Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives

• Published in: Renewable & sustainable energy reviews 2020-10, Vol.131, p.110048, Article 110048
• Main Authors: Xiong, Rui, Pan, Yue, Shen, Weixiang, Li, Hailong, Sun, Fengchun
• Format: Article
• Language: English
• Subjects: Accelerated aging tests; Aging mechanism; Automotive applications; Automotive batteries; Battery management systems; Battery management systems (BMSs); Diagnosis; Electrochemical reactions; Electrodes; Induced degradation; Intelligent battery management systems; Ions; Lithium-ion batteries; Lithium-ion battery; Model-based analysis; onboard diagnosis; Online systems; Post mortem analysis; Quantitative diagnosis
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2020.110048

Lithium-ion batteries decay every time as it is used. Aging-induced degradation is unlikely to be eliminated. The aging mechanisms of lithium-ion batteries are manifold and complicated which are strongly linked to many interactive factors, such as battery types, electrochemical reaction stages, and operating conditions. In this paper, we systematically summarize mechanisms and diagnosis of lithium-ion battery aging. Regarding the aging mechanism, effects of different internal side reactions on lithium-ion battery degradation are discussed based on the anode, cathode, and other battery structures. The influence of different external factors on the aging mechanism is explained, in which temperature can exert the greatest impact compared to other external factors. As for aging diagnosis, three widely-used methods are discussed: disassembly-based post-mortem analysis, curve-based analysis, and model-based analysis. Generally, the post-mortem analysis is employed for cross-validation while the curve-based analysis and the model-based analysis provide quantitative analysis. The challenges in the use of quantitative diagnosis and on-board diagnosis on battery aging are also discussed, based on which insights are provided for developing online battery aging diagnosis and battery health management in the next generation of intelligent battery management systems (BMSs). •Basic aging reactions inside battery during storage and cycling were described.•Detailed classification and comparison of aging diagnosis methods were presented.•Progress and challenges of aging diagnosis in quantitative analysis and on-board applications were provided.•Evolution of dominant aging mechanism under different external factors was discussed.