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Smart Data Selection and Reduction for Electric Vehicle Service Analytics


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Schoch, Jennifer ; Staudt, Philipp ; Setzer, Thomas:
Smart Data Selection and Reduction for Electric Vehicle Service Analytics.
In: Hawaii International Conference on System Sciences 2017 (HICSS-50) : Hilton Waikoloa Village, Hawaii, January 4-7, 2017. - Hawaii, USA, 2017. - S. 1592-1601
ISBN 978-0-9981331-0-2


Volltext Link zum Volltext (externe URL): 10.24251/HICSS.2017.192


Battery electric vehicles (BEV) are increasingly used in mobility services such as car-sharing. A severe problem with BEV is battery degradation, leading to a reduction of the already very limited range of a BEV. Analytic models are required to determine the impact of service usage to provide guidance on how to drive and charge and also to support service tasks such as predictive maintenance. However, while the increasing number of sensor data in automotive applications allows for more fine-grained model parameterization and better predictive outcomes, in practical settings the amount of storage and transmission bandwidth is limited by technical and economical considerations. By means of a simulation-based analysis, dynamic user behavior is simulated based on real-world driving profiles parameterized by different driver characteristics and ambient conditions. We find that by using a shrinked subset of variables the required storage can be reduced considerably at low costs in terms of only slightly decreased predictive accuracy.

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Publikationsform:Aufsatz in einem Buch
Schlagwörter:Battery Electric Vehicles; Service Analytics; Service Usage; Data Reduction
Institutionen der Universität:Wirtschaftswissenschaftliche Fakultät > Betriebswirtschaftslehre > ABWL und Wirtschaftsinformatik
DOI / URN / ID:10.24251/HICSS.2017.192
Titel an der KU entstanden:Nein
Eingestellt am: 06. Okt 2020 10:17
Letzte Änderung: 06. Okt 2020 16:09
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