Non-destructive degradation pattern decoupling for early battery trajectory prediction via physics-informed learning

Manufacturing complexities and uncertainties have impeded the transition from material prototypes to commercial batteries, making their verification a critical quality assessment link. A fundamental challenge is to decouple electrochemical interactions for establishing a quantitative mapping from electrochemical parameters to macro battery performance. Here, we show that the proposed physics-informed learning model can quantify and visualize temporally resolved thermodynamic and kinetic parameters from field accessible electric signals, facilitating a non-destructive degradation pattern decoupling. The lifetime trajectory prediction is 25 times faster than the traditional capacity calibration test while retaining a 95.1% average accuracy across temperatures, underpinned by projected electrochemical data from early cycle observations which have not yet been established. We rationalize this predictability to the interpretation of statistical insights from material-agnostic featurization, excited by a multistep charging scheme with different current intensities and their switching conditions. The waste management of defective prototypes is enabled by statistically and non-destructively interpreting internal electrochemical states, demonstrating a 19.76 billion USD defective material recycling market by 2060. This paper highlights the potential of revisiting electrochemical degradation behaviors using physicsinformed learning and dynamic current excitations, facilitating next-generation battery manufacturing, reuse, and recycling sustainability.