Staff View: Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., ?1, ?2, ?3, ?4, RC, ?, and b. The fuel�cells�(FCs) involve multiple variable quantities with complex non-linear behaviours, demanding accurate modelling to ensure op...

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Main Authors:	Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.
Other Authors:	57191413142
Format:	Article
Published:	Nature Research 2025
Subjects:	proton adaptive behavior algorithm article controlled study diagnosis foraging fuel goose learning mathematical model root mean squared error simulation
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spelling	my.uniten.dspace-362122025-03-03T15:41:35Z Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm Manoharan P. Ravichandran S. Kavitha S. Tengku Hashim T.J. Alsoud A.R. Sin T.C. 57191413142 57219263030 57850854400 55241766100 55711826000 57212007867 proton adaptive behavior algorithm article controlled study diagnosis foraging fuel goose learning mathematical model root mean squared error simulation In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., ?1, ?2, ?3, ?4, RC, ?, and b. The fuel�cells�(FCs) involve multiple variable quantities with complex non-linear behaviours, demanding accurate modelling to ensure optimal operation. An accurate model of these FCs is essential to evaluate their performance accurately. Furthermore, the design of the FCs significantly impacts simulation studies, which are crucial for various technological applications. This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. In order to validate the proposed algorithm, a number of experiments using various datasets were conducted and compared the outcomes with different�state-of-the-art algorithms. The outcomes indicate that the proposed GOOSE algorithm not only produced promising results but also exhibited superior performance in comparison to other similar algorithms. This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. The proposed strategy facilitates the development of more accurate and effective advancements in the utilization of FCs. ? The Author(s) 2024. Final 2025-03-03T07:41:35Z 2025-03-03T07:41:35Z 2024 Article 10.1038/s41598-024-71223-7 2-s2.0-85203380920 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203380920&doi=10.1038%2fs41598-024-71223-7&partnerID=40&md5=b793fc371395d14a9c36c1e6a6452f19 https://irepository.uniten.edu.my/handle/123456789/36212 14 1 20979 All Open Access; Gold Open Access Nature Research Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	proton adaptive behavior algorithm article controlled study diagnosis foraging fuel goose learning mathematical model root mean squared error simulation
spellingShingle	proton adaptive behavior algorithm article controlled study diagnosis foraging fuel goose learning mathematical model root mean squared error simulation Manoharan P. Ravichandran S. Kavitha S. Tengku Hashim T.J. Alsoud A.R. Sin T.C. Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
description	In this paper, a new method is designed to effectively determine the parameters of proton exchange membrane fuel cells (PEMFCs), i.e., ?1, ?2, ?3, ?4, RC, ?, and b. The fuel�cells�(FCs) involve multiple variable quantities with complex non-linear behaviours, demanding accurate modelling to ensure optimal operation. An accurate model of these FCs is essential to evaluate their performance accurately. Furthermore, the design of the FCs significantly impacts simulation studies, which are crucial for various technological applications. This study proposed an improved parameter estimation procedure for PEMFCs by using the GOOSE algorithm, which was inspired by the adaptive behaviours found in geese during their relaxing and foraging times. The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. In order to validate the proposed algorithm, a number of experiments using various datasets were conducted and compared the outcomes with different�state-of-the-art algorithms. The outcomes indicate that the proposed GOOSE algorithm not only produced promising results but also exhibited superior performance in comparison to other similar algorithms. This approach demonstrates the ability of the GOOSE algorithm to simulate complex systems and enhances the robustness and adaptability of the simulation tool by integrating essential behaviours into the computational framework. The proposed strategy facilitates the development of more accurate and effective advancements in the utilization of FCs. ? The Author(s) 2024.
author2	57191413142
author_facet	57191413142 Manoharan P. Ravichandran S. Kavitha S. Tengku Hashim T.J. Alsoud A.R. Sin T.C.
format	Article
author	Manoharan P. Ravichandran S. Kavitha S. Tengku Hashim T.J. Alsoud A.R. Sin T.C.
author_sort	Manoharan P.
title	Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
title_short	Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
title_full	Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
title_fullStr	Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
title_full_unstemmed	Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm
title_sort	parameter characterization of pem fuel cell mathematical models using an orthogonal learning-based goose algorithm
publisher	Nature Research
publishDate	2025
_version_	1825816055242555392
score	13.244413

Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

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