Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41345

Smart Data Selection and Reduction for Electric Vehicle Service Analytics

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Title: Smart Data Selection and Reduction for Electric Vehicle Service Analytics
Authors: Schoch, Jennifer
Staudt, Philipp
Setzer, Thomas
Keywords: Battery Electric Vehicles
Service Analytics
Service Usage
Data Reduction
Issue Date: 04 Jan 2017
Abstract: 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. \
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41345
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.192
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Service Analytics Minitrack



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