Data-based modelling of proton exchange membrane fuel cell performance and degradation dynamics

Abstract

Proton exchange membrane fuel cell (PEMFC) is in the commercial adoption process for hard-to-decarbonize applications such as transport. However, its long-term durability, reliability, diagnostics, and performance remain a critical challenge. In this study, the data-based modelling technique, artificial neural network (ANN), is adopted to comprehend performance degradation and thermal and pressure dynamics. Experimental data consisting of polarization and cyclic voltammetry data are acquired after every 6,000 accelerated stress test (AST) degradation cycles as stipulated by the US Department of Energy guidelines. Over 40,000 test cases were considered for training and evaluating the neural network comprising data from 30,000 full-cell AST cycles. Fuel cell operating temperatures, pressures, flow rates, and relative humidities are varied to capture the entire spectrum of PEMFC performance characteristics. Parameters such as the number of AST cycles endured or instantaneous catalyst electrode surface area, operating current density, reactant flow rate, relative humidity, system temperature and pressure are considered as feature vectors (inputs) to predict cell output voltage, reactant outlet pressure and temperature for both anode and cathode streams. Data preprocessing and batch learning are implemented in Python to improve prediction accuracy and computational time; considering the substantial nonlinear data sets, various Keras library optimizers and corresponding hyperparameters are investigated for better convergence. The trained neural network model is evaluated on 15,000 test cases resulting in an R2 ≥ 0.995 for all the predicted variables, demonstrating the capability of data-based models to accurately predict the nonlinear behavior of such electrochemical systems with minimal processing time, advocating for their application in real-time system monitoring, controls and diagnostics.