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  2. School of Engineering
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dc.contributor.authorDe Martinis, Valerio-
dc.date.accessioned2023-03-27T13:25:35Z-
dc.date.available2023-03-27T13:25:35Z-
dc.date.issued2022-08-22-
dc.identifier.issn2753-3239de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/27456-
dc.description.abstractThe on-board collection of data related to train operation enables a better calibration of the train resistances, which are fundamental for the elaboration of optimized train control solutions. Here, the possibility to implement a calibration of train resistances models based on power measurements is investigated. For this purpose, a huge dataset of train runs data collected by a Swiss train operator has been analysed. The train runs of three train types, operating on three different lines, have been extracted and used for the calibration phase. The calibration model is formulated as an optimization problem for parameters fitting and a Global Optimization Multi Start approach is used for finding the suboptimal solution. The performances of the model are therefore discussed together with possible further investigation.de_CH
dc.language.isoende_CH
dc.publisherCivil-Comp Pressde_CH
dc.relation.ispartofseriesCivil-Comp Conferencesde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectCalibrationde_CH
dc.subjectResistance parameterde_CH
dc.subjectOptimizationde_CH
dc.subjectPower measurementde_CH
dc.subjectTrainde_CH
dc.subject.ddc380: Verkehrde_CH
dc.titleEnhancing the calibration of train resistance parameters with power measurementsde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.4203/ccc.1.31.20de_CH
zhaw.conference.detailsFifth International Conference on Railway Technology, Montpellier, France, 22-25 August 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.parentwork.editorPombo, J.-
zhaw.publication.statuspublishedVersionde_CH
zhaw.series.number1de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.title.proceedingsProceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenancede_CH
zhaw.webfeedTransport und Mobilitätde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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De Martinis, V. (2022). Enhancing the calibration of train resistance parameters with power measurements [Conference paper]. In J. Pombo (Ed.), Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance. Civil-Comp Press. https://doi.org/10.4203/ccc.1.31.20
De Martinis, V. (2022) ‘Enhancing the calibration of train resistance parameters with power measurements’, in J. Pombo (ed.) Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance. Civil-Comp Press. Available at: https://doi.org/10.4203/ccc.1.31.20.
V. De Martinis, “Enhancing the calibration of train resistance parameters with power measurements,” in Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance, Aug. 2022. doi: 10.4203/ccc.1.31.20.
DE MARTINIS, Valerio, 2022. Enhancing the calibration of train resistance parameters with power measurements. In: J. POMBO (Hrsg.), Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance. Conference paper. Civil-Comp Press. 22 August 2022
De Martinis, Valerio. 2022. “Enhancing the Calibration of Train Resistance Parameters with Power Measurements.” Conference paper. In Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance, edited by J. Pombo. Civil-Comp Press. https://doi.org/10.4203/ccc.1.31.20.
De Martinis, Valerio. “Enhancing the Calibration of Train Resistance Parameters with Power Measurements.” Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance, edited by J. Pombo, Civil-Comp Press, 2022, https://doi.org/10.4203/ccc.1.31.20.


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