Robust and fast estimation of equivalent circuit model from noisy electrochemical impedance spectra

Abstract

The Equivalent Circuit Model (ECM) is a powerful technique to quantitatively analyze and compare Electrochemical Impedance Spectroscopy (EIS) data. In practice, noise is prevalent in EIS data, due to fuel cell system fluctuations, limited measuring time, and instruments, challenging the accuracy of ECM. There are algorithms that work well on noisy data, yet in many cases, either run time or robustness remains a problem. In this paper, we proposed a robust and fast algorithm that detects outliers, weighs the EIS data, and automatically fits an ECM within a second. For both experimentally measured and simulated noisy EIS data, the new algorithm reduced the impact of noise drastically. The algorithm is demonstrated in Python as part of the open-source software EISART at github.com/leehangyue/EISART.