Degradation Regime Discovery for EIS-Based Lithium-Ion Battery SOH Estimation Using Equivalent-Circuit and Vision-Based Features

Accurate State of Health (SOH) estimation is essential for the safe and reliable operation of lithium-ion batteries (LIBs), yet most electrochemical impedance spectroscopy (EIS)-based approaches fix a single equivalent circuit model (ECM) topology and measurement state without testing whether that choice is actually the most degradation-sensitive one, and rarely cross-check circuit-based findings against an independent, model-agnostic representation of the same impedance data. This study addresses both gaps through a physics-constrained framework applied to four commercial Li-ion cells cycled to end of life, with EIS spectra acquired at nine states in a cycle. Six ECM topologies (1R to 2RCPEW) were fitted at each state, and Pearson Correlation Coefficient was used to jointly rank topology–state combinations by their sensitivity to capacity fade. The best-ranked ECM parameters were then used as input to a left-to-right Hidden Markov Model (HMM) to segment battery ageing into ordered latent degradation regimes, with results compared against an HMM applied to DINOv2 embeddings of Nyquist and DRT plots. Finally, Gaussian Process Regression (GPR) was used to estimate SOH with and without the HMM-derived regime label as an auxiliary feature. Results show that State V (reached after a post-charge rest) yields the highest degradation sensitivity for most ECMs, with ohmic and charge-transfer resistances as the dominant health factors. Adding the latent regime feature improved SOH prediction for the 1RC and 1RCPE models (MAE reduced by up to 46.5%) but degraded uncertainty calibration for the 2RC model, indicating that the value of latent degradation information depends on how much temporal structure the underlying ECM already captures.