摘要
近年来,随着锂离子电池的能量密度、功率密度逐渐提升,其安全性能与剩余使用寿命预测变得愈发重要。本综述全面分析了锂电池剩余使用寿命预测领域研究现状,系统介绍了现有预测算法,并着重探讨了机器学习方法在该领域的应用。基于模型的方法包括电化学模型、等效电路模型和经验退化模型;基于数据驱动的方法涵盖了支持向量回归、高斯过程回归、极限学习机、卷积神经网络、循环神经网络和Transformer等常用的机器学习方法。本文详细分析了每种方法的优缺点,并重点阐述了机器学习方法在特征提取与融合方法等方面的应用及发展情况。对于特征提取,本文从电流电压温度曲线、IC曲线、EIS曲线中进行总结分析;对于融合方法,本文将其细分为模型-模型、数据-模型、数据-数据融合方法并进行分析。最后,针对当前研究存在的问题,本综述从早期预测、在线预测和多工况预测3个方面提出了对剩余使用寿命预测方法的研究建议,为提升锂电池剩余使用寿命预测算法的准确性和实用性提供思路。
As the energy and power density of lithium-ion batteries have gradually increased in recent years,the safety performance and prediction of remaining service life have become increasingly crucial.This review offers a comprehensive analysis of the current research status of predicting the remaining useful life of lithium batteries.It systematically introduces the existing forecast algorithms,focusing on the application of machine learning methods in this field.Model-based methods encompass electrochemical,equivalent circuits,and empirical models.In contrast,data-driven methods involve machine learning techniques such as support vector machines,Gaussian process regression,extreme learning machines,convolutional neural networks,recurrent neural networks,and transformers.We meticulously examine the advantages and disadvantages of each method,emphasizing on the application and evolution of machine learning methods in feature extraction and fusion techniques.This study summarizes and analyzes current-voltage-temperature,IC,and EIS curves regarding feature extraction.It subdivides and analyzes fusion methods into model-model,data-model,and data-data fusion methods.Finally,addressing the existing research challenges,this review proposes research suggestions for predicting remaining service life from three perspectives:early,online,and multioperating condition predictions.These suggestions provide insights into enhancing the accuracy and practicability of remaining service life prediction algorithms for Liion batteries.
作者
李炳金
韩晓霞
张文杰
曾伟国
武晋德
LI Bingjin;HAN Xiaoxia;ZHANG Wenjie;ZENG Weiguo;WU Jinde(Taiyuan University of Technology,Taiyuan 030000,Shanxi,China;Suzhou Chuhui Technology Co.,Ltd,Suzhou 215101,Jiangsu,China)
出处
《储能科学与技术》
CAS
CSCD
北大核心
2024年第4期1266-1276,共11页
Energy Storage Science and Technology
基金
国家自然科学基金(52307247)
山西省基础研究计划(202203021222124)。
关键词
锂离子电池
剩余使用寿命
数据驱动
机器学习
lithium-ion batteries
remaining useful life
data-driven
machine learning
