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The Early Detection of Faults for Lithium-Ion Batteries in Energy Storage Systems Using Independent Component Analysis with Mahalanobis Distance

Author

Listed:
  • Seunghwan Jung

    (Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Minseok Kim

    (Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Eunkyeong Kim

    (Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Baekcheon Kim

    (Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Jinyong Kim

    (Department of Electrical and Electronics Engineering, Pusan National University, Busan 46241, Republic of Korea)

  • Kyeong-Hee Cho

    (Energy Platform Research Center, Korea Electrotechnology Research Institute, Gwangju 61751, Republic of Korea)

  • Hyang-A Park

    (Energy Platform Research Center, Korea Electrotechnology Research Institute, Gwangju 61751, Republic of Korea)

  • Sungshin Kim

    (Department of Electrical Engineering, Pusan National University, Busan 46241, Republic of Korea)

Abstract

In recent years, battery fires have become more common owing to the increased use of lithium-ion batteries. Therefore, monitoring technology is required to detect battery anomalies because battery fires cause significant damage to systems. We used Mahalanobis distance (MD) and independent component analysis (ICA) to detect early battery faults in a real-world energy storage system (ESS). The fault types included historical data of battery overvoltage and humidity anomaly alarms generated by the system management program. These are typical preliminary symptoms of thermal runaway, the leading cause of lithium-ion battery fires. The alarms were generated by the system management program based on thresholds. If a fire occurs in an ESS, the humidity inside the ESS will increase very quickly, which means that threshold-based alarm generation methods can be risky. In addition, industrial datasets contain many outliers for various reasons, including measurement and communication errors in sensors. These outliers can lead to biased training results for models. Therefore, we used MD to remove outliers and performed fault detection based on ICA. The proposed method determines confidence limits based on statistics derived from normal samples with outliers removed, resulting in well-defined thresholds compared to existing fault detection methods. Moreover, it demonstrated the ability to detect faults earlier than the point at which alarms were generated by the system management program: 15 min earlier for battery overvoltage and 26 min earlier for humidity anomalies.

Suggested Citation

  • Seunghwan Jung & Minseok Kim & Eunkyeong Kim & Baekcheon Kim & Jinyong Kim & Kyeong-Hee Cho & Hyang-A Park & Sungshin Kim, 2024. "The Early Detection of Faults for Lithium-Ion Batteries in Energy Storage Systems Using Independent Component Analysis with Mahalanobis Distance," Energies, MDPI, vol. 17(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:535-:d:1323964
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    References listed on IDEAS

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    1. Ma, Mina & Li, Xiaoyu & Gao, Wei & Sun, Jinhua & Wang, Qingsong & Mi, Chris, 2022. "Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA," Applied Energy, Elsevier, vol. 324(C).
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    4. Olatomiwa Badmos & Andreas Kopp & Timo Bernthaler & Gerhard Schneider, 2020. "Image-based defect detection in lithium-ion battery electrode using convolutional neural networks," Journal of Intelligent Manufacturing, Springer, vol. 31(4), pages 885-897, April.
    5. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
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