Prediction and analysis of the cathode catalyst layer performance of proton exchange membrane fuel cells using artificial neural network and statistical methods

N. Khajeh-Hosseini-Dalasm, S. Ahadian, K. Fushinobu, K. Okazaki, Y. Kawazoe

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

A mathematical model was developed to investigate the cathode catalyst layer (CL) performance of a proton exchange membrane fuel cell (PEMFC). A numerous parameters influencing the cathode CL performance are implemented into the CL agglomerate model, namely, saturation and eight structural parameters, i.e., ionomer film thickness covering the agglomerate, agglomerate radius, platinum and carbon loading, membrane content, gas diffusion layer penetration content and CL thickness. For the first time, an artificial neural network (ANN) approach along with statistical methods were employed for modeling, prediction, and analysis of the CL performance, which is denoted by activation overpotential. The ANN was constructed to build the relationship between the named parameters and activation overpotential. Statistical analysis, namely, analysis of means (ANOM) and analysis of variance (ANOVA) were done on the data obtained by the trained neural network and resulted in the sensitivity factors of structural parameters and their mutual combinations as well as the best performance.

Original languageEnglish
Pages (from-to)3750-3756
Number of pages7
JournalJournal of Power Sources
Volume196
Issue number8
DOIs
Publication statusPublished - 2011 Apr 15

Keywords

  • Agglomerate model
  • Analysis of means
  • Analysis of variance
  • Artificial neural network
  • Catalyst layer
  • 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

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