@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#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00867007v1> a dcat:Dataset ;
    dct:description """
              Energy management of a hybrid vehicle is to develop a strategy that determines at each time the distribution flow of thermal and electricity energy that minimizes the overall consumption of the vehicle. The modelling of hybrid vehicles consumption enables to study energy management as a dynamic optimization problems under evolution constraints. The optimal solution is found when all the driving conditions are known a priori. The optimal control provides a benchmark and is used to assess the performance of embedded strategies. Two strategies based on the optimal optimization theory were created : one predictive, which has been tested on a numerical simulator and another one, based on the dual problem principle, which was successfully embedded on two conventional hybrids vehicles. For plug-in hybrids, their electrical energy capacity and their ability to be recharged from the grid relaxes some constraints on the energy optimization problem. Therefore, in order to minimize the overall emissions of the vehicle, a new strategy was developed to make the most of embedded electric energy. For all hybrid vehicles, the battery is the keystone component, whose aging alters its profitability and energy efficiency. Thus, in order to provide an accurate internal temperature of cells, an observer was designed. This information has been used by a specific strategy which optimizes consumption while preserving the battery from extreme temperatures that are prejudicial to its longevity.
            """ ;
    dct:identifier "tel-00867007" ;
    dct:issued "2026-05-09T13:57:57.485563"^^xsd:dateTime ;
    dct:language "fr" ;
    dct:modified "2026-05-09T13:57:57.485568"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Optimal energy management startegies for hybrid electric vehicules" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00867007v1/resource/460d6467-38ab-4145-950a-3f3fba88b1b0> ;
    dcat:keyword "batterie",
        "battery",
        "data-mining",
        "dynamic-optimisation",
        "dynamic-programming",
        "ecms",
        "energy-management",
        "fusion-dinformations",
        "gestion-denergie",
        "hybrid-vehicles",
        "infoeu-reposemanticsdoctoralthesis",
        "infoinfo-aucomputer-science-csautomatic-control-engineering",
        "lpv-observer",
        "mpc",
        "observateur-lpv",
        "optimisation-dynamique",
        "pontryagin-minimum-principle",
        "principe-du-minimum-de-pontryagin",
        "programmation-dynamique",
        "theses",
        "vehicule-hybride" ;
    dcat:landingPage <https://theses.hal.science/tel-00867007> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-tel-00867007v1/resource/460d6467-38ab-4145-950a-3f3fba88b1b0> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-09T13:57:57.498440"^^xsd:dateTime ;
    dct:modified "2026-05-09T13:57:57.464630"^^xsd:dateTime ;
    dct:title "Optimal energy management startegies for hybrid electric vehicules" ;
    dcat:accessURL <https://theses.hal.science/tel-00867007> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .

<https://theses.hal.science/tel-00867007> a foaf:Document .