Detection of Impedance Inhomogeneity in Lithium-Ion Battery Packs Based on Local Outlier Factor

被引:2
作者
Zhu, Lijun [1 ]
Wang, Jian [1 ]
Wang, Yutao [1 ]
Pan, Bin [2 ]
Wang, Lujun [1 ]
机构
[1] Hubei Univ Technol, Hubei Key Lab High Efficiency Utilizat Solar Energ, Wuhan 430068, Peoples R China
[2] Guangdong Power Grid Co Ltd, Huizhou Power Supply Bur, Huizhou 516000, Peoples R China
基金
国家重点研发计划;
关键词
Lithium-ion battery; EIS; DRT; battery packs; detection of inhomogeneities; INTERNAL SHORT-CIRCUIT; RELAXATION-TIMES; DIAGNOSIS; FAILURE; SPECTRA;
D O I
10.3390/en17205123
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The inhomogeneity between cells is the main cause of failure and thermal runaway in Lithium-ion battery packs. Electrochemical Impedance Spectroscopy (EIS) is a non-destructive testing technique that can map the complex reaction processes inside the battery. It can detect and characterise battery anomalies and inconsistencies. This study proposes a method for detecting impedance inconsistencies in Lithium-ion batteries. The method involves conducting a battery EIS test and Distribution of Relaxation Times (DRT) analysis to extract characteristic frequency points in the full frequency band. These points are less affected by the State of Charge (SOC) and have a strong correlation with temperature, charge/discharge rate, and cycles. An anomaly detection characteristic impedance frequency of 136.2644 Hz was determined for a cell in a Lithium-ion battery pack. Single-frequency point impedance acquisition solves the problem of lengthy measurements and identification of anomalies throughout the frequency band. The experiment demonstrates a significant reduction in impedance measurement time, from 1.05 h to just 54 s. The LOF was used to identify anomalies in the EIS data at this characteristic frequency. The detection results were consistent with the actual conditions of the battery pack in the laboratory, which verifies the feasibility of this detection method. The LOF algorithm was chosen due to its superior performance in terms of FAR (False Alarm Rate), MAR (Missing Alarm Rate), and its fast anomaly identification time of only 0.1518 ms. The method does not involve complex mathematical models or parameter identification. This helps to achieve efficient anomaly identification and timely warning of single cells in the battery pack.
引用
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页数:20
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