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Energy Prediction for EVs Using Support Vector Regression Methods

  • Conference paper
Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 323))

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 consumption on each road segment. As a result, the prediction of propulsion energy consumption for EVs before the start of a trip has a relative mean error of less than 6.7%.

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Author information

Authors and Affiliations

  1. Institute of Automotive Technology, Technische Universitaet Muenchen, Boltzmannstr. 15, 85748, Garching b. Muenchen, Germany

    Stefan Grubwinkler & Markus Lienkamp

Authors
  1. Stefan Grubwinkler
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  2. Markus Lienkamp
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Corresponding author

Correspondence to Stefan Grubwinkler .

Editor information

Editors and Affiliations

  1. Ford Motor Company, Research & Advanced Engineering, Dearborn, Mississippi, USA

    D. Filev

  2. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    J. Jabłkowski

  3. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    J. Kacprzyk

  4. Polish Academy of Sciences and WIT - Warsaw School of Information Technology, Systems Research Institute, Warsaw, Poland

    M. Krawczak

  5. Bulgarian Academy of Sciences, Institute of Information and Communication, Sofia, Bulgaria

    I. Popchev

  6. Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland

    L. Rutkowski

  7. Bulgarian Academy of Sciences, Institute of Information and Communication Technologies, Sofia, Bulgaria

    V. Sgurev

  8. Department of Computer and Information Technologies, “Prof. Assen Zlatarov" University Faculty of Technical Sciences, Bourgas, Bulgaria

    E. Sotirova

  9. Industrial Research Institute for Automation and Measurements (PIAP), Warsaw, Poland

    P. Szynkarczyk

  10. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    S. Zadrozny

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© 2015 Springer International Publishing Switzerland

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Grubwinkler, S., Lienkamp, M. (2015). Energy Prediction for EVs Using Support Vector Regression Methods. In: Filev, D., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 323. Springer, Cham. https://doi.org/10.1007/978-3-319-11310-4_67

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  • DOI: https://doi.org/10.1007/978-3-319-11310-4_67

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11309-8

  • Online ISBN: 978-3-319-11310-4

  • eBook Packages: EngineeringEngineering (R0)

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