IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i8p2055-d348044.html
   My bibliography  Save this article

An Algorithm for Optimization of Recharging Stops: A Case Study of Electric Vehicle Charging Stations on Canadian’s Ontario Highway 401

Author

Listed:
  • Andrea Stabile

    (Department of Energy, Politecnico di Milano, via La Masa 34, 20156 Milan, Italy)

  • Michela Longo

    (Department of Energy, Politecnico di Milano, via La Masa 34, 20156 Milan, Italy)

  • Wahiba Yaïci

    (Buildings and Renewables Group, CanmetENERGY Research Centre, Natural Resources Canada, 1 Haanel Drive, Ottawa, ON K1A 1M1, Canada)

  • Federica Foiadelli

    (Department of Energy, Politecnico di Milano, via La Masa 34, 20156 Milan, Italy)

Abstract

Electric vehicles (EVs), which have become a fundamental part of the automotive industry, were developed as part of concerted worldwide efforts to reduce dependency on fossil fuels due to their devastating effects on the environment. The aim of this study was to analyse a complete trip using an EV from Toronto to Ottawa (Canada) along Ontario’s Highway 401, considering that use of conventional vehicles powered by petrol or diesel allow one to make this trip without stops; using EVs, it is necessary to recharge the vehicle. For this purpose, an algorithm was developed for optimizing recharging stops during a complete trip. In particular, the simulations analysed the number of stops and specifically where it is possible to recharge taking into account the actual charging stations (CSs) located along the trip and the time of recharge during the stops as a function of the state of charge (SoC) of the vehicle. Using this approach, it was possible to evaluate the suitable coverage of the CSs on the stretch considered as well as to assess the main parameters that influence performance on the route.

Suggested Citation

  • Andrea Stabile & Michela Longo & Wahiba Yaïci & Federica Foiadelli, 2020. "An Algorithm for Optimization of Recharging Stops: A Case Study of Electric Vehicle Charging Stations on Canadian’s Ontario Highway 401," Energies, MDPI, vol. 13(8), pages 1-19, April.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2055-:d:348044
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/8/2055/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/8/2055/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chao-Tsung Ma, 2019. "System Planning of Grid-Connected Electric Vehicle Charging Stations and Key Technologies: A Review," Energies, MDPI, vol. 12(21), pages 1-22, November.
    2. Bryden, Thomas S. & Hilton, George & Cruden, Andrew & Holton, Tim, 2018. "Electric vehicle fast charging station usage and power requirements," Energy, Elsevier, vol. 152(C), pages 322-332.
    3. Xiang, Yue & Liu, Junyong & Li, Ran & Li, Furong & Gu, Chenghong & Tang, Shuoya, 2016. "Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates," Applied Energy, Elsevier, vol. 178(C), pages 647-659.
    4. Cesar Diaz-Londono & Luigi Colangelo & Fredy Ruiz & Diego Patino & Carlo Novara & Gianfranco Chicco, 2019. "Optimal Strategy to Exploit the Flexibility of an Electric Vehicle Charging Station," Energies, MDPI, vol. 12(20), pages 1-29, October.
    5. Guozhong Liu & Li Kang & Zeyu Luan & Jing Qiu & Fenglei Zheng, 2019. "Charging Station and Power Network Planning for Integrated Electric Vehicles (EVs)," Energies, MDPI, vol. 12(13), pages 1-22, July.
    6. Weiss, Martin & Patel, Martin K. & Junginger, Martin & Perujo, Adolfo & Bonnel, Pierre & van Grootveld, Geert, 2012. "On the electrification of road transport - Learning rates and price forecasts for hybrid-electric and battery-electric vehicles," Energy Policy, Elsevier, vol. 48(C), pages 374-393.
    7. Morris Brenna & Michela Longo & Wahiba Yaïci, 2017. "Modelling and Simulation of Electric Vehicle Fast Charging Stations Driven by High Speed Railway Systems," Energies, MDPI, vol. 10(9), pages 1-23, August.
    8. Huang, Pei & Ma, Zhenjun & Xiao, Longzhu & Sun, Yongjun, 2019. "Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities," Applied Energy, Elsevier, vol. 255(C).
    9. Luo, Lizi & Gu, Wei & Zhou, Suyang & Huang, He & Gao, Song & Han, Jun & Wu, Zhi & Dou, Xiaobo, 2018. "Optimal planning of electric vehicle charging stations comprising multi-types of charging facilities," Applied Energy, Elsevier, vol. 226(C), pages 1087-1099.
    10. He, Fang & Wu, Di & Yin, Yafeng & Guan, Yongpei, 2013. "Optimal deployment of public charging stations for plug-in hybrid electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 47(C), pages 87-101.
    11. Ren, Xianqiang & Zhang, Huiming & Hu, Ruohan & Qiu, Yueming, 2019. "Location of electric vehicle charging stations: A perspective using the grey decision-making model," Energy, Elsevier, vol. 173(C), pages 548-553.
    12. Dallinger, David & Gerda, Schubert & Wietschel, Martin, 2013. "Integration of intermittent renewable power supply using grid-connected vehicles – A 2030 case study for California and Germany," Applied Energy, Elsevier, vol. 104(C), pages 666-682.
    13. Wu, Tian & Ma, Lin & Mao, Zhonggen & Ou, Xunmin, 2015. "Setting up charging electric stations within residential communities in current China: Gaming of government agencies and property management companies," Energy Policy, Elsevier, vol. 77(C), pages 216-226.
    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. Pichamon Keawthong & Veera Muangsin & Chupun Gowanit, 2022. "Location Selection of Charging Stations for Electric Taxis: A Bangkok Case," Sustainability, MDPI, vol. 14(17), pages 1-23, September.
    2. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).

