IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i19p4775-d1484688.html
   My bibliography  Save this article

Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review

Author

Listed:
  • Jingzhe Hu

    (Engineering Training and Innovation Education Center, Shanghai Polytechnic University, Shanghai 201209, China)

  • Xu Wang

    (Key Laboratory of Control of Power Transmission and Conversion, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Shengmin Tan

    (State Grid Yangzhou Power Supply Company, Yangzhou 225100, China)

Abstract

Integrating electric vehicles (EVs) into the coupled power distribution network (PDN) and transportation network (TN) presents substantial challenges. This paper explores three key areas in EV integration: charging/discharging scheduling, charging navigation, and charging station planning. First, the paper discusses the features and importance of EV integrated traffic–power networks. Then, it examines key factors influencing EV strategy, such as user behavior, charging preferences, and battery performance. Next, the study establishes an EV charging and discharging model, with particular emphasis on the complexities introduced by factors such as pricing mechanisms and integration approaches. Furthermore, the charging navigation model and the role of real-time traffic information are discussed. Additionally, the paper highlights the importance of multi-type charging stations and the impact of uncertainty on charging station planning. The paper concludes by identifying significant challenges and potential opportunities for EV integration. Future research should focus on enhancing coupled network modeling, refining user behavior models, developing incentive pricing mechanisms, and advancing autonomous driving and automated charging technologies. Such efforts will be essential for achieving a sustainable and efficient EV ecosystem.

Suggested Citation

  • Jingzhe Hu & Xu Wang & Shengmin Tan, 2024. "Electric Vehicle Integration in Coupled Power Distribution and Transportation Networks: A Review," Energies, MDPI, vol. 17(19), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4775-:d:1484688
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/19/4775/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/19/4775/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kurani, Kenneth S., 2019. "The State of Electric Vehicle Markets, 2017: Growth Faces an Attention Gap," Institute of Transportation Studies, Working Paper Series qt9435h15r, Institute of Transportation Studies, UC Davis.
    2. Algafri, Mohammed & Alghazi, Anas & Almoghathawi, Yasser & Saleh, Haitham & Al-Shareef, Khaled, 2024. "Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming," Applied Energy, Elsevier, vol. 372(C).
    3. Aghajan-Eshkevari, Saleh & Ameli, Mohammad Taghi & Azad, Sasan, 2023. "Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems," Applied Energy, Elsevier, vol. 341(C).
    4. Zhan, Weipeng & Wang, Zhenpo & Deng, Junjun & Liu, Peng & Cui, Dingsong, 2024. "Integrating system dynamics and agent-based modeling: A data-driven framework for predicting electric vehicle market penetration and GHG emissions reduction under various incentives scenarios," Applied Energy, Elsevier, vol. 372(C).
    5. 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)

    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. Wei Dai & Zhihong Zeng & Cheng Wang & Zhijie Zhang & Yang Gao & Jun Xu, 2024. "Non-Iterative Coordinated Optimisation of Power–Traffic Networks Based on Equivalent Projection," Energies, MDPI, vol. 17(8), pages 1-21, April.
    15. 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.
    16. 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).
    17. 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).
    18. 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.
    19. 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.
    20. 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).

    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:17:y:2024:i:19:p:4775-:d:1484688. 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.