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

Seunghwan Jung, Minseok Kim, Eunkyeong Kim, Baekcheon Kim, Jinyong Kim, Kyeong-Hee Cho, Hyang-A Park and Sungshin Kim ()
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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

Energies, 2024, vol. 17, issue 2, 1-23

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.

Keywords: independent component analysis; Mahalanobis distance; early fault detection; lithium-ion battery; energy storage system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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