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

电阻抗 锂(药物) 离群值 电池(电) 荷电状态 锂离子电池 电池组 恒虚警率 介电谱 异常检测 材料科学 计算机科学 电气工程 化学 物理 工程类 人工智能 算法 电化学 电极 医学 功率(物理) 量子力学 内分泌学 物理化学
作者
Lijun Zhu,Jian Wang,Yutao Wang,Bin Pan,Lujun Wang
出处
期刊:Energies [Multidisciplinary Digital Publishing Institute]
卷期号:17 (20): 5123-5123 被引量:3
标识
DOI:10.3390/en17205123
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.
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