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Shortest Feasible Paths with Charging Stops for Battery Electric Vehicles

Author

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  • Moritz Baum

    (Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany)

  • Julian Dibbelt

    (Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany)

  • Andreas Gemsa

    (Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany)

  • Dorothea Wagner

    (Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany)

  • Tobias Zündorf

    (Karlsruhe Institute of Technology (KIT), Karlsruhe 76131, Germany)

Abstract

We study the problem of minimizing overall trip time for battery electric vehicles in road networks. As battery capacity is limited, stops at charging stations may be inevitable. Careful route planning is crucial because charging stations are scarce and recharging is time-consuming. We extend the constrained shortest-path problem for electric vehicles with realistic models of charging stops, including varying charging power and battery-swapping stations. Although the resulting problem is theoretically hard, we propose a combination of algorithmic techniques to achieve good performance in practice. Extensive experimental evaluation shows that our approach (CHArge) enables computation of optimal solutions on realistic inputs even of continental scale. Finally, we investigate heuristic variants of CHArge that derive high-quality routes in well below a second on sensible instances.

Suggested Citation

  • Moritz Baum & Julian Dibbelt & Andreas Gemsa & Dorothea Wagner & Tobias Zündorf, 2019. "Shortest Feasible Paths with Charging Stops for Battery Electric Vehicles," Transportation Science, INFORMS, vol. 53(6), pages 1627-1655, November.
    • Handle: RePEc:inm:ortrsc:v:53:y:2019:i:6:p:1627-1655
    DOI: 10.1287/trsc.2018.0889
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    References listed on IDEAS

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    Cited by:

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    2. LIAN, Ying & LUCAS, Flavien & SÖRENSEN, Kenneth, 2022. "The electric on-demand bus routing problem with partial charging and nonlinear functions," Working Papers 2022005, University of Antwerp, Faculty of Business and Economics.
    3. Junchi Ma & Yuan Zhang & Zongtao Duan & Lei Tang, 2023. "PROLIFIC: Deep Reinforcement Learning for Efficient EV Fleet Scheduling and Charging," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    4. Pottel, Steffen & Goel, Asvin, 2022. "Scheduling activities with time-dependent durations and resource consumptions," European Journal of Operational Research, Elsevier, vol. 301(2), pages 445-457.
    5. Maximiliano Cubillos & Mauro Dell’Amico & Ola Jabali & Federico Malucelli & Emanuele Tresoldi, 2023. "An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences," Energies, MDPI, vol. 16(10), pages 1-19, May.

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