健康状况
电池(电)
锂离子电池
主流
计算机科学
钥匙(锁)
锂(药物)
可靠性工程
功率(物理)
风险分析(工程)
工程类
汽车工程
业务
医学
哲学
内分泌学
物理
量子力学
计算机安全
神学
作者
Huixin Tian,Pengliang Qin,Kun Li,Zhen Zhao
标识
DOI:10.1016/j.jclepro.2020.120813
摘要
Lithium-ion batteries (LIBs) have become the mainstream power source for battery electric vehicles (BEVs) with relatively superior performance. However, LIBs experience battery aging and performance degradation due to the external environment and internal factors, which should be reflected in the evaluation of the state of health (SOH). Accurately predicting SOH can improve the overall life of the battery and support safe driving in BEVs. At present, while there are many prediction methods for SOH, most are implemented in simulated environments but are challenging to execute in actual industrial production. This review provides a discussion on the aging reasons for LIBs, introduces the SOH prediction method based on the classification framework, and analyzes the key benefits and drawbacks of each method. Finally, the corresponding suggestions and solutions are given in combination with the actual industrial production.
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