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Battery-Aware Electric Truck Delivery Route Exploration

Author

Listed:
  • Donkyu Baek

    (School of Electronics Engineering, Chungbuk National University, Cheongju 28644, Korea)

  • Yukai Chen

    (Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, 10129 Torino, Italy)

  • Naehyuck Chang

    (School of Electrical Engineering, Korea Advanced Institute of Science and Technology, lDaejeon 34141, Korea)

  • Enrico Macii

    (Interuniversity Department of Regional and Urban Studies and Planning (DIST), Politecnico di Torino, 10129 Torino, Italy)

  • Massimo Poncino

    (Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, 10129 Torino, Italy)

Abstract

The energy-optimal routing of Electric Vehicles (EVs) in the context of parcel delivery is more complicated than for conventional Internal Combustion Engine (ICE) vehicles, in which the total travel distance is the most critical metric. The total energy consumption of EV delivery strongly depends on the order of delivery because of transported parcel weight changing over time, which directly affects the battery efficiency. Therefore, it is not suitable to find an optimal routing solution with traditional routing algorithms such as the Traveling Salesman Problem (TSP), which use a static quantity (e.g., distance) as a metric. In this paper, we explore appropriate metrics considering the varying transported parcel total weight and achieve a solution for the least-energy delivery problem using EVs. We implement an electric truck simulator based on EV powertrain model and nonlinear battery model. We evaluate different metrics to assess their quality on small size instances for which the optimal solution can be computed exhaustively. A greedy algorithm using the empirically best metric (namely, distance × residual weight) provides significant reductions (up to 33%) with respect to a common-sense heaviest first package delivery route determined using a metric suggested by the battery properties. This algorithm also outperforms the state-of-the-art TSP heuristic algorithms, which consumes up to 12.46% more energy and 8.6 times more runtime. We also estimate how the proposed algorithms work well on real roads interconnecting cities located at different altitudes as a case study.

Suggested Citation

  • Donkyu Baek & Yukai Chen & Naehyuck Chang & Enrico Macii & Massimo Poncino, 2020. "Battery-Aware Electric Truck Delivery Route Exploration," Energies, MDPI, vol. 13(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2096-:d:349889
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    References listed on IDEAS

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    1. Margaretha Gansterer & Murat Küçüktepe & Richard F. Hartl, 2017. "The multi-vehicle profitable pickup and delivery problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(1), pages 303-319, January.
    2. Donkyu Baek & Yukai Chen & Naehyuck Chang & Enrico Macii & Massimo Poncino, 2020. "Optimal Battery Sizing for Electric Truck Delivery," Energies, MDPI, vol. 13(3), pages 1-15, February.
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    Cited by:

    1. Wojciech Cieslik & Weronika Antczak, 2023. "Research of Load Impact on Energy Consumption in an Electric Delivery Vehicle Based on Real Driving Conditions: Guidance for Electrification of Light-Duty Vehicle Fleet," Energies, MDPI, vol. 16(2), pages 1-19, January.

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