锂离子电池剩余寿命预测研究综述

被引:69
作者
林娅
陈则王
机构
[1] 南京航空航天大学自动化学院
关键词
锂离子电池; 循环寿命; 剩余使用寿命预测; 预测方法;
D O I
10.19651/j.cnki.emt.1701091
中图分类号
TM912 [蓄电池];
学科分类号
080802 [电力系统及其自动化];
摘要
电池剩余寿命预测技术是电池预测与健康管理技术的核心内容之一,也是电池管理系统中公认的研究热点和难点。准确的电池剩余寿命估计不仅能让用户及时获取电池寿命信息,更换即将失效的电池,保障电池组安全高效运行,还能在很大程度上确保以锂离子电池作为主要供能、储能原件的设备在其运行中的安全性和可靠性,避免事故的发生,降低运行成本。重点阐述了国内外锂离子电池剩余寿命预测的方法和研究现状、影响电池使用寿命及其预测精度的主要因素等,归纳和比较了各类预测方法的优势和局限性,总结了目前的技术研究难点,给出了电池寿命预测技术研究亟待解决的问题及发展趋势展望。
引用
收藏
页码:29 / 35
页数:7
相关论文
共 33 条
[1]
矿用锂离子电池状态监测与寿命预测研究 [D]. 
姜玉叶 .
中国矿业大学,
2015
[2]
A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations.[J].M.A. Hannan;M.S.H. Lipu;A. Hussain;A. Mohamed.Renewable and Sustainable Energy Reviews.2017,
[3]
Failure statistics for commercial lithium ion batteries: A study of 24 pouch cells.[J].Stephen J. Harris;David J. Harris;Chen Li.Journal of Power Sources.2017,
[4]
An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction.[J].Xiujuan Zheng;Huajing Fang.Reliability Engineering and System Safety.2015,
[5]
Remaining useful life estimation using an inverse Gaussian degradation model.[J].Donghui Pan;Jia-Bao Liu;Jinde Cao.Neurocomputing.2016,
[6]
An SOC estimation approach based on adaptive sliding mode observer and fractional order equivalent circuit model for lithium-ion batteries [J].
Zhong, Fuli ;
Li, Hui ;
Zhong, Shouming ;
Zhong, Qishui ;
Yin, Chun .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2015, 24 (1-3) :127-144
[7]
Lithium-ion battery remaining useful life estimation with an optimized Relevance Vector Machine algorithm with incremental learning.[J].Datong Liu;Jianbao Zhou;Dawei Pan;Yu Peng;Xiyuan Peng.Measurement.2015,
[8]
Determination of lithium-ion battery state-of-health based on constant-voltage charge phase.[J].Akram Eddahech;Olivier Briat;Jean-Michel Vinassa.Journal of Power Sources.2014,
[9]
Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter.[J].Hancheng Dong;Xiaoning Jin;Yangbing Lou;Changhong Wang.Journal of Power Sources.2014,
[10]
On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models.[J].Jürgen Remmlinger;Michael Buchholz;Thomas Soczka-Guth;Klaus Dietmayer.Journal of Power Sources.2013,