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Implementation of machine learning based real time range estimation method without destination knowledge for BEVs

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  • Yavasoglu, H.A.
  • Tetik, Y.E.
  • Gokce, K.

Abstract

In this work, an advanced range estimation method based on experimental test data including environmental factors and dynamic vehicle parameters with driver and road type predictions is proposed for electric vehicles.

Suggested Citation

  • Yavasoglu, H.A. & Tetik, Y.E. & Gokce, K., 2019. "Implementation of machine learning based real time range estimation method without destination knowledge for BEVs," Energy, Elsevier, vol. 172(C), pages 1179-1186.
    • Handle: RePEc:eee:energy:v:172:y:2019:i:c:p:1179-1186
    DOI: 10.1016/j.energy.2019.02.032
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    References listed on IDEAS

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    Cited by:

    1. Antonio Galvagno & Umberto Previti & Fabio Famoso & Sebastian Brusca, 2021. "An Innovative Methodology to Take into Account Traffic Information on WLTP Cycle for Hybrid Vehicles," Energies, MDPI, vol. 14(6), pages 1-16, March.
    2. Marouane Adnane & Ahmed Khoumsi & João Pedro F. Trovão, 2023. "Efficient Management of Energy Consumption of Electric Vehicles Using Machine Learning—A Systematic and Comprehensive Survey," Energies, MDPI, vol. 16(13), pages 1-39, June.
    3. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2023. "Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art," Sustainability, MDPI, vol. 15(7), pages 1-30, March.
    4. Bas, Javier & Cirillo, Cinzia & Cherchi, Elisabetta, 2021. "Classification of potential electric vehicle purchasers: A machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 168(C).

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