LAPSE:2023.26426
Published Article
LAPSE:2023.26426
Faraday’s Efficiency Modeling of a Proton Exchange Membrane Electrolyzer Based on Experimental Data
April 3, 2023
In electrolyzers, Faraday’s efficiency is a relevant parameter to assess the amount of hydrogen generated according to the input energy and energy efficiency. Faraday’s efficiency expresses the faradaic losses due to the gas crossover current. The thickness of the membrane and operating conditions (i.e., temperature, gas pressure) may affect the Faraday’s efficiency. The developed models in the literature are mainly focused on alkaline electrolyzers and based on the current and temperature change. However, the modeling of the effect of gas pressure on Faraday’s efficiency remains a major concern. In proton exchange membrane (PEM) electrolyzers, the thickness of the used membranes is very thin, enabling decreasing ohmic losses and the membrane to operate at high pressure because of its high mechanical resistance. Nowadays, high-pressure hydrogen production is mandatory to make its storage easier and to avoid the use of an external compressor. However, when increasing the hydrogen pressure, the hydrogen crossover currents rise, particularly at low current densities. Therefore, faradaic losses due to the hydrogen crossover increase. In this article, experiments are performed on a commercial PEM electrolyzer to investigate Faraday’s efficiency based on the current and hydrogen pressure change. The obtained results have allowed modeling the effects of Faraday’s efficiency by a simple empirical model valid for the studied PEM electrolyzer stack. The comparison between the experiments and the model shows very good accuracy in replicating Faraday’s efficiency.
Keywords
crossover current, Energy Efficiency, faradaic losses, Faraday’s efficiency, gas pressure, hydrogen flow rate, Modelling, PEM electrolyzer
Suggested Citation
Yodwong B, Guilbert D, Phattanasak M, Kaewmanee W, Hinaje M, Vitale G. Faraday’s Efficiency Modeling of a Proton Exchange Membrane Electrolyzer Based on Experimental Data. (2023). LAPSE:2023.26426
Author Affiliations
Yodwong B: Group of Research in Electrical Engineering of Nancy (GREEN), Université de Lorraine, GREEN, F-54000 Nancy, France; Department of Teacher Training in Electrical Engineering, King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok 10800, [ORCID]
Guilbert D: Group of Research in Electrical Engineering of Nancy (GREEN), Université de Lorraine, GREEN, F-54000 Nancy, France [ORCID]
Phattanasak M: Department of Teacher Training in Electrical Engineering, King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok 10800, Thailand [ORCID]
Kaewmanee W: Department of Teacher Training in Electrical Engineering, King Mongkut’s University of Technology North Bangkok (KMUTNB), Bangkok 10800, Thailand
Hinaje M: Group of Research in Electrical Engineering of Nancy (GREEN), Université de Lorraine, GREEN, F-54000 Nancy, France
Vitale G: Institute for High Performance Computing and Networking (ICAR), National Research Council of Italy, Unit of Palermo, 90146 Palermo, Italy [ORCID]
Journal Name
Energies
Volume
13
Issue
18
Article Number
E4792
Year
2020
Publication Date
2020-09-14
Published Version
ISSN
1996-1073
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PII: en13184792, Publication Type: Journal Article
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doi:10.3390/en13184792
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