Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/21600
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dc.contributor.authorCvetkovski, Gogaen_US
dc.contributor.authorPetkovska, Lidijaen_US
dc.date.accessioned2022-07-26T12:43:10Z-
dc.date.available2022-07-26T12:43:10Z-
dc.date.issued2016-06-01-
dc.identifier.citationGoga Cvetkovski, Lidija Petkovska, (2016) Genetic Algorithm as a Tool for Multi-Objective Optimization of Permanent Magnet Disc Motor, Archives of Electrical Engineering, Polish Academy of Sciences, Warsaw, Vol. 65(2), pp. 285-294, 2016.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12188/21600-
dc.description.abstractThe analysed permanent magnet disc motor (PMDM) is used for direct wheel drive in an electric vehicle. Therefore there are several objectives that could be tackled in the design procedure, such as an increased efficiency, reduced iron weight, reduced copper weight or reduced weight of the permanent magnets (reduced rotor weight). In this paper the optimal design of PMDM using a multi-objective genetic algorithm optimisation procedure is performed. A comparative analysis of the optimal motor solution and its parameters in relation to the prototype is presented.en_US
dc.language.isoenen_US
dc.publisherWalter de Gruyter GmbHen_US
dc.relation.ispartofArchives of Electrical Engineeringen_US
dc.subjectdesign optimisationen_US
dc.subjectelectric vehicleen_US
dc.subjectgenetic algorithmen_US
dc.subjectmulti-objective optimisationen_US
dc.subjectpermanent magnet disc motoren_US
dc.titleGenetic algorithm as a tool for multi-objective optimization of permanent magnet disc motoren_US
dc.typeJournal Articleen_US
dc.identifier.doi10.1515/aee-2016-0020-
dc.identifier.urlhttp://content.sciendo.com/view/journals/aee/65/2/article-p285.xml-
dc.identifier.urlhttps://www.degruyter.com/view/j/aee.2016.65.issue-2/aee-2016-0020/aee-2016-0020.pdf-
dc.identifier.volume65-
dc.identifier.issue2-
dc.identifier.fpage285-
dc.identifier.lpage294-
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:Faculty of Electrical Engineering and Information Technologies: Journal Articles
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