A Physics-Aware Attention LSTM Autoencoder for Early Fault Diagnosis of Battery Systems

• URL: http://arxiv.org/abs/2512.06809v1
• Date: Sun, 07 Dec 2025 11:58:09 GMT
• Title: A Physics-Aware Attention LSTM Autoencoder for Early Fault Diagnosis of Battery Systems
• Abstract summary: We propose a Physics-Aware Attention LSTM Autoencoder (PA-ALSTM-AE)<n>This framework explicitly integrates battery aging laws (mileage) into the deep learning pipeline through a multi-stage fusion mechanism.<n>Experiments on the large-scale Vloong real-world dataset demonstrate that the proposed method significantly outperforms state-of-the-art baselines.
• Score: 0.799536002595393
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Battery safety is paramount for electric vehicles. Early fault diagnosis remains a challenge due to the subtle nature of anomalies and the interference of dynamic operating noise. Existing data-driven methods often suffer from "physical blindness" leading to missed detections or false alarms. To address this, we propose a Physics-Aware Attention LSTM Autoencoder (PA-ALSTM-AE). This novel framework explicitly integrates battery aging laws (mileage) into the deep learning pipeline through a multi-stage fusion mechanism. Specifically, an adaptive physical feature construction module selects mileage-sensitive features, and a physics-guided latent fusion module dynamically calibrates the memory cells of the LSTM based on the aging state. Extensive experiments on the large-scale Vloong real-world dataset demonstrate that the proposed method significantly outperforms state-of-the-art baselines. Notably, it improves the recall rate of early faults by over 3 times while maintaining high precision, offering a robust solution for industrial battery management systems.

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