Modeling polymer electrolyte fuel cells: A high precision analysis

S. Zhang, U. Reimer, S.B. Beale, W. Lehnert and D. Stolten

Applied Energy, 2019, vol. 233-234, 1094-1103

Abstract: In this paper, a computational model is used to study the distributions of several key parameters and the performance of a fuel cell with an active area of 200 cm2. The results reveal that the model is capable of predicting the overall behavior in good agreement with experimental data and with superior resolution. Polarization curves are compared and cell voltage prediction deviations are within 43mV of experimental values. The predicted current density distribution is very close to both the experimentally measured results and a volume-average approach based on rate equations. Local variations of current density, oxygen, and water mole fraction change significantly from under-rib regions to under-channel regions. The serpentine type flow path leads to greater pressure gradients, but is beneficial to gas bypassing through the gas diffusion layers. This type of flow path helps to redistribute the species and current density distributions. Never before has it been possible to construct computational models capable of predicting fine-scale details in local current density; details which were not captured neither by previous models nor by present-day experiments.

Keywords: Polymer electrolyte fuel cell; Modeling; Mass transport; Current density distribution (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261918315769
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:233-234:y:2019:i::p:1094-1103

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2018.10.026

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().