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

Chair of Automotive Technology

Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

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.

Original language	English
Title of host publication	2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479967834
DOIs	https://doi.org/10.1109/VPPC.2014.7007121
State	Published - 2014
Event	2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014 - Coimbra, Portugal Duration: 27 Oct 2014 → 30 Oct 2014

Publication series

Name	2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014

Conference

Conference	2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014
Country/Territory	Portugal
City	Coimbra
Period	27/10/14 → 30/10/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/VPPC.2014.7007121

Cite this

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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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Grubwinkler, Stefan

AU - Brunner, Tobias

AU - Lienkamp, Markus

PY - 2014

Y1 - 2014

N2 - 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.

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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U2 - 10.1109/VPPC.2014.7007121

DO - 10.1109/VPPC.2014.7007121

M3 - Conference contribution

AN - SCOPUS:84934300459

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

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE Vehicle Power and Propulsion Conference, VPPC 2014

Y2 - 27 October 2014 through 30 October 2014

ER -

Range prediction for EVs via crowd-sourcing

Abstract

Publication series

Conference

UN SDGs

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