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Document type:
Konferenzbeitrag 
Author(s):
Grubwinkler, Stefan; Markus Lienkamp 
Title:
Energy Prediction for EVs Using Support Vector Regression Methods 
Pages contribution:
769-780 
Abstract:
This paper presents the application of machine learning algorithms for an accurate estimation of the energy consumption of electric vehicles (EVs). Normalised energy consumption values and speed profiles are collected from various EVs for a cloud-based prediction approach. We predict the necessary energy for each road segment on the basis of crowd-sourced data. Support vector machines, which are trained by the collected historical data of the driver, predict the deviation from the average energy...    »
 
Keywords:
FTM Smarte Mobilität 
Book / Congress title:
Advances in Intelligent Systems and Computing 
Congress (additional information):
IEEE Intelligent Systems 
Volume:
Volume 323 
Organization:
IEEE 
Date of congress:
24.-26.09.2014 
Publisher:
Springer International Publishing 
Year:
2014 
Covered by:
Scopus 
Print-ISBN:
978-3-319-11309-8 
TUM Institution:
Lehrstuhl für Fahrzeugtechnik