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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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    1. Song, Ziyou & Li, Jianqiu & Hou, Jun & Hofmann, Heath & Ouyang, Minggao & Du, Jiuyu, 2018. "The battery-supercapacitor hybrid energy storage system in electric vehicle applications: A case study," Energy, Elsevier, vol. 154(C), pages 433-441.
    2. Apostolaki-Iosifidou, Elpiniki & Codani, Paul & Kempton, Willett, 2017. "Measurement of power loss during electric vehicle charging and discharging," Energy, Elsevier, vol. 127(C), pages 730-742.
    3. Luin, B. & Petelin, S. & Al Mansour, F., 2017. "Modeling the impact of road network configuration on vehicle energy consumption," Energy, Elsevier, vol. 137(C), pages 260-271.
    4. Abu Hanifah, Rabiatuladawiyah & Toha, Siti Fauziah & Hassan, Mohd Khair & Ahmad, Salmiah, 2016. "Power reduction optimization with swarm based technique in electric power assist steering system," Energy, Elsevier, vol. 102(C), pages 444-452.
    5. Wu, Tian & Shen, Qu & Xu, Ming & Peng, Tianduo & Ou, Xunmin, 2018. "Development and application of an energy use and CO2 emissions reduction evaluation model for China's online car hailing services," Energy, Elsevier, vol. 154(C), pages 298-307.
    6. Benajes, Jesús & García, Antonio & Monsalve-Serrano, Javier & Lago Sari, Rafael, 2018. "Fuel consumption and engine-out emissions estimations of a light-duty engine running in dual-mode RCCI/CDC with different fuels and driving cycles," Energy, Elsevier, vol. 157(C), pages 19-30.
    7. Yuan, Xinmei & Zhang, Chuanpu & Hong, Guokai & Huang, Xueqi & Li, Lili, 2017. "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, Elsevier, vol. 141(C), pages 1955-1968.
    8. Robert B. Noland * & Washington Y. Ochieng & Mohammed A. Quddus & Robin J. North & John W. Polak, 2004. "The vehicle emissions and performance monitoring system: analysis of tailpipe emissions and vehicle performance," Transportation Planning and Technology, Taylor & Francis Journals, vol. 27(6), pages 431-447, September.
    9. Giansoldati, Marco & Danielis, Romeo & Rotaris, Lucia & Scorrano, Mariangela, 2018. "The role of driving range in consumers' purchasing decision for electric cars in Italy," Energy, Elsevier, vol. 165(PA), pages 267-274.
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    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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.

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