IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i12p3471-d573561.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/12/3471/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/12/3471/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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).
    7. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sajjad Haider & Peter Schegner, 2020. "Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid," Energies, MDPI, vol. 13(22), pages 1-19, November.
    2. Mattia Rapa & Laura Gobbi & Roberto Ruggieri, 2020. "Environmental and Economic Sustainability of Electric Vehicles: Life Cycle Assessment and Life Cycle Costing Evaluation of Electricity Sources," Energies, MDPI, vol. 13(23), pages 1-16, November.
    3. Shi You & Junjie Hu & Charalampos Ziras, 2016. "An Overview of Modeling Approaches Applied to Aggregation-Based Fleet Management and Integration of Plug-in Electric Vehicles †," Energies, MDPI, vol. 9(11), pages 1-18, November.
    4. Weitzel, Timm & Glock, Christoph H., 2018. "Energy management for stationary electric energy storage systems: A systematic literature review," European Journal of Operational Research, Elsevier, vol. 264(2), pages 582-606.
    5. Prarthana Pillai & Sneha Sundaresan & Pradeep Kumar & Krishna R. Pattipati & Balakumar Balasingam, 2022. "Open-Circuit Voltage Models for Battery Management Systems: A Review," Energies, MDPI, vol. 15(18), pages 1-25, September.
    6. Guwen Tang & Meng Zhang & Fei Bu, 2023. "Vehicle Environmental Efficiency Evaluation in Different Regions in China: A Combination of the Life Cycle Analysis (LCA) and Two-Stage Data Envelopment Analysis (DEA) Methods," Sustainability, MDPI, vol. 15(15), pages 1-24, August.
    7. Md Ohirul Qays & Yonis Buswig & Md Liton Hossain & Ahmed Abu-Siada, 2020. "Active Charge Balancing Strategy Using the State of Charge Estimation Technique for a PV-Battery Hybrid System," Energies, MDPI, vol. 13(13), pages 1-16, July.
    8. Xia, Bizhong & Cui, Deyu & Sun, Zhen & Lao, Zizhou & Zhang, Ruifeng & Wang, Wei & Sun, Wei & Lai, Yongzhi & Wang, Mingwang, 2018. "State of charge estimation of lithium-ion batteries using optimized Levenberg-Marquardt wavelet neural network," Energy, Elsevier, vol. 153(C), pages 694-705.
    9. Maxwell Woody & Michael T. Craig & Parth T. Vaishnav & Geoffrey M. Lewis & Gregory A. Keoleian, 2022. "Optimizing future cost and emissions of electric delivery vehicles," Journal of Industrial Ecology, Yale University, vol. 26(3), pages 1108-1122, June.
    10. García-Villalobos, J. & Zamora, I. & Knezović, K. & Marinelli, M., 2016. "Multi-objective optimization control of plug-in electric vehicles in low voltage distribution networks," Applied Energy, Elsevier, vol. 180(C), pages 155-168.
    11. Simone Barcellona & Lorenzo Codecasa & Silvia Colnago & Luigi Piegari, 2023. "Calendar Aging Effect on the Open Circuit Voltage of Lithium-Ion Battery," Energies, MDPI, vol. 16(13), pages 1-16, June.
    12. Yang, Zijun & Wang, Bowen & Jiao, Kui, 2020. "Life cycle assessment of fuel cell, electric and internal combustion engine vehicles under different fuel scenarios and driving mileages in China," Energy, Elsevier, vol. 198(C).
    13. Matteo Prussi & Lorenzo Laveneziana & Lorenzo Testa & David Chiaramonti, 2022. "Comparing e-Fuels and Electrification for Decarbonization of Heavy-Duty Transports," Energies, MDPI, vol. 15(21), pages 1-17, October.
    14. Shi, Lei & Wu, Rongxin & Lin, Boqiang, 2023. "Where will go for electric vehicles in China after the government subsidy incentives are abolished? A controversial consumer perspective," Energy, Elsevier, vol. 262(PA).
    15. Gonzalez Venegas, Felipe & Petit, Marc & Perez, Yannick, 2021. "Active integration of electric vehicles into distribution grids: Barriers and frameworks for flexibility services," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    16. Hong, Wanshi & Jenn, Alan & Wang, Bin, 2023. "Electrified autonomous freight benefit analysis on fleet, infrastructure and grid leveraging Grid-Electrified Mobility (GEM) model," Applied Energy, Elsevier, vol. 335(C).
    17. IqtiyaniIlham, Nur & Hasanuzzaman, M. & Hosenuzzaman, M., 2017. "European smart grid prospects, policies, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 776-790.
    18. Hoarau, Quentin & Perez, Yannick, 2018. "Interactions between electric mobility and photovoltaic generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 510-522.
    19. de la Torre, S. & Aguado, J.A. & Sauma, E., 2023. "Optimal scheduling of ancillary services provided by an electric vehicle aggregator," Energy, Elsevier, vol. 265(C).
    20. Andrea Temporelli & Paola Cristina Brambilla & Elisabetta Brivio & Pierpaolo Girardi, 2022. "Last Mile Logistics Life Cycle Assessment: A Comparative Analysis from Diesel Van to E-Cargo Bike," Energies, MDPI, vol. 15(20), pages 1-18, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3471-:d:573561. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.