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Efficiency optimal design of interior permanent magnet machine for scooter

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dc.contributor.author권병일-
dc.date.accessioned2021-06-23T02:24:46Z-
dc.date.available2021-06-23T02:24:46Z-
dc.date.created2021-02-18-
dc.date.issued2013-10-
dc.identifier.urihttps://scholarworks.bwise.kr/erica/handle/2021.sw.erica/26756-
dc.description.abstractIn this paper, a shape optimization for efficiency improvement of an interior permanent magnet (IPM) machine is presented for scooter. Usually scooter machine is limited to the space, so the optimization design for performance improvement with a limited volume and better features is necessary. The optimal design process by using the Kriging method combined with latin hypercube sampling (LHS) and genetic algorithm (GA) is utilized. As a result, the output power and efficiency have been improved effectively. To make more accurate prediction of performance, some other characteristics are also performed with the aid of a time-stepping 2D finite element method (FEM). © 2013 IEEE.-
dc.publisherIEEE Computer Society-
dc.titleEfficiency optimal design of interior permanent magnet machine for scooter-
dc.typeArticle-
dc.contributor.affiliatedAuthor권병일-
dc.identifier.bibliographicCitationInternational Conference on Electrical Machines and Systems 2013, pp.949 - 953-
dc.relation.isPartOfInternational Conference on Electrical Machines and Systems 2013-
dc.citation.titleInternational Conference on Electrical Machines and Systems 2013-
dc.citation.startPage949-
dc.citation.endPage953-
dc.type.rimsART-
dc.description.journalClass3-
dc.subject.keywordAuthorOptimal systems-
dc.subject.keywordAuthorOptimization design-
dc.subject.keywordAuthorShape optimization-
dc.subject.keywordAuthorPermanent magnets-
dc.subject.keywordAuthorElectric machinery-
dc.subject.keywordAuthorMagnets-
dc.subject.keywordAuthorOutput power and efficiencies-
dc.subject.keywordAuthorKriging methods-
dc.subject.keywordAuthorGenetic algorithms-
dc.subject.keywordAuthorVehicles-
dc.subject.keywordAuthorFinite element method-
dc.subject.keywordAuthorAccurate prediction-
dc.subject.keywordAuthorLatin hypercube sampling-
dc.subject.keywordAuthorE-
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