Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models

dc.contributor.author	Scheubner, Stefan
dc.date.accessioned	2022-06-21T04:02:45Z
dc.date.available	2022-06-21T04:02:45Z
dc.date.issued	2022
dc.date.submitted	2022-06-20T19:09:54Z
dc.identifier	ONIX_20220620_9783731511663_74
dc.identifier	1869-6058
dc.identifier	https://library.oapen.org/handle/20.500.12657/56964
dc.identifier.uri	https://directory.doabooks.org/handle/20.500.12854/84371
dc.description.abstract	This work aims at improving the energy consumption forecast of electric vehicles by enhancing the prediction with a notion of uncertainty. The algorithm itself learns from driver and traffic data in a training set to generate accurate, driver-individual energy consumption forecasts.
dc.language	English
dc.relation.ispartofseries	Karlsruher Schriftenreihe Fahrzeugsystemtechnik
dc.rights	open access
dc.subject.other	Elektromobilität
dc.subject.other	Vorhersagen
dc.subject.other	Algorithmen
dc.subject.other	Fahrzeugtechnik
dc.subject.other	Energiemanagement
dc.subject.other	E-Mobility
dc.subject.other	Forecasting
dc.subject.other	Algorithms
dc.subject.other	Vehicle Technology
dc.subject.other	Energy Management
dc.title	Stochastic Range Estimation Algorithms for Electric Vehicles using Data-Driven Learning Models
dc.type	book
oapen.identifier.doi	10.5445/KSP/1000143200
oapen.relation.isPublishedBy	68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
oapen.relation.isbn	9783731511663
oapen.imprint	KIT Scientific Publishing
oapen.pages	192
oapen.place.publication	Karlsruhe
dc.seriesnumber	6

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