Identification and Diagnosis of Abnormal Lithium-ion Batteries Based on Electrochemical Impedance Spectroscopy and Distribution of Relaxation Time Analysis

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.