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Optimization approaches in electric machine design: insights from a bibliometric analysis

2026Artigo científico Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Engenharia Mecânica
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorNascimento, Cecilia Pagnozzi do
dc.contributor.authorSilvério, Ana Cristina
dc.contributor.authorBaptista, Bruno
dc.contributor.authorCarvalho, José Augusto
dc.contributor.authorBazzo, Thiago
dc.contributor.authorFerreira, Ângela P.
dc.date.accessioned2026-03-03T16:06:22Z
dc.date.available2026-03-03T16:06:22Z
dc.date.issued2026
dc.description.abstractOptimizing the design of electric machines is a complex task due to the large number of interrelated geometric and physical parameters that influence performance, efficiency, cost, and sustainability. Numerous deterministic and stochastic optimization techniques have been developed to address these challenges, each presenting distinct advantages and limitations. Deterministic approaches, though computationally efficient, often converge to local minima, while stochastic methods provide broader search capabilities at the expense of higher computational effort. Despite the growing application of optimization in electric machine design, a comprehensive bibliometric overview of this research area is lacking. This study aims to fill that gap by conducting a bibliometric analysis of optimization methods applied to electric machine design. Using data retrieved from the Web of Science (WoS) and analyzed through co-citation mapping with VOSviewer, 246 relevant articles were examined, resulting in a focused sample of 73 key studies. The analysis identifies the most frequently optimized machine types, the main optimization objectives, and the predominant methodologies employed in recent years. By addressing these dimensions, this work provides a conceptual reference framework to guide both academic researchers and industry professionals in selecting appropriate optimization strategies. The results also highlight emerging trends and future research opportunities, contributing to a deeper understanding of the evolution and intellectual structure of optimization in electric machine design.eng
dc.description.sponsorshipThis work was supported by FCT - Fundação para a Ciência e Tecnologia, I.P. by projects: CeDRI, UID/05757/2025 (DOI: https://doi.org/10.54499/UID/05757/2025) and UID/PRR/05757/2025 (DOI: https://doi.org/10.54499/UID/PRR/05757/2025); SusTEC, LA/P/0007/2020 (DOI: https://doi.org/10.54499/LA/P/0007/2020).
dc.identifier.citationNascimento, Cecilia Pagnozzi do; Silvério, Ana Cristina; Baptista, Bruno; Carvalho, José Augusto; Bazzo, Thiago; Ferreira, Ângela P. (2026). Optimization approaches in electric machine design: insights from a bibliometric analysis. Journal of Engineering and Applied Science. ISSN 1110-1903. 73:2, p. 1-25
dc.identifier.doi10.1186/s44147-025-00859-7
dc.identifier.issn1110-1903
dc.identifier.issn2536-9512
dc.identifier.urihttp://hdl.handle.net/10198/35932
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.ispartofJournal of Engineering and Applied Science
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subjectOptimization methods
dc.subjectElectric machines
dc.subjectBibliometrics
dc.subjectCo-citation analysis
dc.subjectDeterministic
dc.subjectStochastic techniques
dc.titleOptimization approaches in electric machine design: insights from a bibliometric analysiseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage25
oaire.citation.issue1
oaire.citation.startPage1
oaire.citation.titleJournal of Engineering and Applied Science
oaire.citation.volume73
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSilvério
person.familyNameCarvalho
person.familyNameFerreira
person.givenNameAna Cristina
person.givenNameJosé Augusto
person.givenNameÂngela P.
person.identifier.ciencia-id7914-E20A-2B3C
person.identifier.ciencia-id1319-7B8B-4901
person.identifier.ciencia-id2211-6787-D936
person.identifier.orcid0000-0002-9228-0622
person.identifier.orcid0000-0002-6074-8112
person.identifier.orcid0000-0002-1912-2556
person.identifier.ridM-8188-2013
person.identifier.scopus-author-id27168377100
person.identifier.scopus-author-id55516840300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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