A Battery Internal-Short-Circuit Fault Diagnosis Method Combining Battery Phase Plane with Conformer-BiGRU Network

MAO Lin; ZHANG Haixin; HE Zhiwei; GAO Mingyu; DONG Zhekang

doi:10.11999/JEIT250313

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MAO Lin, ZHANG Haixin, HE Zhiwei, GAO Mingyu, DONG Zhekang. A Battery Internal-Short-Circuit Fault Diagnosis Method Combining Battery Phase Plane with Conformer-BiGRU Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250313

Citation:

MAO Lin, ZHANG Haixin, HE Zhiwei, GAO Mingyu, DONG Zhekang. A Battery Internal-Short-Circuit Fault Diagnosis Method Combining Battery Phase Plane with Conformer-BiGRU Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250313

Citation:

MAO Lin, ZHANG Haixin, HE Zhiwei, GAO Mingyu, DONG Zhekang. A Battery Internal-Short-Circuit Fault Diagnosis Method Combining Battery Phase Plane with Conformer-BiGRU Network[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250313

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A Battery Internal-Short-Circuit Fault Diagnosis Method Combining Battery Phase Plane with Conformer-BiGRU Network

doi: 10.11999/JEIT250313 cstr: 32379.14.JEIT250313

MAO Lin^{1, 2},
ZHANG Haixin^{1, 2},
HE Zhiwei^{1, 2},
GAO Mingyu^{1, 2
,
,},
DONG Zhekang^{1, 2}

1.
School of Electronics Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Zhejiang Key Laboratory of Intelligent Vehicle Electronics Research, Hangzhou 310018, China

Funds: The National Natural Science Foundation of China (62227802)

Rev Recd Date: 2025-07-25

Available Online: 2025-07-31

Abstract

Abstract

Objective New Energy Vehicles (NEVs) have gained rapid popularity in recent years due to their environmental benefits and high efficiency. However, as their market share continues to grow, concerns regarding frequent malfunctions and safety risks have also increased. Among these issues, Internal Short Circuit (ISC) faults are particularly concerning due to their strong concealment and the potential for severe consequences. Without accurate diagnosis and timely intervention, ISC faults can result in serious safety incidents. Therefore, developing efficient and reliable diagnostic methods for ISC faults is of practical significance. Methods A novel ISC fault diagnosis method is proposed for battery packs by combining an improved battery phase plane approach with a Conformer-BiGRU network. First, the improved battery phase plane method is employed to extract two-dimensional features from voltage sequences, providing deeper spatial and structural information. Second, a Conformer-BiGRU network is employed to learn features from the voltage data. The network integrates a CNN branch for local feature extraction and a Transformer branch for global representation. A feature coupling unit fuses the outputs of both branches, which are then passed to a BiGRU module to classify individual cells within the battery pack and detect ISC faults. Results and Discussions The proposed method is evaluated using fault data collected from an experimental platform. The results demonstrate that the improved battery phase plane effectively distinguishes between normal and faulty batteries within a two-dimensional plane (Figure 6) and further confirm its capability to detect ISC faults with varying severity under different data volumes (Figure 9). Using the Conformer-BiGRU network for fault diagnosis, the method achieves classification accuracies of 94.30%, 92.70%, and 94.85% under FUDS, UDDS, and US06 operating conditions, respectively (Table 3), significantly exceeding the performance of comparative models. Additionally, the feature extraction module contributes to an overall performance improvement of approximately 2.04% (Table 4). These findings indicate that the proposed method exhibits strong robustness (Table 4 and Figure 11) and offers a promising approach for enhancing the safety of NEVs. Conclusions This study proposes a novel method for diagnosing ISC faults in battery packs by integrating an improved battery phase plane approach with a Conformer-BiGRU network. The main contributions are as follows: First, the improved battery phase plane method enhances the separability of different fault states in two-dimensional space by incorporating both voltage and its first-order differential distribution, addressing the limitations of conventional one-dimensional feature extraction. Second, a hybrid Conformer-BiGRU architecture is developed, in which the Conformer module captures local discharge characteristics, while the BiGRU module models temporal dependencies. These features are integrated through a feature coupling unit to achieve cross-level feature fusion. Third, an experimental ISC fault dataset with varying severity levels is established using a self-built testing platform. Experimental results demonstrate average diagnostic accuracy, recall, and F1-scores of 91.26%, 85.17%, and 88.09%, respectively, across three international driving cycles. Although laboratory testing verifies the effectiveness of the proposed method, real-world application requires targeted optimization. This includes adapting BiGRU parameters during the migration of the Improved Battery Phase Plane (IBPP) module and refining the Conformer’s local perception weights through transfer learning to enhance feature decoupling. Future research focuses on improving diagnostic performance under concurrent fault scenarios to enhance engineering robustness in complex operating conditions.
- New energy vehicles,
- Battery packs,
- Fault diagnosis,
- Internal Short Circuit (ISC)

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References(35)

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