PhyMamba: Physics-Modulated Mamba for Robust Battery Health Prognostics
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, ...
| Authors | Sara Sameer, Yunyi Zhao, Wei Zhang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 28 August 2026 |
| Last updated at source | 28 August 2026 |
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