Automated electrochemical impedance spectroscopy fitting for quantitative analysis of degradation modes in lithium-ion batteries

Electrochemical Impedance Spectroscopy (EIS) is a powerful diagnostic technique for probing internal electrochemical processes and assessing lithium-ion battery degradation, yet its practical application is often limited by the complexity and subjectivity of manual fitting procedures. This paper proposes a fully automated algorithm for extracting equivalent circuit model (ECM) parameters from EIS measurements to quantify the battery degradation modes, namely conductivity loss (CL), loss of active material (LAM), and loss of lithium inventory (LLI). The developed method integrates an automated partitioning of the measured impedance spectrum into three frequency regions using optimized percentile thresholds. This segmentation enables an initial estimation of circuit parameters describing lithium-ion cell behaviour, including ohmic resistance, charge-transfer resistances, non-ideal capacitances, and Warburg diffusion elements. These initial estimates are subsequently refined through an iterative, sequential optimization process based on a Trust Region Reflective least-squares algorithm, using a reference spectrum and propagating the optimized parameters across successive aging cycles. The algorithm was validated using two experimental datasets comprising various cell types and impedance magnitudes ranging from micro-ohms to a few ohms. The proposed approach minimizes operator intervention and provides a reliable and scalable tool for battery health monitoring, suitable for both real-life diagnostics and research activities involving large volumes of experimental data. • Automatized algorithm for multiple Electrochemical Impedance Spectroscopy fitting • Optimization of initial parameters set once, then automatically selected by code. • Practical applications are provided with validation on two datasets. • Equivalent Circuit Model theory is applied for aging mechanisms quantifications. • Industrial potential via improvements in battery management and aging predictions