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Public charging locations for battery electric trucks: A GIS-based statistical analysis using real-world truck stop data for Germany

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  • Auer, Judith
  • Link, Steffen
  • Plötz, Patrick

Abstract

Adequate public charging infrastructure for battery electric trucks (BETs) is crucial for electrifying road freight transport and, thus, curtailing greenhouse gas emissions. Although manufacturer announcements on BET sales targets are promising, many logistic companies still question their technical feasibility due to the limited all-electric range and insufficient public charging infrastructure. Therefore, knowing the attractiveness of truck stop locations and their relevance for ensuring operational schedules is essential to facilitate the coordinated deployment of public charging infrastructure while its profitability is almost pre-secured. This paper aims to characterize current truck stop locations and evaluate possible public charging station locations for BETs via multi-criteria analyses using Geographical Information Systems (GIS) data. This study benefits from real-world truck stop location data, including geo-coordinates and occupancy data, and uses several GIS data sources to enhance the data and verify the presence of distinct truck-relevant features. Features may comprise the proximity to the TEN-T highway network or infrastructure availability, such as fueling stations or rest areas. Additionally, correlation and archetypal analysis are applied to better understand truck stops and their feature dependencies. The results demonstrate the high attractiveness of industrial areas with many potential business destinations along the TEN-T network. However, no particular feature determines the attractiveness of truck stop locations, but the distinct feature combination is decisive. The archetypal analysis reveals three extremes that may constitute the backbone of a public German charging infrastructure network: (1) industry hotspots, (2) hosted rest areas or truck stops along the TEN-T network, (3) and public truck parking areas with additional services. Finally, 1,648 public parking and rest areas in Germany are identified using OpenStreetMaps.org (OSM) data, and their attractiveness for future BET charging infrastructure is evaluated. These results are provided in an interactive HTML-based map.

Suggested Citation

  • 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).
    • Handle: RePEc:zbw:fisisi:s042023
    DOI: 10.24406/publica-1198
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    References listed on IDEAS

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    1. Wang, Jiang-Jiang & Jing, You-Yin & Zhang, Chun-Fa & Zhao, Jun-Hong, 2009. "Review on multi-criteria decision analysis aid in sustainable energy decision-making," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2263-2278, December.
    2. Mikołaj Schmidt & Paweł Zmuda-Trzebiatowski & Marcin Kiciński & Piotr Sawicki & Konrad Lasak, 2021. "Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem," Energies, MDPI, vol. 14(11), pages 1-34, May.
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    Cited by:

    1. 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.

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    Keywords

    Charging infrastructure site selection; Multi-criteria decision analysis; GIS; Battery electric trucks;
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