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Optimizing the allocation of fast charging infrastructure along the German autobahn

Author

Listed:
  • Patrick Jochem

    (Karlsruhe Institute of Technology (KIT))

  • Carsten Brendel

    (Karlsruhe Institute of Technology (KIT))

  • Melanie Reuter-Oppermann

    (Karlsruhe Institute of Technology (KIT))

  • Wolf Fichtner

    (Karlsruhe Institute of Technology (KIT))

  • Stefan Nickel

    (Karlsruhe Institute of Technology (KIT))

Abstract

The allocation of fast charging stations is a severe investment for the future mobility system with electric vehicles. The allocation of the first charging stations influences the profitability of all other fast charging stations and should therefore be perfectly arranged. Hence, we applied and extended the flow-refueling location model (FRLM) developed by Capar et al. (Eur J Oper Res 227(1):142–151, 2013) to the German autobahn with a focus on the states Baden-Württemberg and Bavaria with 595 nodes and 3569 highway km. Our model extension comprehends mainly the inclusion of the access distance for traffic participants to their closest network node. In order to analyze the impact of different vehicle ranges and the desired coverage of flows we defined four scenarios. The results indicate the significance of vehicle range and the desired coverage value. 20 optimally allocated fast charging stations along the highways lead already to a coverage of about 62 % (100 km vehicle range) or even 83 % (150 km vehicle range) of all trips. A complete coverage of trips requires at least 50 (150 km vehicle range), 77 (100 km vehicle range) or even 84 (70 km vehicle range) fast charging stations. The last 30 % coverage leads to a tripling of charging stations. Furthermore, a first estimation of the corresponding surcharge for fixed costs per charging process amounts to about 20 % of the total costs for a charging process.

Suggested Citation

  • Patrick Jochem & Carsten Brendel & Melanie Reuter-Oppermann & Wolf Fichtner & Stefan Nickel, 2016. "Optimizing the allocation of fast charging infrastructure along the German autobahn," Journal of Business Economics, Springer, vol. 86(5), pages 513-535, July.
  • Handle: RePEc:spr:jbecon:v:86:y:2016:i:5:d:10.1007_s11573-015-0781-5
    DOI: 10.1007/s11573-015-0781-5
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    Cited by:

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    2. Xie, Fei & Liu, Changzheng & Li, Shengyin & Lin, Zhenhong & Huang, Yongxi, 2018. "Long-term strategic planning of inter-city fast charging infrastructure for battery electric vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 261-276.
    3. Antonia Golab & Sebastian Zwickl-Bernhard & Hans Auer, 2022. "Minimum-Cost Fast-Charging Infrastructure Planning for Electric Vehicles along the Austrian High-Level Road Network," Energies, MDPI, vol. 15(6), pages 1-26, March.
    4. Münzel, Christiane & Plötz, Patrick & Sprei, Frances & Gnann, Till, 2019. "How large is the effect of financial incentives on electric vehicle sales? – A global review and European analysis," Energy Economics, Elsevier, vol. 84(C).
    5. Philipp Kluschke & Fabian Neumann, 2019. "Interaction of a Hydrogen Refueling Station Network for Heavy-Duty Vehicles and the Power System in Germany for 2050," Papers 1908.10119, arXiv.org.
    6. Anastasios Tsakalidis & Andreea Julea & Christian Thiel, 2019. "The Role of Infrastructure for Electric Passenger Car Uptake in Europe," Energies, MDPI, vol. 12(22), pages 1-18, November.
    7. Neaimeh, Myriam & Salisbury, Shawn D. & Hill, Graeme A. & Blythe, Philip T. & Scoffield, Don R. & Francfort, James E., 2017. "Analysing the usage and evidencing the importance of fast chargers for the adoption of battery electric vehicles," Energy Policy, Elsevier, vol. 108(C), pages 474-486.
    8. Funke, Simon Árpád & Plötz, Patrick & Wietschel, Martin, 2019. "Invest in fast-charging infrastructure or in longer battery ranges? A cost-efficiency comparison for Germany," Applied Energy, Elsevier, vol. 235(C), pages 888-899.
    9. Mandev, Ahmet & Plötz, Patrick & Sprei, Frances & Tal, Gil, 2022. "Empirical charging behavior of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 321(C).

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    More about this item

    Keywords

    Fast charging station; Electric vehicle; Optimization; Allocation; Germany;
    All these keywords.

    JEL classification:

    • M20 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • P48 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies

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