用物理规律增强Mamba模型，提升电池健康预测精度与稳定性
PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics
- 将电化学老化机制融入Mamba架构，无需侵入式测量内部参数
- 在多个数据集上实现31.8%的平均误差降低，长周期预测更准确
- 兼顾精度与效率，适合实际电池管理系统部署
电池健康预测是电池管理系统的核心功能，但受工况依赖和传感器噪声影响，长期健康预测仍具挑战。本文提出PhyMamba，一种两阶段物理调制Mamba框架，将电化学老化机制嵌入序列建模。PhyMamba无需显式识别内部老化参数（常需侵入式测量）。第一阶段，轻量级Mamba编码器处理BMS信号，生成潜在表征，经老化参数化模块转化为物理感知的老化特征；第二阶段，定制化Mamba预测主干进行多周期预测，通过物理约束调节模型内部时序更新，使其符合退化一致性演化。在三个公开数据集上、多种预测时长下实验表明，PhyMamba整体性能最优，相比多样基线平均误差降低31.8%。该模型还具备优化的精度-效率权衡，支持鲁棒电池健康预测的实际部署。
原文摘要 · Abstract (English)
Battery health prognostics is a core function in battery management systems (BMSs), yet long-horizon health forecasting from BMS signals remains challenging due to operating-condition dependency and sensor noise. In this paper, we propose PhyMamba, a two-stage physics-modulated Mamba framework that integrates electrochemical aging into sequence modelling. PhyMamba does not require explicit identification of internal aging parameters, which often relies on intrusive measurements. In stage-1, a lightweight Mamba encoder first processes BMS signals and produces a latent representation that is transformed via an aging parameterization module, into physics-informed aging features. In stage-2, a customized Mamba forecasting backbone performs multi-cycle prediction, where physics is tightly integrated to regulate the model's internal temporal updates toward degradation-consistent evolution. Experiments on three public datasets under multiple forecast horizons show that PhyMamba achieves the best aggregated performance, with an overall mean error reduction of 31.8% compared with a diverse range of baselines. PhyMamba also offers an optimized accuracy-efficiency trade-off, which supports practical deployment for robust battery health prognostics.
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