# Continuous Physics‐Informed Learning Expedited Battery Mechanism Decoupling

## TL;DR

A new battery modeling framework combines data and physics to track internal processes and predict aging, improving battery management and design.

## Contribution

A novel physics-informed battery modeling network (PIBMN) that enables continuous adaptation and decouples complex battery mechanisms.

## Key findings

- PIBMN captures fast and slow battery dynamics across various cell formats and chemistries.
- The model decouples overpotential components and tracks voltage in real time without additional measurements.
- PIBMN supports scalable battery management and optimization of next-generation battery manufacturing.

## 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 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.

A battery modeling framework combining dual neural networks with physical prior is presented to track internal mechanisms, decouple overpotential components, and early predict aging trajectories using only DC data, eliminating the need for additional measurements. The proposed framework based on PINN and PIKAN is well aligned with the current drive toward explainable, data‐efficient AI models in energy systems.