Finding best operational conditions of PEM fuel cell using adaptive neuro-fuzzy inference system and metaheuristics

Hegazy Rezk*, Tabbi Wilberforce, Enas Taha Sayed, Ahmed N.M. Alahmadi, A. G. Olabi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The optimum output power of the proton exchange membrane fuel cell (PEMFC) is dependent on operational conditions such as fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate. Therefore, the aim of this paper is to enhance performance of PEMFC by identifying optimal operating parameters of PEMFC. The proposed strategy includes both modelling and optimization stages. An adaptive network-based fuzzy inference system (ANFIS) is utilized in creating the model based on experimental datasets. Whereas, the grey wolf optimizer (GWO) is used to identify the best values of fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power PEMFC. The obtained results demonstrated the superiority of the integration between ANIFS based modelling and GWO. Regarding the modelling accuracy, The RMSE values are 0.017 as well as 0.0262 respectively for treating and testing phases. The coefficient of determination values is 0.9921 as well as 0.9622 respectively for treating coupled with testing phases. The optimal parameters are 1.0 bar, 0.8 bar, 117.03 mL/min, 150.0 mL/min respectively fuel pressure, oxidant pressure, fuel flow rate, and oxidant flow rate corresponding to maximum power of PEMFC. Thanks to the integration between ANFIS-based modelling and GWO, the output power of PEMFC has been increased from 0.587 W using experimental work to 0.92 W.

Original languageEnglish
Pages (from-to)6181-6190
Number of pages10
JournalEnergy Reports
Volume8
Early online date12 May 2022
DOIs
Publication statusE-pub ahead of print - 12 May 2022

Bibliographical note

© 2022 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license 4.0

Funding Information:
The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project number ( IF-PSAU-2021/01/17835 ).

Keywords

  • ANFIS modelling
  • Fuel cells
  • Grey wolf optimizer

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