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Optimizing the deployment of electric vehicle charging stations using pervasive mobility data

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  • Vazifeh, Mohammad M.
  • Zhang, Hongmou
  • Santi, Paolo
  • Ratti, Carlo

Abstract

With the recent advances in battery technology and the resulting decrease in the charging times, public charging stations are becoming a viable option for Electric Vehicle (EV) drivers. Concurrently, emergence and the wide-spread use of location-tracking devices in mobile phones and wearable devices has paved the way to track individual-level human movements to an unprecedented spatial and temporal grain. Motivated by these developments, we propose a novel methodology to perform data-driven optimization of EV charging station locations. We formulate the problem as a discrete optimization problem on a geographical grid, with the objective of covering the entire demand region while minimizing a measure of drivers’ total excess driving distance to reach charging stations, the related energy overhead, and the number of charging stations. Since optimally solving the problem is computationally infeasible, we present computationally efficient solutions based on the genetic algorithm. We then apply the proposed methodology to optimize EV charging stations layout in the city of Boston, starting from Call Detail Records (CDR) of one million users over the span of 4 months. The results show that the genetic algorithm provides solutions that significantly reduce drivers’ excess driving distance to charging stations, energy overhead, and the number of charging stations required compared to both a locally-optimized feasible solution and the current charging station deployment in the Boston metro area. We further investigate the robustness of the proposed methodology and show that building upon well-known regularity of aggregate human mobility patterns, the layout computed for demands based on the single day movements preserves its advantage also in later days and months. When collectively considered, the results presented in this paper indicate the potential of data-driven approaches for optimally placing public charging facilities at urban scale.

Suggested Citation

  • Vazifeh, Mohammad M. & Zhang, Hongmou & Santi, Paolo & Ratti, Carlo, 2019. "Optimizing the deployment of electric vehicle charging stations using pervasive mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 75-91.
    • Handle: RePEc:eee:transa:v:121:y:2019:i:c:p:75-91
    DOI: 10.1016/j.tra.2019.01.002
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    2. Andreas Dypvik Landmark & Petter Arnesen & Carl-Johan Södersten & Odd André Hjelkrem, 2021. "Mobile phone data in transportation research: methods for benchmarking against other data sources," Transportation, Springer, vol. 48(5), pages 2883-2905, October.
    3. Tommaso Schettini & Mauro dell’Amico & Francesca Fumero & Ola Jabali & Federico Malucelli, 2023. "Locating and Sizing Electric Vehicle Chargers Considering Multiple Technologies," Energies, MDPI, vol. 16(10), pages 1-16, May.
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    6. Abood Mourad & Martin Hennebel & Ahmed Amrani & Amira Ben Hamida, 2021. "Analyzing the Fast-Charging Potential for Electric Vehicles with Local Photovoltaic Power Production in French Suburban Highway Network," Energies, MDPI, vol. 14(9), pages 1-20, April.
    7. Tu, Wei & Santi, Paolo & Zhao, Tianhong & He, Xiaoyi & Li, Qingquan & Dong, Lei & Wallington, Timothy J. & Ratti, Carlo, 2019. "Acceptability, energy consumption, and costs of electric vehicle for ride-hailing drivers in Beijing," Applied Energy, Elsevier, vol. 250(C), pages 147-160.
    8. Rawan Shabbar & Anemone Kasasbeh & Mohamed M. Ahmed, 2021. "Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    9. 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).
    10. Woo, Hyeon & Son, Yongju & Cho, Jintae & Kim, Sung-Yul & Choi, Sungyun, 2023. "Optimal expansion planning of electric vehicle fast charging stations," Applied Energy, Elsevier, vol. 342(C).
    11. Scorrano, Mariangela & Danielis, Romeo & Giansoldati, Marco, 2020. "Dissecting the total cost of ownership of fully electric cars in Italy: The impact of annual distance travelled, home charging and urban driving," Research in Transportation Economics, Elsevier, vol. 80(C).
    12. Anastasios Tsakalidis & Andreea Julea & Christian Thiel, 2019. "The Role of Infrastructure for Electric Passenger Car Uptake in Europe," Energies, MDPI, vol. 12(22), pages 1-18, November.
    13. 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.
    14. 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).
    15. Afaq Ahmad & Muhammad Khalid & Zahid Ullah & Naveed Ahmad & Mohammad Aljaidi & Faheem Ahmed Malik & Umar Manzoor, 2022. "Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging," Energies, MDPI, vol. 15(24), pages 1-32, December.
    16. 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.
    17. Mikołaj Schmidt & Paweł Zmuda-Trzebiatowski & Marcin Kiciński & Piotr Sawicki & Konrad Lasak, 2021. "Multiple-Criteria-Based Electric Vehicle Charging Infrastructure Design Problem," Energies, MDPI, vol. 14(11), pages 1-34, May.

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