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

Abstract

This paper reports our initial development of simulation-informed machine learning algorithms for failure diagnostics in solid oxide fuel cell (SOFC) systems. We used physics-based models to simulate electrochemical impedance spectroscopy (EIS) response of a short SOFC stack under normal conditions and under three different failure modes: fuel maldistribution, delamination, and oxidant gas crossover to the anode channel. These data were used to train a support vector machine (SVM) model, which is able to detect and differentiate these failures in simulated data under various conditions. The SVM model can also distinguish these failures from simulated uniform degradation that often occurs with long-term operation. These encouraging results are guiding our ongoing efforts to apply EIS as a failure diagnostic for real SOFC cells and short stacks.