Data-driven analysis of electrochemical impedance spectroscopy using the Loewner framework

Optimizing electrochemical devices requires an understanding of the complex interplay between mass transport and electrokinetics at the electrode-electrolyte interface. Electrochemical impedance spectroscopy (EIS) is a powerful tool for probing these processes, with analysis typically performed using equivalent circuit models (ECMs). However, selecting the appropriate ECM is challenging, as different models can yield deceptively similar spectra, complicating the accurate representation of the underlying physics. This work presents a data-driven approach for extracting the distribution of relaxation times (DRTs) through the Loewner framework (LF), facilitating the identification of the most suitable ECM for a given EIS dataset. The method is validated on different variants of Randles ECMs, which are commonly used to describe electrochemical interfaces. Its robustness to noisy datasets, as well as its advantages over similar methods that employ inversion algorithms, are also discussed.