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Optimizing the Journey: Dynamic Charging Strategies for Battery Electric Trucks in Long-Haul Transport

Author

Listed:
  • Maximilian Zähringer

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

  • Olaf Teichert

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

  • Georg Balke

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

  • Jakob Schneider

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

  • Markus Lienkamp

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

Abstract

Battery electric trucks (BETs) represent a well-suited option for decarbonizing road freight transport to achieve climate targets in the European Union. However, lower ranges than the daily distance of up to 700 km make charging stops mandatory. This paper presents an online algorithm for optimal dynamic charging strategies for long-haul BET based on a dynamic programming approach. In several case studies, we investigate the advantages optimal strategies can bring compared to driver decisions. We further show which charging infrastructure characteristics in terms of charging power, density, and charging station availability should be achieved for BETs in long-haul applications to keep the additional time required for charging stops low. In doing so, we consider the dynamic handling of occupied charging stations for the first time in the context of BET. Our findings show that, compared to driver decisions, optimal charging strategies can reduce the time loss by half compared to diesel trucks. To keep the time loss compared to a diesel truck below 30 min a day, a BET with a 500 kWh battery would need a charging point every 50 km on average, a distributed charging power between 700 and 1500 kW, and an average charger availability above 75%. The presented method and the case studies’ results’ plausibility are interpreted within a comprehensive sensitivity analysis and subsequently discussed in detail. Finally, we transformed our findings into concrete recommendations for action for the efficient rollout of BETs in long-haul applications.

Suggested Citation

  • Maximilian Zähringer & Olaf Teichert & Georg Balke & Jakob Schneider & Markus Lienkamp, 2024. "Optimizing the Journey: Dynamic Charging Strategies for Battery Electric Trucks in Long-Haul Transport," Energies, MDPI, vol. 17(4), pages 1-25, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:4:p:973-:d:1341631
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    References listed on IDEAS

    as
    1. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    2. Auer, Judith & Link, Steffen & Plötz, Patrick, 2023. "Public charging locations for battery electric trucks: A GIS-based statistical analysis using real-world truck stop data for Germany," Working Papers "Sustainability and Innovation" S04/2023, Fraunhofer Institute for Systems and Innovation Research (ISI).
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