Battery Lifetime Prognostics

被引:803
作者
Hu, Xiaosong [1 ]
Xu, Le [1 ]
Lin, Xianke [2 ]
Pecht, Michael [3 ]
机构
[1] Chongqing Univ, Dept Automot Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[2] Univ Ontario Inst Technol, Dept Automot Mech & Mfg Engn, Oshawa, ON L1G 0C5, Canada
[3] Univ Maryland, Ctr Adv Life Cycle Engn, College Pk, MD 20742 USA
基金
加拿大自然科学与工程研究理事会; 中国国家自然科学基金;
关键词
REMAINING USEFUL LIFE; LITHIUM-ION BATTERIES; SOLID-ELECTROLYTE INTERPHASE; PARTICLE SWARM OPTIMIZATION; GAUSSIAN PROCESS REGRESSION; SYSTEM STATE ESTIMATION; CYCLE-LIFE; CAPACITY FADE; 2ND LIFE; HEALTH ESTIMATION;
D O I
10.1016/j.joule.2019.11.018
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Lithium-ion batteries have been widely used in many important applications. However, there are still many challenges facing lithium-ion batteries, one of them being degradation. Battery degradation is a complex problem, which involves many electrochemical side reactions in anode, electrolyte, and cathode. Operating conditions affect degradation significantly and therefore the battery lifetime. It is of extreme importance to achieve accurate predictions of the remaining battery lifetime under various operating conditions. This is essential for the battery management system to ensure reliable operation and timely maintenance and is also critical for battery second-life applications. After introducing the degradation mechanisms, this paper provides a timely and comprehensive review of the battery lifetime prognostic technologies with a focus on recent advances in model-based, data-driven, and hybrid approaches. The details, advantages, and limitations of these approaches are presented, analyzed, and compared. Future trends are presented, and key challenges and opportunities are discussed.
引用
收藏
页码:310 / 346
页数:37
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