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Planning of the Charging Station for Electric Vehicles Utilizing Cellular Signaling Data

Author

Listed:
  • Jianmin Jia

    (School of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Chenhui Liu

    (Civil, Construction and Environmental Engineering, Iowa State University, Ames, IA 50011, USA)

  • Tao Wan

    (Tianjin Urban Planning & Design Institute, Tianjin 300201, China)

Abstract

Electric Vehicles (EVs), by reducing the dependency on fossil fuel and minimizing the traffic-related pollutants emission, are considered as an effective component of a sustainable transportation system. However, the massive penetration of EVs brings a big challenge to the establishment of charging infrastructures. This paper presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data (CSD). Most previous work focused on the commute trips estimated from the number of jobs and households between traffic analysis zones (TAZs). This paper investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. The complete trip in a day for EV was reconstructed through merging the time sequenced trajectory derived from simulation. This paper proposed a two-step model that grouped the charging demand location into clusters and then identified the charging station site through optimization. The proposed approach was applied to investigate the charging behavior of medium-range EVs with Cellular Signaling Data collected from the China Unicom in Tianjin. The results indicate that over 50% of the charging stations are located within the central urban area. The developed approach could contribute to the planning of future charging stations.

Suggested Citation

  • Jianmin Jia & Chenhui Liu & Tao Wan, 2019. "Planning of the Charging Station for Electric Vehicles Utilizing Cellular Signaling Data," Sustainability, MDPI, vol. 11(3), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:3:p:643-:d:200916
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    References listed on IDEAS

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    Cited by:

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    2. He, Sylvia Y. & Kuo, Yong-Hong & Sun, Ka Kit, 2022. "The spatial planning of public electric vehicle charging infrastructure in a high-density city using a contextualised location-allocation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 21-44.
    3. Pokpong Prakobkaew & Somporn Sirisumrannukul, 2022. "Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data," Energies, MDPI, vol. 15(11), pages 1-19, May.
    4. Timothy Sands, 2020. "Electric Vehicle Sales Catastrophe Averted (?)," Modern Applied Science, Canadian Center of Science and Education, vol. 14(3), pages 1-1, March.
    5. Shuping Wu & Zan Yang, 2020. "Availability of Public Electric Vehicle Charging Pile and Development of Electric Vehicle: Evidence from China," Sustainability, MDPI, vol. 12(16), pages 1-14, August.
    6. Faping Wang & Rui Chen & Lixin Miao & Peng Yang & Bin Ye, 2019. "Location Optimization of Electric Vehicle Mobile Charging Stations Considering Multi-Period Stochastic User Equilibrium," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    7. Bhat, Furqan A. & Tiwari, Gaurav Yash & Verma, Ashish, 2024. "Preferences for public electric vehicle charging infrastructure locations: A discrete choice analysis," Transport Policy, Elsevier, vol. 149(C), pages 177-197.
    8. Pablo Tamay & Esteban Inga, 2022. "Charging Infrastructure for Electric Vehicles Considering Their Integration into the Smart Grid," Sustainability, MDPI, vol. 14(14), pages 1-21, July.

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