电源技术2024，Vol.48Issue(8)：1494-1502,9.DOI:10.3969/j.issn.1002-087X.2024.08.010
电化学阻抗谱机器学习评估动力电池状态研究进展
Research progress of evaluating the status of power lithium batteries based on electrochemical impedance spectroscopy combined with machine learning
摘要
Abstract
The cascading utilization of retired power lithium batteries (with a rated capacity of over 80％) can effectively alleviate the pressure of battery recycling and environmental pollution,and im-prove resource utilization efficiency and economic benefits. However,conducting rapid,non-destructive,and accurate state assessment of the retired batteries remains a challenge. Compared with other reported methods,electrochemical alternating current measurement of batteries and col-lecting data to draw impedance spectra are the core methods for studying battery states,which have two advantages:fast and non-destructive. The battery detected in this way can establish internal im-pedance and state correlation,and quickly complete battery state evaluation. The analysis methods of electrochemical impedance spectroscopy mainly include predicting impedance based on measurement data and machine learning methods,analyzing the changes in various equivalent components of the circuit based on equivalent circuit diagrams,and using integration algorithms to convert impedance spectroscopy into a more intuitive relaxation time distribution spectroscopy. These methods all pro-vide analytical methods for the internal aging of batteries,providing an electrochemical basis for the relationship between the internal impedance and health status of batteries. Based on this,this article reviewed the latest research progress in combining electrochemical impedance spectroscopy with ma-chine learning to evaluate the state of power lithium batteries both domestically and internationally,with a focus on summarizing and exploring the relationship between electrochemical impedance spec-troscopy,equivalent circuit models,relaxation time distribution,and machine learning.关键词
电池状态/电化学阻抗谱图/机器学习/等效电路模型/弛豫时间分布Key words
battery state/electrochemical impedance spectroscopy/machine learning/equivalent circuit model/distribution of relaxation time分类
信息技术与安全科学引用本文复制引用
姜岱延,金玉红,张子恒,刘晶冰,张媛,李思全,汪浩..电化学阻抗谱机器学习评估动力电池状态研究进展[J].电源技术,2024,48(8):1494-1502,9.基金项目
国家电网有限公司总部管理科技项目(5108-202218280A-2-314-XG) （5108-202218280A-2-314-XG）
