Lithium-ion battery aging mechanisms and diagnosis method for automotive applications Recent advances and perspectives
发表时间:2021-01-20     阅读次数:     字体:【

摘要

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).


部分图片:

图1 Framework for systematic review of battery aging mechanism and diagnosis.

图2 Main procedure for applying EIS to diagnose battery internal reaction mechanisms.

引文信息

Xiong R, Pan Y, Shen W, et al. Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives[J]. Renewable and Sustainable Energy Reviews, 2020, 131: 110048. (下载链接)

其他相关论文

1. Zhang Y , Xiong R , He H , et al. Lithium-ion battery remaining useful life prediction with Box-Cox transformation and Monte Carlo simulation[J]. IEEE Transactions on Industrial Electronics, 2018:1-1.(下载链接

2. Rui, Xiong, et al. Towards a smarter battery management system: A critical review on battery state of health monitoring methods[J]. Journal of Power Sources, 2018. (下载链接)




上一篇:Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles
下一篇:A review on state of health estimation for lithium ion batteries in photovoltaic systems
0
联系地址:北京市海淀区中关村南大街5号北京理工大学   Copyright  ©  2020-   先进储能科学与应用联合实验室  All Rights Reserved.网站地图
友情链接: 新能源与智能载运期刊    北京理工大学    机械与车辆学院