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dc.contributor.authorŠevčík, Jakub
dc.contributor.authorŠmídl, Václav
dc.contributor.authorVotava, Martin
dc.date.accessioned2020-03-30T10:00:26Z-
dc.date.available2020-03-30T10:00:26Z-
dc.date.issued2019
dc.identifier.citationŠEVČÍK, J., ŠMÍDL, V., VOTAVA, M. Identification of thermal model of power module using expectation-maximization algorithm. In: Proceedings : IECON 2019 : 45th Annual Conference of the IEEE Industrial Electronics Society. Piscataway: IEEE, 2019. s. 1-7. ISBN 978-1-72814-878-6.en
dc.identifier.isbn978-1-72814-878-6
dc.identifier.urihttp://hdl.handle.net/11025/36803
dc.description.abstractPrediction of junction temperatures in power semiconductor modules is essential to improve reliability of the device and prevent module failures due to thermal stress. Lumped parameter network is a popular approach for temperature modeling. Calibration of the thermal model is based on thermal measurements of the junction temperatures that are difficult to obtain. We aim to combine the knowledge of internal model structure and as little measurements as possible. Specifically, we use a state space thermal model with structure determined by the module layout, and propose to use the Expectation-Maximization algorithm from that can utilize data from different incomplete experiments. The identification procedure is introduced in detail in this paper and the applicability of the proposed approach is demonstrated on simulated and experimental data.en
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesProceedings : IECON 2019 : 45th Annual Conference of the IEEE Industrial Electronics Societyen
dc.rightsPlný text je přístupný v rámci univerzity přihlášeným uživatelům.cs
dc.rights© IEEEen
dc.titleIdentification of thermal model of power module using expectation-maximization algorithmen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessrestrictedAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedthermal networken
dc.subject.translatedstate space modelen
dc.subject.translatedsystem identificationen
dc.subject.translatedexpectation-maximization algorithmen
dc.subject.translatedpower semiconductor moduleen
dc.identifier.doi10.1109/IECON.2019.8927553
dc.type.statusPeer-revieweden
dc.identifier.obd43927141
dc.project.IDEF18_069/0009855/Elektrotechnické technologie s vysokým podílem vestavěné inteligencecs
dc.project.IDSGS-2018-009/Výzkum a vývoj perspektivních technologií v elektrických pohonech a strojích IIIcs
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Konferenční příspěvky / Conference papers (RICE)
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