Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC) Using Adaptive Neuro Inference System (ANFIS)
1
Mechanical Engineering and Design, Aston University, School of Engineering and Applied Science, Aston Triangle, Birmingham B4 7ET, UK
2
Department of Sustainable and Renewable Energy Engineering, University of Sharjah, P.O. Box 27272, Sharjah, UAE
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(12), 4952; https://doi.org/10.3390/su12124952
Received: 20 May 2020 / Revised: 13 June 2020 / Accepted: 15 June 2020 / Published: 17 June 2020
(This article belongs to the Special Issue Energy Storage Materials in Sustainability)
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.
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Keywords:
machine learning; ambient conditions; flow rate; pressure; hydrogen
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MDPI and ACS Style
Wilberforce, T.; Olabi, A.G. Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC) Using Adaptive Neuro Inference System (ANFIS). Sustainability 2020, 12, 4952. https://doi.org/10.3390/su12124952
AMA Style
Wilberforce T, Olabi AG. Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC) Using Adaptive Neuro Inference System (ANFIS). Sustainability. 2020; 12(12):4952. https://doi.org/10.3390/su12124952
Chicago/Turabian StyleWilberforce, Tabbi, and Abdul G. Olabi 2020. "Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC) Using Adaptive Neuro Inference System (ANFIS)" Sustainability 12, no. 12: 4952. https://doi.org/10.3390/su12124952
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