基于数据驱动的锂离子电池健康状态估计研究进展综述  

Review on progress of data-driven based health state estimation for lithium-ion batteries

作　　者：金帅 董静[1] Jin Shuai;Dong Jing(School of Light Industry,Harbin University of Commerce,Harbin 150028,China)

机构地区：[1]哈尔滨商业大学轻工学院,哈尔滨150028

出　　处：《仪器仪表学报》2024年第3期45-59,共15页Chinese Journal of Scientific Instrument

基　　金：黑龙江省省属高等学校基本科研项目(2023-KYYWF-1013);哈尔滨商业大学研究生科研创新项目(YJSCX2023-778HSD)资助。

摘　　要：锂离子电池(LIBs)在电气化交通、电化学储能和移动电子产品等领域广泛使用,精准评估其健康状态(SOH)是确保安全可靠应用的基础。数据驱动法是当前评估SOH的主流方法,该方法无需考虑电池内部复杂的物理化学反应,依赖于直接的数据分析,且具有较高的精度。本文从锂离子电池SOH影响因素入手分析了基于数据驱动的电池SOH估计方法的研究现状,着重比较了机器学习、滤波器和时间序列等方法实施SOH估计的原理、优缺点。最后,针对电动汽车实际应用场景,对SOH估计方法的未来发展趋势进行了展望。Lithium-ion batteries(LIBs)are widely used in areas such as electrified transportation,electrochemical energy storage and mobile electronics.Consequently,accurate assessment of their state of health(SOH)is fundamental to ensure safe and reliable applications.Data-driven methods are the mainstream methods to evaluate SOH,which do not need to consider the complex physical and chemical reactions inside the battery,and only rely on direct data analysis to achieve accurate SOH estimation.This paper analyzes the current research progress of data-driven estimation methods for battery SOH under the consideration of the influencing factors of SOH for LIBs,and focuses on comparing the principles,advantages and disadvantages of machine learning,filter and time series methods in implementing SOH estimation.Finally,according to the practical application scenarios of electric vehicles,the future development trend of SOH estimation methods is prospected.

关 键 词：锂离子电池 健康状态 数据驱动 电动汽车 

分 类 号：TH89[机械工程—仪器科学与技术] TM93[机械工程—精密仪器及机械]