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

Tabbi Wilberforce; Abdul Ghani Olabi

doi:10.3390/su12124952

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

Tabbi Wilberforce^*
, Abdul Ghani Olabi

^*Corresponding author for this work

Mechanical, Biomedical & Design Engineering

University of Sharjah

Research output: Contribution to journal › Article › peer-review

39 Citations (Scopus)

14 Downloads (Pure)

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.

Original language	English
Article number	4952
Journal	Sustainability (Switzerland)
Volume	12
Issue number	12
DOIs	https://doi.org/10.3390/su12124952
Publication status	Published - 17 Jun 2020

Bibliographical note

© 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/)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Ambient conditions
Flow rate
Hydrogen
Machine learning
Pressure

Access to Document

10.3390/su12124952Licence: CC BY 3.0

Performance Prediction of Proton Exchange Membrane Fuel Cells (PEMFC)
© 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/)
Final published version, 2.75 MBLicence: CC BY 3.0

Cite this

@article{6eac468311ec4a119b440960306d2094,

title = "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.",

keywords = "Ambient conditions, Flow rate, Hydrogen, Machine learning, Pressure",

author = "Tabbi Wilberforce and Olabi, \{Abdul Ghani\}",

note = "{\textcopyright} 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/)",

year = "2020",

month = jun,

day = "17",

doi = "10.3390/su12124952",

language = "English",

volume = "12",

journal = "Sustainability (Switzerland)",

issn = "2071-1050",

publisher = "Multidisciplinary Digital Publishing Institute",

number = "12",

}

TY - JOUR

T1 - Performance prediction of proton exchange membrane fuel cells (PEMFC) using adaptive neuro inference system (ANFIS)

AU - Wilberforce, Tabbi

AU - Olabi, Abdul Ghani

N1 - © 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/)

PY - 2020/6/17

Y1 - 2020/6/17

N2 - 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.

AB - 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.

KW - Ambient conditions

KW - Flow rate

KW - Hydrogen

KW - Machine learning

KW - Pressure

UR - https://www.scopus.com/pages/publications/85088781940

UR - https://www.mdpi.com/2071-1050/12/12/4952

U2 - 10.3390/su12124952

DO - 10.3390/su12124952

M3 - Article

AN - SCOPUS:85088781940

SN - 2071-1050

VL - 12

JO - Sustainability (Switzerland)

JF - Sustainability (Switzerland)

IS - 12

M1 - 4952

ER -

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

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this