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dc.contributor.authorWang, Yongxing
dc.contributor.authorLu, Chaoru
dc.contributor.authorBi, Jun
dc.contributor.authorSai, Qiuyue
dc.contributor.authorZhang, Yongzhi
dc.date.accessioned2022-03-04T08:09:51Z
dc.date.available2022-03-04T08:09:51Z
dc.date.created2021-04-29T11:01:38Z
dc.date.issued2021
dc.identifier.citationIET Intelligent Transport Systems. 2021, 15 (6), 824-836.en_US
dc.identifier.issn1751-956X
dc.identifier.urihttps://hdl.handle.net/11250/2982976
dc.description.abstractBattery electric buses (BEBs) have been regarded as effective options to address the congestion and pollution problems in the field of urban transportation. However, since the limited driving range of BEBs brings challenges for their promotion, the accurate estimation of the driving range with limited available information has become a critical issue for public transport operators. The real-world data collected from 50 BEBs operated in two different cities is used to develop the driving range estimation method by considering the battery degradation effects. Firstly, the incremental capacity analysis method is introduced to characterize the battery performance, and the battery degradation levels under different charging modes are recognized. Afterward, four types of ensemble machine learning (EML) methods are adopted to model the driving range estimation. The BEB driving data, weather condition data and battery degradation levels are used to train and test the models with consideration of 17 impact factors together with two different charging modes. The results indicate that the ensemble machine learning methods have good performance overall, of which the random forest has the highest accuracy. Furthermore, the importance of influencing factors is analysed, and the relevant insights are discussed.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleEnsemble machine learning based driving range estimation for real‐world electric city buses by considering battery degradation levelsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber824-836en_US
dc.source.volume15en_US
dc.source.journalIET Intelligent Transport Systemsen_US
dc.source.issue6en_US
dc.identifier.doi10.1049/itr2.12064
dc.identifier.cristin1907171
dc.relation.projectNorges forskningsråd: 299078en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal