A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries

• Published in: Measurement : journal of the International Measurement Confederation Vol. 174; p. 109057
• Main Authors: Ge, Ming-Feng, Liu, Yiben, Jiang, Xingxing, Liu, Jie
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2021
• Subjects: Battery management system; Energy storage; Lithium-ion battery; Remaining useful life prediction; State of health estimation; Sustainable energy; State of health estimation; Lithium-ion battery; Remaining useful life prediction; Battery management system; Sustainable energy; Energy storage
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2021.109057

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.