Data Science and Management (Mar 2021)

Data analytics for optimizing extreme fast charging: a survey

  • Haibing Lu,
  • Xi Chen,
  • Cheng Fang,
  • Hua Yang

Journal volume & issue
Vol. 1, no. 1
pp. 23 – 31

Abstract

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Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy. Whereas the current market share of electric vehicles is still low, one main obstacle is no enough extreme fast charging, which requires high capital costs and strategic planning. Proper placement of extreme fast charging stations can maximize investment utility, promote public acceptance of electric vehicles, minimize adverse effects on transportation and power networks, and improve penetration market rate for electric vehicles. As such, the research on extreme fast charging station siting and sizing has received considerable attention in the past decade, where optimization modeling has become the main approach. This survey is to review various optimization models, examine their characteristics and evaluate their applications. We also discuss challenges/limitations of the optimization modeling approach for the extreme fast charging location planning, and point out future research directions. Our study can serve as a tutorial/reference to the extreme fast charging location optimization modeling, and attempts to attract more talents and research interests in this exciting field.

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