PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics

2026-08-28

AI Summary

本文提出PhyMamba框架，通过结合电化学老化和序列建模解决电池健康预测中的条件依赖性和传感器噪声问题，实现准确高效的长期预测。

Abstract

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.

Problem

• battery health prognostics
• operating-condition dependency
• sensor noise

Innovation

• Physics-Modulated
• Electrochemical Aging
• Sequence Modeling
• Latent Representation
• Degradation-Consistent Evolution