Advances in battery state estimation of battery management system in electric vehicles

• Published in: Journal of power sources Vol. 612; p. 234781
• Main Authors: Jiang, Ming, Li, Dongjiang, Li, Zonghua, Chen, Zhuo, Yan, Qinshan, Lin, Fu, Yu, Cheng, Jiang, Bo, Wei, Xuezhe, Yan, Wensheng, Yang, Yong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30-08-2024
• Subjects: Battery management system; Battery state estimation; Electric vehicles; State of charge; State of health; State of safety; Battery state estimation; Battery management system; Electric vehicles; State of safety; State of charge; State of health
• ISSN: 0378-7753; 1873-2755
• DOI: 10.1016/j.jpowsour.2024.234781

Lithium-ion batteries (LIBs) have emerged as an indispensable component in the development of green transportation such as electric vehicles (EVs) and large-scale applications of renewable energy such as smart grid energy storage systems. The detection, judgment, and prediction of various battery states such as State of Charge (SOC) and State of Health (SOH) in the battery management system (BMS) play a critical role in guaranteeing the LIBs work under a safe and reliable situation. After decades of intensive investigation, accompanied by the fast development of big-data techniques (BDT) and artificial intelligence (AI) algorithms, the framework of BMS is moving from the traditional onboard system towards the functional integrated scheme. This paper starts with a comprehensive overview of the underlying degradation mechanism of the battery and algorithm distinction and judgment of the battery states in BMS. Subsequently, the paper has systematically reviewed and discussed the most commonly used approaches and state-of-the-art algorithms for battery state estimation in BMS from the perspective of three different BMS configurations: onboard-BMS, cloud-BMS, and functional integrated-BMS. This review expects to stimulate more new insights and encourage more efforts to develop advanced BMS for intelligent and innovative battery control.
•Various battery degradation phenomena and their model development are explained.•Three distinct BMS structures—onboard-BMS, cloud-BMS, and Fi-BMS—are explained.•The latest advancements in battery state estimation algorithms are reviewed.•Emerging technical innovation prospects are highlighted in four areas.