    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. Schmidt, Marc & Staudt, Philipp & Weinhardt, Christof, 2020. "Evaluating the importance and impact of user behavior on public destination charging of electric vehicles," Applied Energy, Elsevier, vol. 258(C).
    2. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    3. Duan, Ditao & Poursoleiman, Roza, 2021. "Modified teaching-learning-based optimization by orthogonal learning for optimal design of an electric vehicle charging station," Utilities Policy, Elsevier, vol. 72(C).
    4. Xie, Shiwei & Hu, Zhijian & Wang, Jueying & Chen, Yuwei, 2020. "The optimal planning of smart multi-energy systems incorporating transportation, natural gas and active distribution networks," Applied Energy, Elsevier, vol. 269(C).
    5. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    6. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).
    7. Sanchari Deb & Kari Tammi & Karuna Kalita & Pinakeswar Mahanta, 2018. "Review of recent trends in charging infrastructure planning for electric vehicles," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(6), November.
    8. Ferro, G. & Minciardi, R. & Robba, M., 2020. "A user equilibrium model for electric vehicles: Joint traffic and energy demand assignment," Energy, Elsevier, vol. 198(C).
    9. Lin, Haiyang & Bian, Caiyun & Wang, Yu & Li, Hailong & Sun, Qie & Wallin, Fredrik, 2022. "Optimal planning of intra-city public charging stations," Energy, Elsevier, vol. 238(PC).
    10. Geng, Lijun & Lu, Zhigang & He, Liangce & Zhang, Jiangfeng & Li, Xueping & Guo, Xiaoqiang, 2019. "Smart charging management system for electric vehicles in coupled transportation and power distribution systems," Energy, Elsevier, vol. 189(C).
    11. Hyung Tae Kim & Young Gyu Jin & Yong Tae Yoon, 2019. "An Economic Analysis of Load Leveling with Battery Energy Storage Systems (BESS) in an Electricity Market Environment: The Korean Case," Energies, MDPI, vol. 12(9), pages 1-16, April.
    12. Ji, Zhenya & Huang, Xueliang, 2018. "Plug-in electric vehicle charging infrastructure deployment of China towards 2020: Policies, methodologies, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 710-727.
    13. Qingyou Yan & Hua Dong & Meijuan Zhang, 2021. "Service Evaluation of Electric Vehicle Charging Station: An Application of Improved Matter-Element Extension Method," Sustainability, MDPI, vol. 13(14), pages 1-25, July.
    14. Kumar, Rajeev Ranjan & Guha, Pritha & Chakraborty, Abhishek, 2022. "Comparative assessment and selection of electric vehicle diffusion models: A global outlook," Energy, Elsevier, vol. 238(PC).
    15. Hassan S. Hayajneh & Xuewei Zhang, 2019. "Evaluation of Electric Vehicle Charging Station Network Planning via a Co-Evolution Approach," Energies, MDPI, vol. 13(1), pages 1-11, December.
    16. Panah, Payam Ghaebi & Bornapour, Mosayeb & Hemmati, Reza & Guerrero, Josep M., 2021. "Charging station Stochastic Programming for Hydrogen/Battery Electric Buses using Multi-Criteria Crow Search Algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    17. Zhou, Bo & Chen, Guo & Song, Qiankun & Dong, Zhao Yang, 2020. "Robust chance-constrained programming approach for the planning of fast-charging stations in electrified transportation networks," Applied Energy, Elsevier, vol. 262(C).
    18. Zhang, Yaoli & Liu, Xingyu & Wei, Wenshen & Peng, Tianji & Hong, Gang & Meng, Chao, 2020. "Mobile charging: A novel charging system for electric vehicles in urban areas," Applied Energy, Elsevier, vol. 278(C).
    19. Motoaki, Yutaka & Shirk, Matthew G., 2017. "Consumer behavioral adaption in EV fast charging through pricing," Energy Policy, Elsevier, vol. 108(C), pages 178-183.
    20. Ahmed Abdalrahman & Weihua Zhuang, 2017. "A Survey on PEV Charging Infrastructure: Impact Assessment and Planning," Energies, MDPI, vol. 10(10), pages 1-25, 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:13:y:2020:i:8:p:2055-:d:348044. 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.