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

Author

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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:

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    2. Min Wei & Yuhang Liu & Haojie Wang & Siquan Yuan & Jie Hu, 2025. "State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture," Mathematics, MDPI, vol. 13(13), pages 1-22, July.
    3. Irfan Ullah & Kai Liu & Toshiyuki Yamamoto & Rabia Emhamed Al Mamlook & Arshad Jamal, 2022. "A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability," Energy & Environment, , vol. 33(8), pages 1583-1612, December.
    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).
    5. Tancredi Testasecca & Francesco Bellesini & Diego Arnone & Marco Beccali, 2025. "A Digital Twin for Real-Time and Predictive Optimization of Electric Vehicle Charging in Microgrids Integrating Renewable Energy Sources," Energies, MDPI, vol. 18(21), pages 1-26, October.
    6. 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.
    7. 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.
    8. Lou, Benxiao & Tang, Jinjun & Hu, Lipeng & Ye, Junqing, 2025. "Multi-source data-driven short-term remaining driving range prediction for electric vehicles: A hybrid CNN-transformer framework," Energy, Elsevier, vol. 334(C).
    9. 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.
    10. Gurusamy, Azhaganathan & Bokdia, Akshat & Kumar, Harsh & Ashok, Bragadeshwaran & Gunavathi, Chellamuthu, 2025. "Appositeness of automated machine learning libraries on prediction of energy consumption for electric two-wheelers based on micro-trip approach," Energy, Elsevier, vol. 320(C).

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