SOURCE-LINKED INTELLIGENCE
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 tra
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-28T06:47:03.000Z
First collected: 2026-09-21T08:21:55.975Z. This is not the publication date.