Skip to main navigation Skip to search Skip to main content

Modeling the effects of using gas diffusion layers with patterned wettability for advanced water management in proton exchange membrane fuel cells

  • Jaka Dujc
  • , Antoni Forner-Cuenca
  • , Philip Marmet
  • , Magali Cochet
  • , Roman Vetter
  • , Jürgen O. Schumacher (Corresponding author)
  • , Pierre Boillat

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

We present a macrohomogeneous two-phase model of a proton exchange membrane fuel cell (PEMFC). The model takes into account the mechanical compression of the gas diffusion layer (GDL), the two-phase flow of water, the transport of the gas species, and the electrochemical reaction of the reactant gases. The model was used to simulate the behavior of a PEMFC with a patterned GDL. The results of the reduced model, which considers only the mechanical compression and the two-phase flow, are compared to the experimental ex-situ imbibition data obtained by neutron radiography imaging. The results are in good agreement. Additionally, by using all model features, a simulation of an operating fuel cell has been performed to study the intricate couplings in an operating fuel cell and to examine the patterned GDL effects. The model confirms that the patterned GDL design liberates the predefined domains from liquid water and thus locally increases the oxygen diffusivity.

Original languageEnglish
Article number021001
Number of pages14
JournalJournal of Electrochemical Energy Conversion and Storage
Volume15
Issue number2
DOIs
Publication statusPublished - 1 May 2018
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Fingerprint

Dive into the research topics of 'Modeling the effects of using gas diffusion layers with patterned wettability for advanced water management in proton exchange membrane fuel cells'. Together they form a unique fingerprint.

Cite this