可靠性(半导体)
状态监测
断层(地质)
状态维修
预测性维护
故障检测与隔离
作者
Ming-Feng Ge,Yiben Liu,Xingxing Jiang,Jie Liu
出处
期刊:Measurement
[Elsevier]
日期:2021-04-01
卷期号:174: 109057-
被引量:19
标识
DOI:10.1016/j.measurement.2021.109057
摘要
Abstract Lithium-ion batteries have been generally used in industrial applications. In order to ensure the safety of the power system and reduce the operation cost, it is particularly important to accurately and timely estimate the state of health (SOH) and predict the remaining useful life (RUL) of lithium-ion batteries. With the development of intelligent tools such as artificial intelligence, big data analysis and the Internet of Things, the methods of battery health assessment have been gradually diversified. Here, we have compiled four publicly available battery datasets. The SOH estimations and RUL prognostics of lithium-ion batteries are reviewed by analyzing the research status. To this end, after studying different scientific and technical literatures, the respective methods are divided into specific groups, and the advantages and limitations of the battery management system application are discussed. At the end, the future development trend and research challenges are analyzed. All key insights in this review will hopefully drive the development of battery health estimation and life prediction techniques.
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