Data-driven modeling of polymer electrolyte fuel cells: Towards predictive analytics with explainable artificial intelligence

Abstract

• Developed a comprehensive data-driven framework using advanced machine learning models to accurately predict polarization behavior of polymer electrolyte fuel cells. • Integrated explainable AI techniques such as Gini importance or SHAP to reveal how key operational and design parameters especially voltage, relative humidity, platinum loading, and ionomer-to-carbon ratio influence performance. • Identified voltage is the top predictor, followed by relative humidity, platinum loading, and ionomer-to-carbon ratio. • Model-based support for optimization of fabrication, real-time control, and durability improvements. • Providing actionable insights for optimization of the fabrication and operation of polymer electrolyte fuel cells. Polymer electrolyte fuel cells will be an essential technology of the emerging hydrogen economy. However, optimizing their cost and performance necessitates understanding of how different parameters affect their operation. This optimization problem involves numerous interrelated design and operational parameters. However, developing the required understanding through experimental studies alone would be inefficient. Physical modelling is a much-needed complement to experiment but is constrained by simplifying assumptions that diminish the models' predictive capabilities. As a supplement to experiment and physical modelling, we employ a data-based assessment that leverages machine learning techniques to support and enhance decision-making. We first evaluate the predictive accuracy of various machine learning models, including artificial neural networks, to predict the polarization behavior of polymer electrolyte fuel cells, harnessing an extensive experimental dataset. We then apply explainable artificial intelligence techniques, including Gini feature importance and Shapley additive explanations value analyses, to understand how these models incorporate data into the prediction process. Probabilistic analyses can help identify relationships between predictions and feature values. We demonstrate that insights derived from Shapley additive explanations value analysis are consistent with literature data on the thermodynamics and kinetics of relevant electrochemical reaction and transport processes. Our study highlights the potential of interpretable and explainable tools to offer a holistic analysis of the impacts of various interrelated operational and design parameters on the performance of the fuel cell. In the future, such explainable tools could help identify gaps in experimental data and pinpoint research priorities.

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