Data-Driven Health Prognostics of NMC Lithium-Ion Batteries via Impedance Spectroscopy Using a Hybrid CNN-BiLSTM...

Accurate and robust battery health prognostics are critical for reliable battery management in electronic devices and electric vehicles. Previous studies have demonstrated that combining electrochemical impedance spectroscopy (EIS) with machine learning enables accurate health-state forecasting in LiCoO₂ coin cells. However, the applicability of this EIS-AI paradigm across diverse chemistries and industrial-grade battery formats remains unvalidated, limiting its practical deployment in energy storage systems. Here, we develop an EIS-AI battery prognostic framework and validate its performance on LiNi_1/3Mn_1/3Co_1/3O₂ (NMC111) cylindrical cells and LiNi_0.8Mn_0.1Co_0.1O₂ (NMC811) pouch cells. A hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture is developed to estimate state of health (SoH) and predict remaining useful life (RUL) from EIS spectra. Trained on an in-house dataset comprising over 13,000 impedance spectra from 22 cells (8 NMC111 and 14 NMC811), the model achieves robust performance, with average coefficients of determination (R2) exceeding 0.92 for SoH estimation and 0.90 for RUL prediction across various batteries and cycling protocols. Salient feature analysis further reveals chemistry- and protocol-dependent frequency regimes associated with degradation. These results demonstrate that impedance spectra constitute physically informative descriptors for data-driven battery prognostics and provide a scalable and interpretable pathway for deploying EIS-AI frameworks in real-world battery management systems (BMSs).