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<p>Reliable state estimation and fault diagnosis are critical for battery operation. However, existing data-driven methods often suffer from overfitting and poor interpretability, especially when data is scarce. We propose PIMST, a Physics-Informed Multi-scale Spatiotemporal Transformer that integrates electrochemical mechanisms with deep learning. This approach enforces physical consistency and improves robustness. We incorporate a physics-constrained loss derived from the Thevenin Equivalent Circuit Model, which embeds Kirchhoff’s laws and Ampere-hour integration directly into the network. Additionally, our architecture employs multi-scale temporal attention to capture both long-term degradation and high-frequency dynamics, while a Graph Attention Network models inter-cell coupling. Evaluations on NASA, Oxford, and CALCE datasets demonstrate that PIMST achieves a 0.42% RMSE for State of Health estimation and 98.7% accuracy for fault diagnosis. The model significantly outperforms baseline methods in few-shot settings. These results establish PIMST as a reliable and physically grounded framework for intelligent Battery Management Systems.</p>

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Physics-Informed Multi-scale spatiotemporal transformer for robust state estimation and fault localization in Lithium-ion batteries

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月17日

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  • Yuping Xia,
  • Zhe Luo,
  • Xiuyun Zhai,
  • Xinyu Wang,
  • Zhihao Tang

摘要

Reliable state estimation and fault diagnosis are critical for battery operation. However, existing data-driven methods often suffer from overfitting and poor interpretability, especially when data is scarce. We propose PIMST, a Physics-Informed Multi-scale Spatiotemporal Transformer that integrates electrochemical mechanisms with deep learning. This approach enforces physical consistency and improves robustness. We incorporate a physics-constrained loss derived from the Thevenin Equivalent Circuit Model, which embeds Kirchhoff’s laws and Ampere-hour integration directly into the network. Additionally, our architecture employs multi-scale temporal attention to capture both long-term degradation and high-frequency dynamics, while a Graph Attention Network models inter-cell coupling. Evaluations on NASA, Oxford, and CALCE datasets demonstrate that PIMST achieves a 0.42% RMSE for State of Health estimation and 98.7% accuracy for fault diagnosis. The model significantly outperforms baseline methods in few-shot settings. These results establish PIMST as a reliable and physically grounded framework for intelligent Battery Management Systems.