Range prediction for EVs via crowd-sourcing

Stefan Grubwinkler; Tobias Brunner; Markus Lienkamp

doi:10.1109/VPPC.2014.7007121

Range prediction for EVs via crowd-sourcing

Stefan Grubwinkler, Tobias Brunner, Markus Lienkamp

Lehrstuhl für Fahrzeugtechnik

Technische Universität München

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

13 Zitate (Scopus)

Abstract

Drivers of electric vehicles (EVs) need an accurate energy prediction in order to prevent running out of battery. We introduce a cloud-based system using crowd-sourced speed profiles for the energy prediction, since they consider the individual driving behaviour and the prevailing traffic congestion. In this paper, we focus on the modular cloud-based energy prediction system which provides three prediction values with various degrees of accuracy and complexity for different user groups. We realise a prototypical driving range prediction before the start of a trip within an application for a mobile device.

Originalsprache	Englisch
Titel	2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014
Herausgeber (Verlag)	Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)	9781479967834
DOIs	https://doi.org/10.1109/VPPC.2014.7007121
Publikationsstatus	Veröffentlicht - 2014
Veranstaltung	2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014 - Coimbra, Portugal Dauer: 27 Okt. 2014 → 30 Okt. 2014

Publikationsreihe

Name	2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014

Konferenz

Konferenz	2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014
Land/Gebiet	Portugal
Ort	Coimbra
Zeitraum	27/10/14 → 30/10/14

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title = "Range prediction for EVs via crowd-sourcing",

abstract = "Drivers of electric vehicles (EVs) need an accurate energy prediction in order to prevent running out of battery. We introduce a cloud-based system using crowd-sourced speed profiles for the energy prediction, since they consider the individual driving behaviour and the prevailing traffic congestion. In this paper, we focus on the modular cloud-based energy prediction system which provides three prediction values with various degrees of accuracy and complexity for different user groups. We realise a prototypical driving range prediction before the start of a trip within an application for a mobile device.",

author = "Stefan Grubwinkler and Tobias Brunner and Markus Lienkamp",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014 ; Conference date: 27-10-2014 Through 30-10-2014",

year = "2014",

doi = "10.1109/VPPC.2014.7007121",

language = "English",

series = "2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014",

}

Grubwinkler, S, Brunner, T & Lienkamp, M 2014, Range prediction for EVs via crowd-sourcing. in 2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014., 7007121, 2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014, Institute of Electrical and Electronics Engineers Inc., 2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014, Coimbra, Portugal, 27/10/14. https://doi.org/10.1109/VPPC.2014.7007121

Range prediction for EVs via crowd-sourcing. / Grubwinkler, Stefan; Brunner, Tobias; Lienkamp, Markus.
2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 7007121 (2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

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AU - Brunner, Tobias

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AB - Drivers of electric vehicles (EVs) need an accurate energy prediction in order to prevent running out of battery. We introduce a cloud-based system using crowd-sourced speed profiles for the energy prediction, since they consider the individual driving behaviour and the prevailing traffic congestion. In this paper, we focus on the modular cloud-based energy prediction system which provides three prediction values with various degrees of accuracy and complexity for different user groups. We realise a prototypical driving range prediction before the start of a trip within an application for a mobile device.

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DO - 10.1109/VPPC.2014.7007121

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T3 - 2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014

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ER -

Range prediction for EVs via crowd-sourcing

Abstract

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