TY - GEN
T1 - Range prediction for EVs via crowd-sourcing
AU - Grubwinkler, Stefan
AU - Brunner, Tobias
AU - Lienkamp, Markus
N1 - Publisher Copyright:
© 2014 IEEE.
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.
UR - http://www.scopus.com/inward/record.url?scp=84934300459&partnerID=8YFLogxK
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 -