Abstract
Polymer-electrolyte fuel cells are a promising energy-conversion technology. Over the last several decades significant progress has been made in increasing their performance and durability, of which continuum-level modeling of the transport processes has played an integral part. In this review, we examine the state-of-the-art modeling approaches, with a goal of elucidating the knowledge gaps and needs going forward in the field. In particular, the focus is on multiphase flow, especially in terms of understanding interactions at interfaces, and catalyst layers with a focus on the impacts of ionomer thin-films and multiscale phenomena. Overall, we highlight where there is consensus in terms of modeling approaches as well as opportunities for further improvement and clarification, including identification of several critical areas for future research.
Highlights
Some background information is provided in order to orient the reader for the more detailed discussions below concerning multiphase-flow and catalyst-layer modeling and phenomena
Ion, and mass transport are all strongly influenced by water transport and early work in collecting these losses for a comprehensive model was limited to operating conditions where the relative humidity (RH) was less than 100% or the impact of liquid-water accumulation was not accounted for explicitly.[27]
This critical review has focused on examining these advancements as they relate to modeling of transport phenomena in polymer-electrolyte fuel cells (PEFCs)
Summary
A Critical Review of Modeling Transport Phenomena in Polymer-Electrolyte Fuel Cells. During the operation of a PEFC, many interrelated and complex phenomena occur These processes include mass and heat transfer, electrochemical reactions, and ionic and electronic transport. While modeling of the transport and related phenomena has progressed greatly, there are still some critical areas that need attention These areas include modeling the catalyst layer and multiphase phenomena in the PEFC porous media. The section focuses on future perspectives including interactions between modeling and experiments, modeling variability, open-source modeling, and an overall summary of the article
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