A Connectivity-Based Outlier Factor Method for Rapid Battery Internal Short-Circuit Diagnosis

Zhiguo Dong, Gongqiang Li, Fengxiang Xie, Shiwen Zhao, Xiaofan Ji, Mofan Tian and Kailong Liu ()
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Zhiguo Dong: National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China
Gongqiang Li: National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China
Fengxiang Xie: National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China
Shiwen Zhao: School of Control Science and Engineering, Shandong University, Jinan 250100, China
Xiaofan Ji: National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China
Mofan Tian: National Institute of Clean-and-Low-Carbon Energy, Beijing 102211, China
Kailong Liu: School of Control Science and Engineering, Shandong University, Jinan 250100, China

Sustainability, 2025, vol. 17, issue 11, 1-14

Abstract: Internal short-circuit (ISC) is a critical failure mode in lithium-ion (Li-ion) batteries that can trigger thermal runaway and pose serious risks to both environmental and human safety. Early-stage ISC faults are particularly challenging to detect due to their subtle characteristics and the masking effects of voltage fluctuations. To address these challenges, this study proposes a rapid and accurate ISC diagnosis method based on the connectivity-based outlier factor (COF) algorithm. The key innovation lies in the preprocessing of terminal voltage to amplify fault signatures and suppress natural fluctuations, thereby enhancing sensitivity to early anomalies. The COF algorithm is then applied to identify ISC faults in real time. Validation under urban-dynamometer driving schedule (UDDS) conditions demonstrates the method’s effectiveness: it successfully detects early ISC faults with an equivalent resistance as high as 100 Ω within 207 s of onset. This unsupervised, data-driven approach improves fault detection speed and accuracy, contributing to the advancement of safe, reliable, and sustainable LIB deployment in clean energy and transportation systems.

Keywords: energy storage; lithium-ion batteries; fault diagnosis; low carbon; data-driven (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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