Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions

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dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorVepsäläinen, Jarien_US
dc.contributor.authorOtto, Kevinen_US
dc.contributor.authorLajunen, Anttien_US
dc.contributor.authorTammi, Karien_US
dc.contributor.departmentDepartment of Mechanical Engineeringen
dc.contributor.groupauthorAdvanced Manufacturing and Materialsen
dc.date.accessioned2019-01-30T15:06:57Z
dc.date.available2019-01-30T15:06:57Z
dc.date.issued2019-02-15en_US
dc.description.abstractThe uncertainty of operating conditions such as weather and payload cause variations in the energy demand of electric city buses. Uncertain variation in energy demand is a challenge in the design of charging systems and on-board energy storages. To predict the energy demand, a computationally efficient model is required for real-time applications. We present a novel approach to predict energy demand variation with a wide range of uncertain factors. A factor identification is carried out to recognize the range of variation in the operating conditions. A computationally efficient surrogate model is generated based on a previously developed numerical simulation model. The surrogate model is shown to be 10 000 times faster than the numerical model. The surrogate model output corresponds with the numerical model with less than 1% error. The energy demand of the surrogate model varied from 0.43 to 2.30 kWh/km, which is realistic in comparison to previous studies. Successful sensitivity analysis of the surrogate model revealed the most crucial factors. Uncertainty in temperature, rolling resistance and payload contributed most to the variation in energy demand. Variation in these factors should be taken into account when predicting energy consumption and while planning schedules for a bus network. (C) 2018 The Authors. Published by Elsevier Ltd.en
dc.description.versionPeer revieweden
dc.format.extent11
dc.format.extent433-443
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationVepsäläinen, J, Otto, K, Lajunen, A & Tammi, K 2019, ' Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions ', Energy, vol. 169, pp. 433-443 . https://doi.org/10.1016/j.energy.2018.12.064en
dc.identifier.doi10.1016/j.energy.2018.12.064en_US
dc.identifier.issn0360-5442
dc.identifier.issn1873-6785
dc.identifier.otherPURE UUID: 07c9fc83-ce97-4095-9a83-34474e3d331cen_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/07c9fc83-ce97-4095-9a83-34474e3d331cen_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85059337854&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/31175510/ENG_Veps_l_inen_et_al_Computationally_efficient_model_Energy.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/36216
dc.identifier.urnURN:NBN:fi:aalto-201901301386
dc.language.isoenen
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.relation.ispartofseriesEnergyen
dc.relation.ispartofseriesVolume 169en
dc.rightsopenAccessen
dc.subject.keywordElectric busen_US
dc.subject.keywordEnergy demanden_US
dc.subject.keywordSensitivity analysisen_US
dc.subject.keywordSimulationen_US
dc.subject.keywordSurrogate modelingen_US
dc.subject.keywordUncertaintyen_US
dc.subject.keywordBATTERYen_US
dc.subject.keywordSYSTEMen_US
dc.subject.keywordDESIGNen_US
dc.subject.keywordBEHAVIORen_US
dc.subject.keywordGLOBAL SENSITIVITY-ANALYSISen_US
dc.subject.keywordVEHICLESen_US
dc.subject.keywordUNCERTAINTYen_US
dc.subject.keywordCONSUMPTIONen_US
dc.subject.keywordHYBRIDen_US
dc.subject.keywordLIFEen_US
dc.titleComputationally efficient model for energy demand prediction of electric city bus in varying operating conditionsen
dc.typeA1 Alkuperäisartikkeli tieteellisessä aikakauslehdessäfi
dc.type.versionpublishedVersion
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