From EV to stationary energy storage: EIS-based SoH estimation for second life Li-ion batteries

Estimating the State of Health (SoH) of second-life lithium-ion batteries is crucial for determining their suitability for various second-life applications. Electrochemical Impedance Spectroscopy (EIS) is considered a promising method for SoH estimation, as it not only aids in evaluating SoH but also quantifies the degradation modes occurring within the battery cell. Since second-life batteries are more prone to degradation under even slight stress conditions, accurate SoH estimation becomes increasingly important. A computationally efficient Extra Trees Regressor (ETR) model is implemented to estimate the SoH of second-life high-energy pouch cells. The model is trained under static conditions and tested under dynamic conditions, yielding strong performance with maximum mean absolute error of 0.0024 and a mean absolute percentage error (MAPE) of 0.0026. Additionally, a fractional-order equivalent circuit model is fitted to the EIS spectrum to identify equivalent circuit parameters, which are then used to determine the major degradation modes (conductivity loss, loss of lithium inventory, and loss of active material) within the battery cells.