Internal Short-Circuit Fault Diagnosis for Lithium-Ion Batteries Based on Multivariate Information Entropy

Chen, Peiyu; Xu, Bin; Li, Qian; Gan, Zhiyong; Li, Chao; Kaidi, Zeng

doi:10.3390/app16105078

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Open AccessArticle

Internal Short-Circuit Fault Diagnosis for Lithium-Ion Batteries Based on Multivariate Information Entropy

by

Peiyu Chen

^1,2,

Bin Xu

²,

Qian Li

²,

Zhiyong Gan

²,

Chao Li

^1,3 and

Zeng Kaidi

^1,3,*

¹

National Industry-Education Platform for Energy Storage, Tianjin University, Tianjin 300384, China

²

Electric Power Research Institute, State Grid Tianjin Electric Power Company, Tianjin 300384, China

³

Key Laboratory of Smart Grid of Ministry of Education, Tianjin 300384, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2026, 16(10), 5078; https://doi.org/10.3390/app16105078

Submission received: 9 April 2026 / Revised: 18 May 2026 / Accepted: 18 May 2026 / Published: 19 May 2026

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

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Abstract

Lithium-ion battery energy storage systems (BESSs) face significant safety challenges arising from internal short-circuit (ISC) faults, which can ultimately trigger thermal runaway. To address this, this paper proposes an ISC fault diagnosis method based on multivariate information entropy (MIE). The proposed approach fuses voltage and temperature time series from battery cells to extract fault features via MIE. Furthermore, a hierarchical diagnosis framework incorporating statistical confidence intervals is developed to enable robust ISC fault diagnosis. Experiments were conducted on 180 Ah lithium iron phosphate batteries, utilizing external resistors to simulate ISC faults of varying severity. The method was further validated using real-world fault data from an electric vehicle accident. Results demonstrate that the proposed method effectively distinguishes between normal and faulty cells, with MIE values exhibiting a monotonic increase as fault severity intensifies. In the real-world dataset, the method identifies the faulty cell 240 s before a discernible voltage drop, demonstrating its capability for early ISC detection.

Keywords: internal short circuit; fault diagnosis; lithium-ion battery; multivariate information entropy

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MDPI and ACS Style

Chen, P.; Xu, B.; Li, Q.; Gan, Z.; Li, C.; Kaidi, Z. Internal Short-Circuit Fault Diagnosis for Lithium-Ion Batteries Based on Multivariate Information Entropy. Appl. Sci. 2026, 16, 5078. https://doi.org/10.3390/app16105078

AMA Style

Chen P, Xu B, Li Q, Gan Z, Li C, Kaidi Z. Internal Short-Circuit Fault Diagnosis for Lithium-Ion Batteries Based on Multivariate Information Entropy. Applied Sciences. 2026; 16(10):5078. https://doi.org/10.3390/app16105078

Chicago/Turabian Style

Chen, Peiyu, Bin Xu, Qian Li, Zhiyong Gan, Chao Li, and Zeng Kaidi. 2026. "Internal Short-Circuit Fault Diagnosis for Lithium-Ion Batteries Based on Multivariate Information Entropy" Applied Sciences 16, no. 10: 5078. https://doi.org/10.3390/app16105078

APA Style

Chen, P., Xu, B., Li, Q., Gan, Z., Li, C., & Kaidi, Z. (2026). Internal Short-Circuit Fault Diagnosis for Lithium-Ion Batteries Based on Multivariate Information Entropy. Applied Sciences, 16(10), 5078. https://doi.org/10.3390/app16105078

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Internal Short-Circuit Fault Diagnosis for Lithium-Ion Batteries Based on Multivariate Information Entropy

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