Title :
Range Prediction for EVs via Crowd-Sourcing
Author :
Grubwinkler, Stefan ; Brunner, Tobias ; Lienkamp, Markus
Author_Institution :
Inst. of Automotive Technol., Tech. Univ. Muenchen, Garching, Germany
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.
Keywords :
cloud computing; driver information systems; electric vehicles; intelligent transportation systems; road traffic; cloud-based system; crowd-sourced speed profiles; electric vehicles; energy prediction; range prediction; traffic congestion; Acceleration; Energy consumption; Feature extraction; Predictive models; Roads; Routing; Vehicles;
Conference_Titel :
Vehicle Power and Propulsion Conference (VPPC), 2014 IEEE
Conference_Location :
Coimbra
DOI :
10.1109/VPPC.2014.7007121