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| Internal Short Circuit State Estimation for Lithium-Ion Batteries Based on Fault Feature Augmentation and Hybrid Neural Network Modeling |
| Shen Jiangwei1,2, Xu Pan1, Shu Xing3, Wei Fuxing1, Chen Zheng1 |
1. Faculty of Transportation Engineering Kunming University of Science and Technology Kunming 650550 China;
2. Yunnan International Joint Laboratory of Intelligent and Connected Transportation Kunming University of Science and Technology Kunming 650550 China;
3. Faculty of Vehicle Engineering Chongqing University of Technology Chongqing 400054 China |
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Abstract Timely detection and accurate identification of faulted batteries with internal short circuits are crucial for ensuring the safe and reliable operation of electric vehicles (EVs) and energy storage systems (ESS). Early detection of such faults helps to prevent safety hazards such as thermal runaway or fire, thereby ensuring the safety of both the battery system and the user. This paper proposes a novel method for estimating internal short-circuit states in lithium-ion battery packs employing a hybrid neural network approach. The proposed method effectively improves the algorithm's efficiency while maintaining high classification accuracy.
The method first addresses the subtle voltage differences that occur in the early stages of internal short circuits, where the voltage drop is often minimal. An average-difference model is constructed to compute the voltage difference between each battery cell and an “average cell”, yielding a sequence of differential voltage features. This sequence highlights the abnormal behavior of a single cell relative to the battery pack's overall behavior. It can detect even the slightest deviations in battery behavior, which would otherwise be difficult to distinguish from normal operation.
Then, different internal short-circuit severities are simulated. By injecting various parameters, high- confidence simulated fault datasets are generated to represent three distinct fault levels: micro, moderate, and severe short-circuit stages. These simulated datasets are then combined with experimental fault data, yielding a larger, more diverse sample of fault features. This enhanced dataset allows the model to learn from a richer set of fault features, thereby improving its ability to generalize across different battery conditions.
To further improve fault detection, a hybrid neural network model is incorporated using convolutional neural networks (CNNs) to extract local spatial features within the battery pack and bidirectional long short-term memory (BiLSTM) networks to capture temporal information from time-series data. The BiLSTM network processes a sequence of voltage differences over time, enabling the model to identify patterns and trends in fault progression. This hybrid approach leverages both spatial and temporal features, enabling the model to classify internal short-circuit states more accurately.
The proposed method is validated through internal short-circuit simulation experiments. The performance is evaluated from three perspectives: model accuracy, comparisons among different models, and the effectiveness of data augmentation techniques. The results show that the hybrid model achieves classification accuracies of 96.0%, 98.3%, and 99.0% for the micro-, moderate-, and severe-short-circuit stages, respectively. The proposed method is highly effective at detecting and identifying internal short-circuit faults in lithium-ion battery packs.
In conclusion, the proposed method offers a robust and efficient approach to detecting internal short-circuit faults in lithium-ion battery systems. By combining fault feature enhancement with a hybrid CNN-BiLSTM neural network, the method improves fault classification accuracy. It provides an effective solution for safety- critical applications in electric vehicles and energy storage systems. This approach not only enables early detection of internal short-circuit faults but also contributes to the development of safer, more reliable battery management systems.
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Received: 19 June 2025
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2026, Vol. 41 
