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Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing

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  • Fescioglu-Unver, Nilgun
  • Yıldız Aktaş, Melike

Abstract

The majority of global road transportation emissions come from passenger and freight vehicles. Electric vehicles (EV) provide a sustainable transportation way, but customers’ charging service related concerns affect the EV adoption rate. Effective infrastructure planning, charge scheduling, charge pricing, and electric vehicle routing strategies can help relieve customer perceived risks. The number of studies using machine learning algorithms to solve these problems is increasing daily. Forecasting, clustering, and reinforcement based models are frequently the main solution methods or provide valuable inputs to other solution procedures. This study reviews the studies that apply machine learning models to improve EV charging service operations and provides future research directions.

Suggested Citation

  • Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:rensus:v:188:y:2023:i:c:s1364032123007311
    DOI: 10.1016/j.rser.2023.113873
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