Real‐Time, Interpretable Diagnostics for Solid‐State Batteries via Machine Learning on In Situ Impedance Spectra

Electrochemical impedance spectroscopy (EIS) is highly sensitive to interfacial processes in solid‐state batteries (SSBs) but can be difficult to interpret in real time. Here we pair in situ EIS with machine learning (ML) to create a lightweight, interpretable diagnostic framework. By encoding spectra into feature vectors and training tree‐based multi‐output regressors, we achieve real‐time predictions of state of charge and cycle index with R 2 > 0.99. Feature‐importance analysis links dominant mid‐ and low‐frequency responses to cathode and anode degradation, respectively. Remarkably, retraining on only five key features maintains sub‐percent accuracy, enabling millisecond‐scale, impedance‐based monitoring suitable for embedded solid‐state battery management systems.