Operando Diagnostics of Solid Oxide Fuel Cell Stack Via Electrochemical Impedance Spectroscopy Simulation-Informed Machine Learning

Abstract

In this work, we apply a machine learning approach to solid oxide fuel cell (SOFC) system diagnostics. Instead of fitting electrochemical impedance spectroscopy (EIS) into a physics based model or equivalent circuit, we train machine learning models to recognize failures from a database of simulated EIS. We use a coarse-grained physics-based model to simulate stack EIS under three different failure modes: fuel maldistribution, delamination, and cathode gas crossover to anode channel. Synthesized machine learning classification models successfully recognize these different degradation mechanisms in simulated data across different operating conditions. We are also able to differentiate these failures from the uniform degradation that tends to occur with SOFC over time. These encouraging results prompt our current effort to implement machine learning diagnostics methods on experimental EIS collected on SOFC short stack.