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Hybrid Research Platform for Fundamental and Empirical Modeling and Analysis of Energy Management of Shared Electric Vehicles

Author

Listed:
  • Martin Koreny

    (Development Rear Lighting Electronics, HELLA AUTOTECHNIK NOVA, s.r.o., Druzstevni 338/16, 789 85 Mohelnice, Czech Republic)

  • Petr Simonik

    (Department of Electronics, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic)

  • Tomas Klein

    (Department of Electronics, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic)

  • Tomas Mrovec

    (Department of Electronics, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic)

  • Joy Jason Ligori

    (Department of Electronics, Faculty of Electrical Engineering and Computer Science, VSB—Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic)

Abstract

This article presents the results of the development of a hybrid research platform for fundamental and empirical modeling and analysis of energy management of shared electric vehicles. The article describes the hybrid model and its specific features in detail. Within the model architecture, a part of the fundamental model, empirical model and data collection tools were interconnected. The uniqueness lies in the models of electric cars created for a specific vehicle using cost-optimal parameterizations, as well as the implementation of a cloud solution, which is based on custom data communication, custom data logger and cost-optimized parameterization of machine learning algorithms. Experimental verification was performed on a real electric car in public traffic. The car is part of casharing platform.

Suggested Citation

  • Martin Koreny & Petr Simonik & Tomas Klein & Tomas Mrovec & Joy Jason Ligori, 2022. "Hybrid Research Platform for Fundamental and Empirical Modeling and Analysis of Energy Management of Shared Electric Vehicles," Energies, MDPI, vol. 15(4), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1300-:d:746846
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    References listed on IDEAS

    as
    1. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    2. Cedric De Cauwer & Wouter Verbeke & Thierry Coosemans & Saphir Faid & Joeri Van Mierlo, 2017. "A Data-Driven Method for Energy Consumption Prediction and Energy-Efficient Routing of Electric Vehicles in Real-World Conditions," Energies, MDPI, vol. 10(5), pages 1-18, May.
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    Cited by:

    1. Carlos Santos-Iglesia & Pablo Fernández-Arias & Álvaro Antón-Sancho & Diego Vergara, 2022. "Energy Consumption of the Urban Transport Fleet in UNESCO World Heritage Sites: A Case Study of Ávila (Spain)," Sustainability, MDPI, vol. 14(9), pages 1-19, May.
    2. Jacek Pielecha & Kinga Skobiej & Przemyslaw Kubiak & Marek Wozniak & Krzysztof Siczek, 2022. "Exhaust Emissions from Plug-in and HEV Vehicles in Type-Approval Tests and Real Driving Cycles," Energies, MDPI, vol. 15(7), pages 1-38, March.

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