Continuous Physics-Informed Learning Expedited Battery Mechanism Decoupling

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 yields high-fidelity, interpretable representations of internal electrochemical states. Beyond conventional health prognostics, PIBMN introduces a novel capability to decouple complex kinetics processes and concurrently track terminal voltage in real time, enabling mechanistic diagnostics with high resolution. As such, PIBMN establishes a versatile and scalable framework for in-line quality control, adaptive cell-specific battery management, and data-informed optimization of next-generation battery manufacturing processes.