Battery Pack Multi-fault Diagnosis Algorithm Based on Dual-Perspective Spectral Attention Fusion

LIU Mingjun; GU Shenyu; YIN Jingde; ZHANG Yifan; DONG Zhekang; JI Xiaoyue

doi:10.11999/JEIT251156

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LIU Mingjun, GU Shenyu, YIN Jingde, ZHANG Yifan, DONG Zhekang, JI Xiaoyue. Battery Pack Multi-fault Diagnosis Algorithm Based on Dual-Perspective Spectral Attention Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251156

Citation:

LIU Mingjun, GU Shenyu, YIN Jingde, ZHANG Yifan, DONG Zhekang, JI Xiaoyue. Battery Pack Multi-fault Diagnosis Algorithm Based on Dual-Perspective Spectral Attention Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251156

Citation:

LIU Mingjun, GU Shenyu, YIN Jingde, ZHANG Yifan, DONG Zhekang, JI Xiaoyue. Battery Pack Multi-fault Diagnosis Algorithm Based on Dual-Perspective Spectral Attention Fusion[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251156

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Battery Pack Multi-fault Diagnosis Algorithm Based on Dual-Perspective Spectral Attention Fusion

doi: 10.11999/JEIT251156 cstr: 32379.14.JEIT251156

LIU Mingjun^{1, 2},
GU Shenyu^{1, 2},
YIN Jingde^{1, 2},
ZHANG Yifan^{1, 2},
DONG Zhekang^{1, 2
,
,},
JI Xiaoyue³

1.
School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Zhejiang Province Key Laboratory of Intelligent Vehicle Electronics, Hangzhou 310018, China
3.
Department of Precision Instrument, Tsinghua University, Beijing 100084, China

Funds: China Postdoctoral Science Foundation (2024T170463, 2024M751676), The National Natural Science Foundation of China (62206062), Zhejiang Provincial Natural Science Foundation of China (LZYQ25F020005), Ministry of Science and Technology-Yangtze River Delta Science and Technology Innovation Program (2023CSJGG1300)

Received Date: 2025-11-01
Accepted Date: 2025-12-22
Rev Recd Date: 2025-12-18

Available Online: 2026-01-03

Abstract

Abstract

Objective With the rapid growth of electric vehicles and their widespread deployment, battery pack faults have become more frequent, creating an urgent need for efficient fault diagnosis methods. Although deep learning-based approaches have achieved notable progress, existing studies remain limited in addressing multiple fault types, such as Internal Short Circuit (ISC), sensor noise, sensor drift, and State-Of-Charge (SOC) inconsistency, and in modeling the coupling relationships among these faults. To address these limitations, a multi-fault diagnosis algorithm for battery packs based on dual-perspective spectral attention is proposed. A dual-perspective tokenization module is designed to extract spatiotemporal features from battery data, whereas a spectral attention mechanism addresses non-stationary time-series characteristics and captures long-term dependencies, thereby improving diagnostic performance. Methods To improve spatiotemporal feature extraction and fault diagnosis performance, a dual-perspective spectral attention fusion algorithm for battery pack multi-fault diagnosis is proposed. The overall architecture consists of four core modules (Fig. 3): a dual-perspective tokenization module, a spectral attention module, a feature fusion module, and an output module. The dual-perspective tokenization module applies positional encoding to jointly model temporal and spatial dimensions, enabling comprehensive spatiotemporal feature representation. When combined with the spectral attention mechanism, the capability of the model to handle non-stationary characteristics is strengthened, leading to improved diagnostic performance. In addition, to address the lack of comprehensive publicly available datasets for battery pack fault diagnosis, a new dataset is constructed, covering ISC, sensor noise, sensor drift, and SOC inconsistency faults. The dataset includes three operating conditions, FUDS, UDDS, and US06, which alleviates data scarcity in this research field. Results and Discussions Experimental results indicate that the proposed method improves average precision, recall, F1 score, and accuracy by 10.98%, 12.64%, 13.84%, and 13.45%, respectively, compared with existing optimal fault diagnosis methods. Comparison experiments under different operating conditions (Table 6) support this conclusion. Conventional convolutional neural network methods perform well in local feature extraction; however, fixed-size convolution kernels are not well suited to time features with varying frequencies, which limits long-term temporal dependency modeling and global feature capture. Recurrent neural network-based methods show reduced computational efficiency when large-scale datasets are processed. Transformer-based models face constraints in spatial feature extraction and in representing temporal variations. By contrast, the proposed algorithm addresses these limitations through an integrated architectural design. Ablation experiments demonstrate the contribution of each module to overall performance (Table 7), and the complete framework improves average F1 score and accuracy by 9.30% and 9.26%, respectively, compared with ablation variants. Robustness analysis under simulated noise conditions (Table 8) shows that the proposed method achieves accuracy improvements ranging from 49.95% to 124.34% over baseline methods at noise levels from –2 dB to –8 dB, indicating strong noise resistance. Conclusions A multi-fault diagnosis algorithm for battery packs is presented that integrates dual-perspective tokenization and spectral attention to combine spatiotemporal and spectral information. The dual-perspective tokenization module performs tokenization and positional encoding along temporal and spatial axes, which improves spatiotemporal representation. The spectral attention mechanism strengthens modeling of non-stationary signals and long-term dependencies. Experiments under FUDS, UDDS, and US06 driving cycles show that the proposed method outperforms existing multi-fault diagnosis approaches, with average gains of 13.84% in F1 score and 13.45% in accuracy. Ablation studies confirm that both modules contribute substantially and that their combination enables effective handling of complex time-series features. Under high-noise conditions (–2 dB, –4 dB, –6 dB, and –8 dB), the method also shows improved robustness, with accuracy gains of 49.95%, 90.39%, 112.01%, and 124.34%, respectively, compared with baseline methods. Several limitations remain. First, the data are mainly derived from laboratory simulations, and further validation under real-world operating conditions is required. Second, the effect of fault severity on battery management system hierarchical decision making has not been fully addressed, and future work will focus on establishing a fault severity grading strategy. Third, physical interpretability requires further improvement, and subsequent studies will explore the integration of equivalent circuit models or electrochemical mechanism models to balance diagnostic accuracy and interpretability.
- Multi-fault diagnosis,
- Lithium-ion battery pack,
- Spectral attention

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References(35)

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