将电池老化规律融入深度学习，提升早期故障检出率。
A Physics-Aware Attention LSTM Autoencoder for Early Fault Diagnosis of Battery Systems
- 用里程数引导特征构建与LSTM记忆单元校准
- 早期故障召回率提升超3倍，精度仍高
- 适合工业级电池管理系统部署
电池安全对电动车至关重要。由于异常信号微弱且受动态运行噪声干扰，早期故障诊断仍具挑战。现有数据驱动方法常因“物理盲视”导致漏检或误报。为此，本文提出物理感知注意力LSTM自编码器（PA-ALSTM-AE）。该框架通过多阶段融合机制，将电池老化规律（里程）显式嵌入深度学习流程：自适应物理特征构建模块筛选里程敏感特征，物理引导的潜在空间融合模块基于老化状态动态校准LSTM记忆单元。在大规模真实世界数据集Vloong上的实验表明，所提方法显著优于当前主流基线。尤其在早期故障检测中，召回率提升超过3倍，同时保持高精度，为工业级电池管理系统提供可靠解决方案。
原文摘要 · Abstract (English)
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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