Degradation Regime Discovery for EIS-Based Lithium-Ion Battery SOH Estimation Using Equivalent-Circuit and Vision-Based Features
- 1Department of Chemical Engineering Materials Environment, “Sapienza” University of Rome, via Eudossiana 18, 00184 Rome, Italy
Abstract
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 the 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 aging 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, 1RCPE, and 2RC models (MAE reduced by up to 58.1%), but with different improvements, indicating that the value of latent degradation information depends on how much temporal structure the underlying ECM already captures.
Related
- Fast State-of-Health Estimation of Lithium-Ion Batteries Using Short-Term Relaxation-Voltage Features Integrated with a Differential Evolution-Optimized Extreme Learning Machine Algorithm
- Structure–Performance Interplay and Mechanical Degradation Modes in Lithium Cobalt Oxide Cathodes: Implications for Flexible Lithium-Ion Battery Design
- Aging Trends and Predictive Value of Equivalent Circuit Parameters: The Role of Operating Conditions in Battery State-of-Health Estimation
- Critical Evaluation of Electrochemical Impedance Spectroscopy for State-of-Health Estimation of Lithium-Ion Cells Against Battery Management System Baselines
- Probing Formation, Degradation, and Entropic Changes in Lithium-Ion Batteries Via a Versatile Reference Electrode
BibTeX
@article{tulabi2026degradation,
title = {{Degradation Regime Discovery for EIS-Based Lithium-Ion Battery SOH Estimation Using Equivalent-Circuit and Vision-Based Features}},
author = {Milad Tulabi and Roberto Bubbico},
journal = {Batteries},
year = {2026},
month = nov,
doi = {10.3390/batteries12110471},
url = {https://www.mdpi.com/2313-0105/12/11/471},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Degradation Regime Discovery for EIS-Based Lithium-Ion Battery SOH Estimation Using Equivalent-Circuit and Vision-Based Features
AU - Milad Tulabi
AU - Roberto Bubbico
PY - 2026
DA - 2026/11/06
JO - Batteries
DO - 10.3390/batteries12110471
UR - https://www.mdpi.com/2313-0105/12/11/471
AB - 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 the 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 aging 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, 1RCPE, and 2RC models (MAE reduced by up to 58.1%), but with different improvements, indicating that the value of latent degradation information depends on how much temporal structure the underlying ECM already captures.
SN - 2313-0105
ER -