Smart Data Selection and Reduction for Electric Vehicle Service Analytics

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dc.contributor.authorSchoch, Jennifer
dc.contributor.authorStaudt, Philipp
dc.contributor.authorSetzer, Thomas
dc.date.accessioned2016-12-29T00:40:09Z
dc.date.available2016-12-29T00:40:09Z
dc.date.issued2017-01-04
dc.description.abstractBattery 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. \
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2017.192
dc.identifier.isbn978-0-9981331-0-2
dc.identifier.urihttp://hdl.handle.net/10125/41345
dc.language.isoeng
dc.relation.ispartofProceedings of the 50th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBattery Electric Vehicles
dc.subjectService Analytics
dc.subjectService Usage
dc.subjectData Reduction
dc.titleSmart Data Selection and Reduction for Electric Vehicle Service Analytics
dc.typeConference Paper
dc.type.dcmiText

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