@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

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              The work presented in this thesis aims at the modelling and optimisation of electrical machine for an automotive application.The first part shows the constraints required to electric/hybrid automotive specifications. A clustering method which allows to reduce evaluations number of the operating points is described. Next, an optimal pre-sizing of the machine is presented and designed in order to respect this optimal pre-sizing.In what follows an accurate and fast analytical electromagnetic modelling of the machine is performed. Well, the analytical modelling developed is related to a genetic algorithm. Two solutions of permanent magnet synchronous machines (PMSM) designed to automotive application are finally showed and analysed.
            """ ;
    dct:identifier "NNT: 2013SUPL0012" ;
    dct:issued "2026-05-05T22:05:53.458691"^^xsd:dateTime ;
    dct:language "fr" ;
    dct:modified "2026-05-05T22:05:53.458696"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Analytical modelling and design optimisation of an electric machine for a mild hybrid electric vehicle" ;
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    dcat:keyword "analytical-modelling",
        "application-automobile",
        "automotive-application",
        "infoeu-reposemanticsdoctoralthesis",
        "machine-synchrone-a-aimants-permanent",
        "modelisation-analytique",
        "optimisation",
        "permanent-magnet-synchronous-machine",
        "spiotherengineering-sciences-physicsother",
        "theses" ;
    dcat:landingPage <https://theses.hal.science/tel-00963620> .

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    dct:issued "2026-05-05T22:05:53.480744"^^xsd:dateTime ;
    dct:modified "2026-05-05T22:05:53.445491"^^xsd:dateTime ;
    dct:title "Analytical modelling and design optimisation of an electric machine for a mild hybrid electric vehicle" ;
    dcat:accessURL <https://theses.hal.science/tel-00963620> .

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