This paper summarizes the approaches used in Li-ion battery RUL estimation. Three categories are classified accordingly, i.e. model-based approach, data-based approach and hybrid approach. The key issues and future trends for battery RUL estimation are also discussed.
引用
收藏
页数:8
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