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Novel Modelling Approach for the Calculation of the Loading Performance of Charging Stations for E-Trucks to Represent Fleet Consumption

Author

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  • Thomas Märzinger

    (Institute of Chemical and Energy Engineering, University of Naturel Resources and Life Sciences, 1190 Vienna, Austria)

  • David Wöss

    (Institute of Chemical and Energy Engineering, University of Naturel Resources and Life Sciences, 1190 Vienna, Austria)

  • Petra Steinmetz

    (VOIGT+WIPP Industrial Research GmbH, 1150 Vienna, Austria)

  • Werner Müller

    (Institute of Chemical and Energy Engineering, University of Naturel Resources and Life Sciences, 1190 Vienna, Austria)

  • Tobias Pröll

    (Institute of Chemical and Energy Engineering, University of Naturel Resources and Life Sciences, 1190 Vienna, Austria)

Abstract

In its “Sustainable and Smart Mobility Strategy”, the European Commission assumes a 90% reduction in traffic emissions by 2050. The decarbonisation of transport logistics as a major contributor to climate change is, therefore, indicated. There are major challenges in converting logistic transport processes to electric mobility. Currently, there is little available information for the conversion of entire fleets from fossil to electric fuel. One of the biggest challenges is the additional time needed for recharging. For the scheduling of entire logistics fleets, exact knowledge of the required loading times and loading quantities is essential. In this work, a parametrized continuous function is, therefore, defined to determine the essential parameters (recharging time, retrieved power, energy amounts) in HPC (high-power charging). These findings are particularly important for the deployment of multiple e-trucks in fleets, as logistics management depends on them. A simple function was constructed that can describe all phases of the charging process in a continuous function. Only the maximum power of the charging station, the size of the battery in the truck and the start SOC (state of charge) are required as parameters while using the function. The method described in this paper can make a significant contribution to the transformation towards electro-mobile urban logistics fleets. The required charging time, for example, is crucial for the planning and scheduling of e-logistics fleets and can be determined using the function described in this paper.

Suggested Citation

  • Thomas Märzinger & David Wöss & Petra Steinmetz & Werner Müller & Tobias Pröll, 2021. "Novel Modelling Approach for the Calculation of the Loading Performance of Charging Stations for E-Trucks to Represent Fleet Consumption," Energies, MDPI, vol. 14(12), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3471-:d:573561
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    References listed on IDEAS

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    1. Škugor, Branimir & Deur, Joško, 2016. "A bi-level optimisation framework for electric vehicle fleet charging management," Applied Energy, Elsevier, vol. 184(C), pages 1332-1342.
    2. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).
    3. Marmiroli, Benedetta & Venditti, Mattia & Dotelli, Giovanni & Spessa, Ezio, 2020. "The transport of goods in the urban environment: A comparative life cycle assessment of electric, compressed natural gas and diesel light-duty vehicles," Applied Energy, Elsevier, vol. 260(C).
    4. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
    5. Du, Jiuyu & Mo, Xinying & Li, Yalun & Zhang, Qun & Li, Jianqiu & Wu, Xiaogang & Lu, Languang & Ouyang, Minggao, 2019. "Boundaries of high-power charging for long-range battery electric car from the heat generation perspective," Energy, Elsevier, vol. 182(C), pages 211-223.
    6. Babaeiyazdi, Iman & Rezaei-Zare, Afshin & Shokrzadeh, Shahab, 2021. "State of charge prediction of EV Li-ion batteries using EIS: A machine learning approach," Energy, Elsevier, vol. 223(C).
    7. Ines Baccouche & Sabeur Jemmali & Bilal Manai & Noshin Omar & Najoua Essoukri Ben Amara, 2017. "Improved OCV Model of a Li-Ion NMC Battery for Online SOC Estimation Using the Extended Kalman Filter," Energies, MDPI, vol. 10(6), pages 1-22, May.
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    1. Doğukan Toktaş & M. Ali Ülkü & Muhammad Ahsanul Habib, 2024. "Toward Greener Supply Chains by Decarbonizing City Logistics: A Systematic Literature Review and Research Pathways," Sustainability, MDPI, vol. 16(17), pages 1-22, August.

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