同济大学学报（自然科学版）2024，Vol.52Issue(z1)：223-234,12.DOI:10.11908/j.issn.0253-374x.24769
基于电化学阻抗谱及弛豫时间分布的锂电池异常识别与诊断
Identification and Diagnosis of Abnormal Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy and Distribution of Relaxation Time Analysis
袁永军 1郭玄 2王学远 3姜波 3戴海峰 3魏学哲3
作者信息
- 1. 同济大学 汽车学院,上海 201804||上海炙云新能源科技有限公司,上海 201823
- 2. 上海炙云新能源科技有限公司,上海 201823
- 3. 同济大学 汽车学院,上海 201804
- 折叠
摘要
Abstract
To address the issues of state identification and diagnosis for cells in lithium-ion battery modules,this paper proposes using electrochemical impedance spectroscopy and distribution of relaxation time curves with the affinity propagation(AP)clustering algorithm for abnormal identification of battery modules.The AP algorithm is compared with the density-based spatial clustering of applications with noise(DBSCAN)algorithm using 10 normal samples and multiple abnormal samples.The results show that AP performs better than DBSCAN in terms of accuracy,robustness,and parameter sensitivity(overlapping data,uneven density,etc.).In addition,the extreme gradient boosting(XGBoost)classifier is introduced,and after storing a certain amount of data corresponding to the battery,the same battery can be directly diagnosed for abnormalities through the XGBoost classifier.The anomaly detection rate is 100%,and the accuracy of identifying anomaly types exceeds 92%.Finally,a battery module abnormal identification and diagnosis system is proposed,which includes key steps such as data collection,feature extraction,identification,and diagnosis.关键词
锂离子电池,异常诊断/电化学阻抗谱,弛豫时间分布,仿射传播聚类算法Key words
lithium-ion battery/electrochemical impedance spectroscopy/distribution of relaxation time/abnormality/affinity propagation clustering algorithm分类
交通工程引用本文复制引用
袁永军,郭玄,王学远,姜波,戴海峰,魏学哲..基于电化学阻抗谱及弛豫时间分布的锂电池异常识别与诊断[J].同济大学学报（自然科学版）,2024,52(z1):223-234,12.
