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000979878 001__ 979878
000979878 005__ 20250930081335.0
000979878 0247_ $$2datacite_doi$$a10.18154/RWTH-2024-01907
000979878 037__ $$aRWTH-2024-01907
000979878 041__ $$aEnglish
000979878 1001_ $$0P:(DE-82)IDM01655$$aRücker, Fabian$$b0$$eCorresponding author
000979878 245__ $$aDataset to „Battery Electric Vehicles in Commercial Fleets: Use profiles, battery aging, and open-access data“
000979878 260__ $$c2024
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000979878 520__ $$aElectric vehicles are key to reducing emissions in the transportation sector, and the electric vehicle market is growing strongly worldwide. In research and development in the electric vehicle sector, it is important to understand electric vehicle use profiles and their impact on battery aging. The battery is the central element of a vehicle as it determines the range as well as the price. For techno-economic analysis, battery aging is of special interest as it is a major factor in the vehicle’s lifetime. Battery aging depends on the battery use profile, but up to now, operational data is scarce, and many publications are based on simulations and assumptions. To contribute, we analyze multi-year commercial vehicle and battery data collected from onboard data loggers in a field test conducted from 2014 to 2016. We also develop and evaluate periodic non-intrusive capacity tests carried out with a chassis dynamometer to assess battery aging in terms of capacity fade. Electric vehicles in a geriatric care fleet experienced high usage and regular driving patterns with annual driving distances of 9062 km to  15308 km and a mean state-of-charge between 70% and 80% while driving. Use profile regularity and state-of-charge values at the time of plug-in are major factors in the evaluation of vehicle-to-X application viability. Regarding aging, the batteries suffered a capacity fade from 3.1% to up to 13% during fleet operation of three years. We publish time series data of the vehicle and the battery in addition to mobility data for nine vehicles to fill the data gap for available electric vehicle data and allow for further analysis.
000979878 536__ $$0G:(BMVI)16SBS001C$$a16SBS001C - Gewerblich operierende Elektro-Kleinflotten GO ELK! (16SBS001C)$$c16SBS001C$$x0
000979878 591__ $$aGermany
000979878 653_7 $$aelectric vehicle
000979878 653_7 $$aopen-access data
000979878 653_7 $$acharging curve
000979878 653_7 $$afield test
000979878 653_7 $$abattery aging
000979878 653_7 $$afleet
000979878 7001_ $$0P:(DE-82)IDM02284$$aFiggener, Jan$$b1
000979878 7001_ $$0P:(DE-HGF)0$$aSchoeneberger, Ilka$$b2
000979878 7001_ $$0P:(DE-82)IDM01459$$aSauer, Dirk Uwe$$b3
000979878 7870_ $$0RWTH-2024-03603$$aRücker, Fabian et.al.$$dAmsterdam [u.a.] : Elsevier, 2024$$iRelatedTo$$tBattery Electric Vehicles in Commercial Fleets: Use profiles, battery aging, and open-access data
000979878 8564_ $$uhttps://publications.rwth-aachen.de/record/979878/files/Rechteeintraeumung_979878.pdf
000979878 8564_ $$uhttps://publications.rwth-aachen.de/record/979878/files/Electric_Vehicle_and_Battery_Data.zip$$yOpenAccess
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000979878 9141_ $$y2024
000979878 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000979878 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000979878 9201_ $$0I:(DE-82)618310_20140620$$k618310$$lLehrstuhl für Elektrochemische Energiewandlung und Speichersystemtechnik$$x0
000979878 9201_ $$0I:(DE-82)080070_20210623$$k080070$$lCenter for Ageing, Reliability and Lifetime Prediction of Electrochemical and Power Electronic Systems$$x1
000979878 9201_ $$0I:(DE-82)080016_20140620$$k080016$$lProfilbereich Energy, Chemical & Process Engineering (ECPE)$$x2
000979878 961__ $$c2024-03-26T09:51:53.927785$$x2024-02-22T16:31:49.460342$$z2024-03-26T09:51:53.927785
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