Decoding Capacity Regeneration in Zinc‐Silver Batteries: Gas‐Guided Multi‐Physics Modeling and Hybrid AI Prediction

ABSTRACT

Accurately predicting battery life is vital for next‐generation energy storage. Zinc‐silver batteries (ZSBs) offer high power and energy density but suffer from short cycle life due to parasitic oxygen and hydrogen evolution reactions (OER/HER) that cause irreversible swelling. Here, we uncover a counterintuitive capacity regeneration phenomenon in ZSBs under a 20°C scenario: a regime where conventional degradation models fail. By establishing a monitoring system, we capture synchronized electrical, strain, gas concentration, pressure, and temperature signals. We provide evidence that OER and HER act as early gas‐phase precursors to regeneration, preceding electrical and mechanical responses. Their coupled electro‐chemo‐mechanical effects induce distinct surface strain transitions from peak to valley spikes. Leveraging these physical insights, we propose a hybrid mechanism‐data driven framework that integrates a chemistry‐informed neural network (CINN) with a TCN‐Mamba backbone and a spiking neural network for dynamic residual calibration. This architecture embeds electrochemical degradation equations as soft constraints, ensuring interpretability while achieving high accuracy (e.g., RMSE ≈ 0.29 cycles) remaining useful life prediction. Experimental results show superior accuracy and stability over purely data‐driven benchmarks. Our findings establish gas‐guided multi‐physics monitoring as a transformative paradigm for battery health management in complex, safety‐critical applications.