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

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  • Lijun Zhu

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage Systesm, Hubei University of Technology, Wuhan 430068, China)

  • Jian Wang

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage Systesm, Hubei University of Technology, Wuhan 430068, China)

  • Yutao Wang

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage Systesm, Hubei University of Technology, Wuhan 430068, China)

  • Bin Pan

    (Huizhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Huizhou 516000, China)

  • Lujun Wang

    (Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage Systesm, Hubei University of Technology, Wuhan 430068, China)

Abstract

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.

Suggested Citation

  • Lijun Zhu & Jian Wang & Yutao Wang & Bin Pan & Lujun Wang, 2024. "Detection of Impedance Inhomogeneity in Lithium-Ion Battery Packs Based on Local Outlier Factor," Energies, MDPI, vol. 17(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5123-:d:1499196
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    References listed on IDEAS

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    1. Rüther, Tom & Plank, Christian & Schamel, Maximilian & Danzer, Michael A., 2023. "Detection of inhomogeneities in serially connected lithium-ion batteries," Applied Energy, Elsevier, vol. 332(C).
    2. Ren, Dongsheng & Feng, Xuning & Lu, Languang & He, Xiangming & Ouyang, Minggao, 2019. "Overcharge behaviors and failure mechanism of lithium-ion batteries under different test conditions," Applied Energy, Elsevier, vol. 250(C), pages 323-332.
    3. Xinwei Cong & Caiping Zhang & Jiuchun Jiang & Weige Zhang & Yan Jiang & Linjing Zhang, 2021. "A Comprehensive Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles," Energies, MDPI, vol. 14(5), pages 1-21, February.
    4. Zhang, Caiping & Jiang, Yan & Jiang, Jiuchun & Cheng, Gong & Diao, Weiping & Zhang, Weige, 2017. "Study on battery pack consistency evolutions and equilibrium diagnosis for serial- connected lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 510-519.
    5. Babaeiyazdi, Iman & Rezaei-Zare, Afshin & Shokrzadeh, Shahab, 2021. "State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach," Energy, Elsevier, vol. 223(C).
    6. Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Constructing battery impedance spectroscopy using partial current in constant-voltage charging or partial relaxation voltage," Applied Energy, Elsevier, vol. 356(C).
    7. Beelen, H.P.G.J. & Raijmakers, L.H.J. & Donkers, M.C.F. & Notten, P.H.L. & Bergveld, H.J., 2016. "A comparison and accuracy analysis of impedance-based temperature estimation methods for Li-ion batteries," Applied Energy, Elsevier, vol. 175(C), pages 128-140.
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