Effective diffusivity of gas diffusion layer in proton exchange membrane fuel cells

Dahua Shou, Jintu Fan, Feng Ding

    Research output: Journal article publicationJournal articleAcademic researchpeer-review

    38 Citations (Scopus)


    In gas diffusion layers (GDLs) of proton exchange membrane fuel cells (PEMFCs), effective gas diffusivity is a key parameter to be determined and engineered. Existing theoretical models of effective diffusivity are limited to one-dimensional (1D) regular fiber arrays. Numerical simulations were carried out to simulate gas diffusion through more realistic fibrous materials like GDLs, in which fibers are randomly distributed in a two-dimensional (2D) plane or three-dimensional (3D) space, but they could not fully reveal the underlying mechanisms. In this paper, we propose an analytical model to predict the effective diffusivities of 1D, 2D and 3D randomly distributed fiber assembles. The present model is established by extending the model of 1D regular fiber alignments to 1D random fiber arrangements through Voronoi Tessellation method, and using the 1D local diffusivities to determine the 2D and 3D diffusivities based on mixing rules. The predicted effective diffusivities agree well with experimental results and numerical data. With the new model, the influences of porosity, fiber distribution, and fiber orientation are analyzed in this study.

    Original languageEnglish
    Pages (from-to)179-186
    Number of pages8
    JournalJournal of Power Sources
    Publication statusPublished - 1 Mar 2013


    • Analytical model
    • Effective diffusivity
    • Fibrous media
    • Gas diffusion layers
    • Proton exchange membrane fuel cell

    ASJC Scopus subject areas

    • Renewable Energy, Sustainability and the Environment
    • Energy Engineering and Power Technology
    • Physical and Theoretical Chemistry
    • Electrical and Electronic Engineering


    Dive into the research topics of 'Effective diffusivity of gas diffusion layer in proton exchange membrane fuel cells'. Together they form a unique fingerprint.

    Cite this