车辆路径问题
调度(生产过程)
聚类分析
电动汽车
强化学习
运输工程
运筹学
控制(管理)
服务(商务)
计算机科学
布线(电子设计自动化)
工程类
业务
运营管理
营销
人工智能
计算机网络
功率(物理)
物理
量子力学
作者
Nilgun Fescioglu-Unver,Melike Yıldız Aktaş
标识
DOI:10.1016/j.rser.2023.113873
摘要
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.
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