赵显赫,耿光超,林达,李志浩,张杨
ZHAO Xianhe,GENG Guangchao,LIN Da,LI Zhihao,ZHANG Yang

• 1:浙江大学电气工程学院
• 2:国网浙江省电力有限公司电力科学研究院
• 3:国网浙江省电力有限公司

摘要(Abstract)：

随着锂离子电池在各类储能系统中的广泛应用,其健康管理及退化分析已成为储能电站运维、电动汽车安全监测、退役动力电池梯次利用等时下多个领域的热点问题。与此同时,大数据及机器学习技术的发展突破了复杂非线性系统难以建模的束缚,使得基于数据驱动的电池健康评估成为可能。详细综述了基于数据驱动的锂离子电池健康状态评估的研究现状,分析了电池退化的影响因素,归纳并比较了基于数据驱动的电池健康状态估计及剩余寿命预测建模方法,最后总结了该领域当前的挑战及未来的发展趋势。
As lithium-ion batteries are widely applied in various energy storage systems, health management and degradation analysis now have become hot issues in many fields including operation and maintenance of energy storage power stations, the safety monitoring for electric vehicles and cascade utilization of decommissioned power lithium battery. At the same time, the development of big data and machine learning techniques have broken the constraints of the difficulty in modeling complex nonlinear systems, making it possible to estimate battery health based on data-driven methods. This paper provides a detailed overview of the current research status of data-driven lithium-ion battery health estimation, analyzes the influencing factors of battery degradation, summarizes and compares modeling methods of battery health state estimation and residual life prediction. Finally, it summarizes the current challenges and development trend in this field.

关键词(KeyWords)： 锂离子电池;退化;健康状态评估;剩余寿命预测;数据驱动
lithium-ion battery;degradation;state of health estimation;residual life prediction;data-driven

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211DS180037）

作者(Author): 赵显赫,耿光超,林达,李志浩,张杨
ZHAO Xianhe,GENG Guangchao,LIN Da,LI Zhihao,ZHANG Yang

DOI: 10.19585/j.zjdl.202107011

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