机器学习强化的电化学阻抗谱技术及其在锂离子电池研究中的应用  

Machine learning-enhanced electrochemical impedance spectroscopy for lithium-ion battery research

作　　者：何智峰 陶远哲 胡泳钢 王其聪[4] 杨勇 HE Zhifeng;TAO Yuanzhe;HU Yonggang;Wang Qicong;YANG Yong(State Key Laboratory of Physical Chemistry of Solid Surfaces,Department of Chemistry,College of Chemistry and Chemical Engineering,Xiamen University;Collaborative Innovation Center of Chemistry for Energy Materials(iChEM),Department of Chemistry,College of Chemistry and Chemical Engineering,Xiamen University;College of Energy,Xiamen University;Department of Computer Science,School of Informatics,Xiamen University,Xiamen 361005,Fujian,China)

机构地区：[1]厦门大学固体表面物理化学国家重点实验室,化学与化工学院化学系 [2]厦门大学能源材料化学协同创新中心(iChEM),化学与化工学院化学系 [3]厦门大学能源学院 [4]厦门大学信息学院计算机系,福建厦门361005

出　　处：《储能科学与技术》2024年第9期2933-2951,共19页Energy Storage Science and Technology

基　　金：国家重点研发计划专项(2021YFB2401800)。

摘　　要：随着电气化的发展,全球动力电池和储能电池的需求迅猛增加。然而,人们对电池使用安全性和可靠性的关注使得电池老化状态的精准诊断和预测成为电池界重要且富有挑战的研究领域之一。电化学阻抗谱(EIS)因其可以解耦电池内部不同频域过程常被用于电池复杂老化过程状态的解析,而通过机器学习方法不仅可以高效获取和解析EIS数据,而且可促进对电池老化和失效机制的深入理解。本文综述了近年来机器学习方法在EIS技术中的应用,重点讨论了通过机器学习获取和解析EIS数据,进而实现对电池寿命评估预测。此外,本文讨论了数据融合方法在实现电池老化行为分析和寿命预测中的前景,当前机器学习在EIS研究中存在的局限性,以及对未来基于EIS实现电池寿命预测进行了展望。The rapid proliferation of electrification has driven a global surge in the demand for power and energy storage batteries.This rise has intensified concerns regarding battery safety and reliability,emphasizing the need for accurate methods for diagnosing and predicting battery aging,making this a notable area of research in the battery domain.Electrochemical impedance spectroscopy(EIS)is widely used to analyze the complex aging processes of batteries because it can effectively decouple various frequency-domain processes.The integration of machine learning methods not only facilitates the acquisition and analysis of EIS data but also offers deeper insights into battery aging and failure mechanisms.This paper reviews the latest applications of machine learning methods in EIS technique,focusing on machine learning-based acquisition and analysis of EIS data for battery life assessment and prediction.In addition,this paper explores the potential of data fusion methods for analyzing the aging behavior of batteries and predicting their lifespan,discusses the current limitations of applying machine learning to EIS research,and describes the future prospects of EIS-based battery life prediction.

关 键 词：锂离子电池 电化学阻抗谱 机器学习 寿命预测 数据驱动 

分 类 号：TM911[电气工程—电力电子与电力传动]