Performance prediction of proton exchange membrane fuel cells (PEMFC) using adaptive neuro inference system (ANFIS)

Abstract

This investigation explored the performance of PEMFC for varying ambient conditions with the aid of an adaptive neuro-fuzzy inference system. The experimental data obtained from the laboratory were initially trained using both the input and output parameters. The model that was trained was then evaluated using an independent variable. The training and testing of the model were then utilized in the prediction of the cell-characteristic performance. The model exhibited a perfect correlation between the predicted and experimental data, and this stipulates that ANFIS can predict characteristic behavior of fuel cell performance with very high accuracy.

Publication DOI: https://doi.org/10.3390/su12124952
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
Additional Information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
Uncontrolled Keywords: Ambient conditions,Flow rate,Hydrogen,Machine learning,Pressure,Geography, Planning and Development,Renewable Energy, Sustainability and the Environment,Management, Monitoring, Policy and Law
Publication ISSN: 2071-1050
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://www.mdp ... 1050/12/12/4952 (Publisher URL)
PURE Output Type: Article
Published Date: 2020-06-17
Accepted Date: 2020-06-15
Authors: Wilberforce, Tabbi (ORCID Profile 0000-0003-1250-1745)
Olabi, Abdul Ghani

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