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

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dc.citedby2
dc.contributor.authorManoharan P.en_US
dc.contributor.authorRavichandran S.en_US
dc.contributor.authorKavitha S.en_US
dc.contributor.authorTengku Hashim T.J.en_US
dc.contributor.authorAlsoud A.R.en_US
dc.contributor.authorSin T.C.en_US
dc.contributor.authorid57191413142en_US
dc.contributor.authorid57219263030en_US
dc.contributor.authorid57850854400en_US
dc.contributor.authorid55241766100en_US
dc.contributor.authorid55711826000en_US
dc.contributor.authorid57212007867en_US
dc.date.accessioned2025-03-03T07:41:35Z
dc.date.available2025-03-03T07:41:35Z
dc.date.issued2024
dc.description.abstractIn 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo20979
dc.identifier.doi10.1038/s41598-024-71223-7
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85203380920
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85203380920&doi=10.1038%2fs41598-024-71223-7&partnerID=40&md5=b793fc371395d14a9c36c1e6a6452f19
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36212
dc.identifier.volume14
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.subjectproton
dc.subjectadaptive behavior
dc.subjectalgorithm
dc.subjectarticle
dc.subjectcontrolled study
dc.subjectdiagnosis
dc.subjectforaging
dc.subjectfuel
dc.subjectgoose
dc.subjectlearning
dc.subjectmathematical model
dc.subjectroot mean squared error
dc.subjectsimulation
dc.titleParameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithmen_US
dc.typeArticleen_US
dspace.entity.typePublication
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