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Data-driven optimized layout of battery electric vehicle charging infrastructure

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  • Tao, Ye
  • Huang, Miaohua
  • Yang, Lan

Abstract

The work established a mathematical model to optimize the layout of charging infrastructure based on the real-world driving data of 196 battery electric vehicles in Wuhan. Two hundred and thirty-three candidate locations of the charging site were designated by analyzing these data. The mathematical model was implemented, using genetic algorithm with Matlab software. The life of power battery of battery electric vehicle was shortened under over discharge (state of charge below 20%). The optimization target was to reduce the amount of over discharge for a lower over discharge rate. Compared with the current charging points, the layout calculated by our model remarkably decreased the over discharge rate of the electric vehicles from 68.1% to 39.6% and 15.3% for slow and fast charging, respectively. Besides, we discussed the relationship among over discharge rate and the number of charging points, budget costs, as well as rated range. Moreover, the work have studied the connection of the number increase of charging points and retention rate. When the number of charging points increased from 60 to 220, the retention rate was 97% for slow charging and 95% for fast charging.

Suggested Citation

  • Tao, Ye & Huang, Miaohua & Yang, Lan, 2018. "Data-driven optimized layout of battery electric vehicle charging infrastructure," Energy, Elsevier, vol. 150(C), pages 735-744.
    • Handle: RePEc:eee:energy:v:150:y:2018:i:c:p:735-744
    DOI: 10.1016/j.energy.2018.03.018
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    Cited by:

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    4. Wang, Zhichao & Guo, Zhaomiao & Liu, Kai & Lin, Zhenhong & Li, Shunxi, 2024. "Multi-stage layout strategies of intercity charging and swapping infrastructure for electric logistics vehicles: random, radical, or conservative?," Transport Policy, Elsevier, vol. 153(C), pages 12-23.
    5. Ye Tao & Yupu Chen & Miaohua Huang & Lan Yang, 2023. "Data-Based Orderly Charging Strategy Considering Users’ Charging Choices," Energies, MDPI, vol. 16(19), pages 1-16, October.
    6. Zhao, Zhonghao & Lee, Carman K.M. & Huo, Jiage, 2023. "EV charging station deployment on coupled transportation and power distribution networks via reinforcement learning," Energy, Elsevier, vol. 267(C).
    7. 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.
    8. 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.
    9. Praveen Prakash Singh & Fushuan Wen & Ivo Palu & Sulabh Sachan & Sanchari Deb, 2022. "Electric Vehicles Charging Infrastructure Demand and Deployment: Challenges and Solutions," Energies, MDPI, vol. 16(1), pages 1-21, December.
    10. Ma, Shao-Chao & Fan, Ying, 2020. "A deployment model of EV charging piles and its impact on EV promotion," Energy Policy, Elsevier, vol. 146(C).
    11. Milan Straka & Pasquale De Falco & Gabriella Ferruzzi & Daniela Proto & Gijs van der Poel & Shahab Khormali & v{L}ubov{s} Buzna, 2019. "Predicting popularity of EV charging infrastructure from GIS data," Papers 1910.02498, arXiv.org.
    12. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M. & Lund, Peter D., 2019. "Energy integration and interaction between buildings and vehicles: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    13. Davidov, Sreten & Pantoš, Miloš, 2019. "Optimization model for charging infrastructure planning with electric power system reliability check," Energy, Elsevier, vol. 166(C), pages 886-894.
    14. Mingdong Sun & Chunfu Shao & Chengxiang Zhuge & Pinxi Wang & Xiong Yang & Shiqi Wang, 2022. "Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications," Transportation, Springer, vol. 49(5), pages 1409-1439, October.
    15. Tao, Ye & Huang, Miaohua & Chen, Yupu & Yang, Lan, 2020. "Orderly charging strategy of battery electric vehicle driven by real-world driving data," Energy, Elsevier, vol. 193(C).
    16. Golsefidi, Atefeh Hemmati & Hüttel, Frederik Boe & Peled, Inon & Samaranayake, Samitha & Pereira, Francisco Câmara, 2023. "A joint machine learning and optimization approach for incremental expansion of electric vehicle charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    17. Qing Li & Xue Li & Zuyu Liu & Yaping Qi, 2022. "Application of Clustering Algorithms in the Location of Electric Taxi Charging Stations," Sustainability, MDPI, vol. 14(13), pages 1-15, June.

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