A review of the state of health for lithium -ion batteries: Research status and suggestions

被引:339
|
作者
Tian, Huixin [1 ,2 ]
Qin, Pengliang [1 ,2 ]
Li, Kun [3 ]
Zhao, Zhen [4 ]
机构
[1] Tiangong Univ, Sch Elect Engn & Automat, Tianjin 300387, Peoples R China
[2] Tiangong Univ, Key Lab Adv Elect Engn & Energy Technol, Tianjin 300387, Peoples R China
[3] Tiangong Univ, Sch Econ & Management, Tianjin 300387, Peoples R China
[4] Civil Aviat Univ China, Sch Elect Informat & Automat, Tianjin 300300, Peoples R China
基金
中国国家自然科学基金;
关键词
Battery electric vehicles; Lithium-ion battery; State of health; Battery aging; Prediction method; REMAINING USEFUL LIFE; INCREMENTAL CAPACITY; DIFFERENTIAL VOLTAGE; ONLINE ESTIMATION; AGING PARAMETERS; PARTICLE FILTER; NEURAL-NETWORK; PROGNOSTICS; PREDICTION; MODEL;
D O I
10.1016/j.jclepro.2020.120813
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
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. (c) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:30
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