Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10892
Title: Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing
Authors: Fescioglu-Unver, N.
Yıldız, Aktaş, M.
Keywords: Charge scheduling
Charging service operations
Electric vehicle
Infrastructure planning
Machine learning
Pricing
Routing
Charging (batteries)
Electric vehicles
Learning algorithms
Reinforcement learning
Vehicle routing
Charge scheduling
Charging service operation
Electric vehicle charging
Infrastructure planning
Machine learning applications
Machine-learning
Planning controls
Road transportation
Routings
Service operations
Costs
Issue Date: 2023
Publisher: Elsevier Ltd
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. © 2023 Elsevier Ltd
URI: https://doi.org/10.1016/j.rser.2023.113873
https://hdl.handle.net/20.500.11851/10892
ISSN: 1364-0321
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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