AI RESEARCH
Interpretable Battery Aging without Extra Tests via Neural-Assisted Physics-based Modelling
arXiv CS.LG
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ArXi:2604.01229v1 Announce Type: cross State of health (SoH) is widely used for battery management, but it is a single scalar and offers limited interpretability. Two batteries with similar SoH can exhibit very different degradation behaviors and the lack of interpretability hinders optimal battery operation. In this paper, we propose IBAM for interpretable battery aging modelling with a neural-assisted physics-based framework. IBAM outputs a 2-D aging fingerprint without extra diagnostic tests and uses only routine logs from the battery management system.