IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i5p1324-d143115.html
   My bibliography  Save this article

Reserving Charging Decision-Making Model and Route Plan for Electric Vehicles Considering Information of Traffic and Charging Station

Author

Listed:
  • Haoming Liu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Wenqian Yin

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Xiaoling Yuan

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Man Niu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

With the advance of battery energy technology, electric vehicles (EV) are catching more and more attention. One of the influencing factors of electric vehicles large-scale application is the availability of charging stations and convenience of charging. It is important to investigate how to make reserving charging strategies and ensure electric vehicles are charged with shorter time and lower charging expense whenever charging request is proposed. This paper proposes a reserving charging decision-making model for electric vehicles that move to certain destinations and need charging services in consideration of traffic conditions and available charging resources at the charging stations. Besides, the interactive mechanism is described to show how the reserving charging system works, as well as the rolling records-based credit mechanism where extra charges from EV is considered to hedge default behavior. With the objectives of minimizing driving time and minimizing charging expenses, an optimization model with two objective functions is formulated. Then the optimizations are solved by a K shortest paths algorithm based on a weighted directed graph, where the time and distance factors are respectively treated as weights of corresponding edges of transportation networks. Case studies show the effectiveness and validity of the proposed route plan and reserving charging decision-making model.

Suggested Citation

  • Haoming Liu & Wenqian Yin & Xiaoling Yuan & Man Niu, 2018. "Reserving Charging Decision-Making Model and Route Plan for Electric Vehicles Considering Information of Traffic and Charging Station," Sustainability, MDPI, vol. 10(5), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1324-:d:143115
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/5/1324/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/5/1324/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Montoya, Alejandro & Guéret, Christelle & Mendoza, Jorge E. & Villegas, Juan G., 2017. "The electric vehicle routing problem with nonlinear charging function," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 87-110.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Junpeng Cai & Dewang Chen & Shixiong Jiang & Weijing Pan, 2020. "Dynamic-Area-Based Shortest-Path Algorithm for Intelligent Charging Guidance of Electric Vehicles," Sustainability, MDPI, vol. 12(18), pages 1-20, September.
    2. Li Zhang & Ke Gong & Maozeng Xu, 2019. "Congestion Control in Charging Stations Allocation with Q-Learning," Sustainability, MDPI, vol. 11(14), pages 1-11, July.
    3. Anders F. Jensen & Thomas K. Rasmussen & Carlo G. Prato, 2020. "A Route Choice Model for Capturing Driver Preferences When Driving Electric and Conventional Vehicles," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    4. Witt Andreas, 2023. "Determination of the Number of Required Charging Stations on a German Motorway Based on Real Traffic Data and Discrete Event-Based Simulation," LOGI – Scientific Journal on Transport and Logistics, Sciendo, vol. 14(1), pages 1-11, January.

    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. Zhou, Yu & Meng, Qiang & Ong, Ghim Ping, 2022. "Electric Bus Charging Scheduling for a Single Public Transport Route Considering Nonlinear Charging Profile and Battery Degradation Effect," Transportation Research Part B: Methodological, Elsevier, vol. 159(C), pages 49-75.
    2. Ana Bricia Galindo-Muro & Riccardo Cespi & Stephany Isabel Vallarta-Serrano, 2023. "Applications of Electric Vehicles in Instant Deliveries," Energies, MDPI, vol. 16(4), pages 1-18, February.
    3. Gitae Kim, 2024. "Electric Vehicle Routing Problem with States of Charging Stations," Sustainability, MDPI, vol. 16(8), pages 1-17, April.
    4. Alberto Ponso & Angelo Bonfitto & Giovanni Belingardi, 2023. "Route Planning for Electric Vehicles Including Driving Style, HVAC, Payload and Battery Health," Energies, MDPI, vol. 16(12), pages 1-22, June.
    5. Raeesi, Ramin & Zografos, Konstantinos G., 2020. "The electric vehicle routing problem with time windows and synchronised mobile battery swapping," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 101-129.
    6. Cortés-Murcia, David L. & Prodhon, Caroline & Murat Afsar, H., 2019. "The electric vehicle routing problem with time windows, partial recharges and satellite customers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 130(C), pages 184-206.
    7. Alberto Ceselli & Ángel Felipe & M. Teresa Ortuño & Giovanni Righini & Gregorio Tirado, 2021. "A Branch-and-Cut-and-Price Algorithm for the Electric Vehicle Routing Problem with Multiple Technologies," SN Operations Research Forum, Springer, vol. 2(1), pages 1-33, March.
    8. Maximilian Schiffer & Michael Schneider & Grit Walther & Gilbert Laporte, 2019. "Vehicle Routing and Location Routing with Intermediate Stops: A Review," Transportation Science, INFORMS, vol. 53(2), pages 319-343, March.
    9. Sina Abbasi & Maryam Moosivand & Ilias Vlachos & Mohammad Talooni, 2023. "Designing the Location–Routing Problem for a Cold Supply Chain Considering the COVID-19 Disaster," Sustainability, MDPI, vol. 15(21), pages 1-24, October.
    10. Gansterer, Margaretha & Födermayr, Patrick & Hartl, Richard F., 2021. "The capacitated multi-level lot-sizing problem with distributed agents," International Journal of Production Economics, Elsevier, vol. 235(C).
    11. Feifeng Zheng & Zhaojie Wang & Ming Liu, 2022. "Overnight charging scheduling of battery electric buses with uncertain charging time," Operational Research, Springer, vol. 22(5), pages 4865-4903, November.
    12. Raeesi, Ramin & Zografos, Konstantinos G., 2022. "Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping," European Journal of Operational Research, Elsevier, vol. 301(1), pages 82-109.
    13. Azra Ghobadi & Mohammad Fallah & Reza Tavakkoli-Moghaddam & Hamed Kazemipoor, 2022. "A Fuzzy Two-Echelon Model to Optimize Energy Consumption in an Urban Logistics Network with Electric Vehicles," Sustainability, MDPI, vol. 14(21), pages 1-31, October.
    14. Koyuncu, Işıl & Yavuz, Mesut, 2019. "Duplicating nodes or arcs in green vehicle routing: A computational comparison of two formulations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 605-623.
    15. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    16. Lai, Kexing & Chen, Tao & Natarajan, Balasubramaniam, 2020. "Optimal scheduling of electric vehicles car-sharing service with multi-temporal and multi-task operation," Energy, Elsevier, vol. 204(C).
    17. Shen, Zuo-Jun Max & Feng, Bo & Mao, Chao & Ran, Lun, 2019. "Optimization models for electric vehicle service operations: A literature review," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 462-477.
    18. Park, Hyunwoo & Lee, Chungmok, 2024. "An exact algorithm for maximum electric vehicle flow coverage problem with heterogeneous chargers, nonlinear charging time and route deviations," European Journal of Operational Research, Elsevier, vol. 315(3), pages 926-951.
    19. Dönmez, Sercan & Koç, Çağrı & Altıparmak, Fulya, 2022. "The mixed fleet vehicle routing problem with partial recharging by multiple chargers: Mathematical model and adaptive large neighborhood search," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    20. Wu, Guoyuan & Peng, Dongbo & Boriboonsomsin, Kanok, 2024. "Developing an Efficient Dispatching Strategy to Support Commercial Fleet Electrification," Institute of Transportation Studies, Working Paper Series qt2qz0n2gv, Institute of Transportation Studies, UC Davis.

    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:jsusta:v:10:y:2018:i:5:p:1324-:d:143115. 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.