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Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach

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  • Yang, Xiong
  • Peng, Zhenhan
  • Wang, Pinxi
  • Zhuge, Chengxiang

Abstract

Electric vehicle (EV) charging demand is an essential input of charging facility location models. However, charging demand may vary across seasons. In response, this paper first provided insights into the seasonal variance in charging demand using a unique GPS trajectory dataset which contained travel, parking, and charging information of 2,658 private EVs in Beijing. The dataset was collected in January, April, July, and October 2018, which were representative months in winter, spring, summer, and autumn, respectively. Through statistical and spatiotemporal analyses, we found that in winter, EVs got recharged when their state of charge (SOC) was lower: the average SOCs on working days were 51.96%, 48.39%, 50.86%, and 43.50%, in spring, summer, autumn, and winter, respectively. Furthermore, the central urban areas tended to have a higher charging demand in winter. To further explore how the seasonal variance in charging demand may influence infrastructure deployment, we used the classical p-median model to deploy charging facilities with the charging demands in the four seasons, considering the modifiable areal unit problem (MAUP). The results suggested that the seasonal variance did influence the layout of charging facilities under different spatial analysis units (SAUs). The deployment of charging facilities in the central urban areas and outer suburbs tended to be more sensitive to seasonal variance in charging demand. The findings are expected to be useful for charging infrastructure planning in both the transport and power sectors.

Suggested Citation

  • Yang, Xiong & Peng, Zhenhan & Wang, Pinxi & Zhuge, Chengxiang, 2023. "Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach," Energy, Elsevier, vol. 280(C).
    • Handle: RePEc:eee:energy:v:280:y:2023:i:c:s0360544223016249
    DOI: 10.1016/j.energy.2023.128230
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    1. Wenxia Liu & Shuya Niu & Huiting Xu & Xiaoying Li, 2016. "A New Method to Plan the Capacity and Location of Battery Swapping Station for Electric Vehicle Considering Demand Side Management," Sustainability, MDPI, vol. 8(6), pages 1-17, June.
    2. Metais, M.O. & Jouini, O. & Perez, Y. & Berrada, J. & Suomalainen, E., 2022. "Too much or not enough? Planning electric vehicle charging infrastructure: A review of modeling options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    3. Aleksandar Janjić & Lazar Velimirović & Jelena Velimirović & Petar Vranić, 2021. "Estimating the optimal number and locations of electric vehicle charging stations: the application of multi-criteria p-median methodology," Transportation Planning and Technology, Taylor & Francis Journals, vol. 44(8), pages 827-842, November.
    4. Joonho Ko & Daejin Kim & Daisik Nam & Taekyung Lee, 2017. "Determining locations of charging stations for electric taxis using taxi operation data," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(4), pages 420-433, May.
    5. Chengxiang Zhuge & Chunfu Shao & Xia Li, 2019. "Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China," Energies, MDPI, vol. 12(16), pages 1-21, August.
    6. Cui, Dingsong & Wang, Zhenpo & Liu, Peng & Wang, Shuo & Zhang, Zhaosheng & Dorrell, David G. & Li, Xiaohui, 2022. "Battery electric vehicle usage pattern analysis driven by massive real-world data," Energy, Elsevier, vol. 250(C).
    7. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    8. Naireeta Deb & Rajendra Singh & Richard R. Brooks & Kevin Bai, 2021. "A Review of Extremely Fast Charging Stations for Electric Vehicles," Energies, MDPI, vol. 14(22), pages 1-27, November.
    9. Yang, Xiong & Zhuge, Chengxiang & Shao, Chunfu & Huang, Yuantan & Hayse Chiwing G. Tang, Justin & Sun, Mingdong & Wang, Pinxi & Wang, Shiqi, 2022. "Characterizing mobility patterns of private electric vehicle users with trajectory data," Applied Energy, Elsevier, vol. 321(C).
    10. Mubarak, Mamdouh & Üster, Halit & Abdelghany, Khaled & Khodayar, Mohammad, 2021. "Strategic network design and analysis for in-motion wireless charging of electric vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    11. 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.
    12. Ramesh Chandra Majhi & Prakash Ranjitkar & Mingyue Sheng & Grant A. Covic & Doug James Wilson, 2021. "A systematic review of charging infrastructure location problem for electric vehicles," Transport Reviews, Taylor & Francis Journals, vol. 41(4), pages 432-455, July.
    13. Doucette, Reed T. & McCulloch, Malcolm D., 2011. "Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions," Applied Energy, Elsevier, vol. 88(7), pages 2315-2323, July.
    14. Zhang, Xudong & Zou, Yuan & Fan, Jie & Guo, Hongwei, 2019. "Usage pattern analysis of Beijing private electric vehicles based on real-world data," Energy, Elsevier, vol. 167(C), pages 1074-1085.
    15. Zhang, Li & Shaffer, Brendan & Brown, Tim & Scott Samuelsen, G., 2015. "The optimization of DC fast charging deployment in California," Applied Energy, Elsevier, vol. 157(C), pages 111-122.
    16. Wang, Ying-Wei & Lin, Chuah-Chih, 2009. "Locating road-vehicle refueling stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(5), pages 821-829, September.
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