An Assessment of Commonly Used Equivalent Circuit Models for Corrosion Analysis: A Bayesian Approach to Electrochemical Impedance Spectroscopy

TL;DR

This paper evaluates the effectiveness of common equivalent circuit models for corrosion analysis using Bayesian inference on EIS data, showing that low-frequency measurements can often be omitted without losing accuracy, thus speeding up data collection.

Contribution

It introduces a Bayesian framework to assess ECM suitability for corrosion EIS, proposing methods to reduce measurement time by identifying minimal frequency ranges needed.

Findings

Bayesian inference effectively evaluates ECM applicability.

Low-frequency data can often be omitted without losing accuracy.

Simple posterior checks help determine ECM suitability and measurement requirements.

Abstract

Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of a metallic materials. The analysis of EIS hinges on the selection of an appropriate equivalent circuit model (ECM) that accurately characterizes the system under study. In this work, we systematically examined the applicability of three commonly used ECMs across several typical material degradation scenarios. By applying Bayesian Inference to simulated corrosion EIS data, we assessed the suitability of these ECMs under different corrosion conditions and identified regions where the EIS data lacks sufficient information to statistically substantiate the ECM structure. Additionally, we posit that the traditional approach to EIS analysis, which often requires measurements to very low frequencies, might not be always necessary to correctly model the appropriate ECM. Our study assesses the impact of omitting data from low to medium-frequency ranges on inference results and reveals that a significant portion of low-frequency measurements can be excluded without substantially compromising the accuracy of extracting system parameters. Further, we propose simple checks to the posterior distributions of the ECM components and posterior predictions, which can be used to quantitatively evaluate the suitability of a particular ECM and the minimum frequency required to be measured. This framework points to a pathway for expediting EIS acquisition by intelligently reducing low-frequency data collection and permitting on-the-fly EIS measurements