Machine learning applications on proton exchange membrane water electrolyzers: A component-level overview

Abstract

Machine Learning (ML) has emerged as a pivotal force in enhancing Proton Exchange Membrane Water Electrolyzer (PEMWE) devices. These devices are critical for transforming renewable electricity into hydrogen, a key clean energy vector. Despite their prospects, the broader implementation of PEMWE is hindered by cost and efficiency barriers. PEMWEs are inherently complex, involving multi-scale processes such as electrochemical reactions, reactant transportation, and thermo-electrical interactions. This complexity has previously limited optimizations to isolated components like electrocatalysts, membrane electrode assemblies (MEAs), Bipolar plates (BPs), and Gas Diffusion Electrodes (GDEs). ML presents a revolutionary pathway to address these obstacles by enabling system-wide optimization. In this paper, we offer an in-depth review of cutting-edge ML applications for improving PEMWE performance and efficiency. ML's ability to process large datasets and identify intricate patterns accelerates the research and development of PEMWEs, thereby reducing costs and boosting efficiency. We describe a variety of algorithms, such as Artificial Neural Networks (ANN), Deep Learning (DL), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM), commonly used in PEMWE applications, highlighting their significance in enhancing PEMWE systems. Additionally, we explore hybrid methods that combine various ML techniques to further improve PEMWE performance and efficiency. The review provides a concise overview and forward-looking perspective on the role of ML in advancing PEMWE technology, marking a significant step towards their cost-effective and scalable deployment.