Continuous Physics-Informed Learning Expedited Battery Mechanism Decoupling.

ABSTRACT: Accurate prediction of battery behavior under different dynamic operating conditions is critical for both fundamental research and practical applications. However, the diversity of emerging materials and cell architectures presents significant challenges to the generalizability of conventional prognostic approaches. Here, a novel physics-informed battery modeling network (PIBMN) that integrates data-driven learning with physical priors, enabling continuous parameter adaptation and broad applicability across cell formats and chemistries, is proposed. PIBMN effectively captures both fast and slow dynamic responses under a wide range of load profiles, applicable to both commercial and laboratory-scale cells. By maintaining nonlinear expressivity while ensuring numerical stability, the model y

SUBMITTER: Ji S 

PROVIDER: S-EPMC12767122 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature