A new predictive electric bus model using big data for estimating battery pack capacity

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dc.contributor.authorYirik, Emrah
dc.contributor.authorIrmak, Umut
dc.contributor.authorDericioglu, Cagla
dc.contributor.authorUnal, Erdem
dc.contributor.authorCuma, Mehmet Ugras
dc.date.accessioned2025-01-06T17:29:48Z
dc.date.available2025-01-06T17:29:48Z
dc.date.issued2017
dc.description30th International Electric Vehicle Symposium and Exhibition, EVS 2017 -- 9 October 2017 through 11 October 2017 -- Stuttgart -- 136982
dc.description.abstractThis paper describes a new predictive neural network model for electric buses by using big data analysis tools. Electric buses are often used for public transportation with many routes. Each route in a city has different parameters such as length, elevation, number of bus stops, traffic density which determines battery pack capacity. Moreover average number of passengers and ambient temperature are other important parameters that must be considered. In this paper, a new predictive model for electric buses is proposed using aforementioned parameters. To develop the model, the study takes the advantages of the Big Data Analysis tools by gathering the data from real vehicles. © 2017 MOBI - Mobility, Logistics and Automotive Technology Research Centre.
dc.description.sponsorshipTEMSA
dc.identifier.scopus2-s2.0-85050075125
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1359
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherLandesmesse Stuttgart GmbH
dc.relation.ispartofEVS 2017 - 30th International Electric Vehicle Symposium and Exhibition
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectBattery Pack Size Estimation
dc.subjectBig Data
dc.subjectElectric Bus
dc.titleA new predictive electric bus model using big data for estimating battery pack capacity
dc.typeConference Object

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