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Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks

Author

Listed:
  • Maximilian Zähringer

    (Institute of Automotive Technology, Technical University Munich, Boltzmannstraße 15, 85748 Garching, Germany)

  • Sebastian Wolff

    (Institute of Automotive Technology, Technical University Munich, Boltzmannstraße 15, 85748 Garching, Germany)

  • Jakob Schneider

    (Institute of Automotive Technology, Technical University Munich, Boltzmannstraße 15, 85748 Garching, Germany)

  • Georg Balke

    (Institute of Automotive Technology, Technical University Munich, Boltzmannstraße 15, 85748 Garching, Germany)

  • Markus Lienkamp

    (Institute of Automotive Technology, Technical University Munich, Boltzmannstraße 15, 85748 Garching, Germany)

Abstract

The decarbonization of the transport sector, and thus of road-based transport logistics, through electrification, is essential to achieve European climate targets. Battery electric trucks offer the greatest well-to-wheel potential for CO 2 saving. At the same time, however, they are subject to restrictions due to charging events because of their limited range compared to conventional trucks. These restrictions can be kept to a minimum through optimal charging stop strategies. In this paper, we quantify these restrictions and show the potential of optimal strategies. The modeling of an optimal charging stop strategy is described mathematically as an optimization problem and solved by a genetic algorithm. The results show that in the case of long-distance transport using trucks with battery capacities lower than 750 kWh, a time loss is to be expected. However, this can be kept below 20 min for most battery capacities by optimal charging stops and sufficient charging infrastructure.

Suggested Citation

  • Maximilian Zähringer & Sebastian Wolff & Jakob Schneider & Georg Balke & Markus Lienkamp, 2022. "Time vs. Capacity—The Potential of Optimal Charging Stop Strategies for Battery Electric Trucks," Energies, MDPI, vol. 15(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7137-:d:928056
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    References listed on IDEAS

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    2. Sebastian Wolff & Svenja Kalt & Manuel Bstieler & Markus Lienkamp, 2021. "Influence of Powertrain Topology and Electric Machine Design on Efficiency of Battery Electric Trucks—A Simulative Case-Study," Energies, MDPI, vol. 14(2), pages 1-15, January.
    3. Michael Schneider & Andreas Stenger & Dominik Goeke, 2014. "The Electric Vehicle-Routing Problem with Time Windows and Recharging Stations," Transportation Science, INFORMS, vol. 48(4), pages 500-520, November.
    4. Sebastian Wolff & Michael Fries & Markus Lienkamp, 2020. "Technoecological analysis of energy carriers for long‐haul transportation," Journal of Industrial Ecology, Yale University, vol. 24(1), pages 165-177, February.
    5. Schneider, M. & Stenger, A. & Goeke, D., 2014. "The Electric Vehicle Routing Problem with Time Windows and Recharging Stations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 62382, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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