Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach

作者：Hani Pourvaziri, Hassan Sarhadi, Nader Azad, Hamid Afshari, Majid Taghavi · 发表于：Transportation Research Part E Logistics and Transportation Review · 年份：2024 · DOI：10.1016/j.tre.2024.103568 · 被引用次数：116 · 研究领域：Electric Vehicles and Infrastructure、Advanced Battery Technologies Research、Transportation and Mobility Innovations

This study presents a hybrid solution for the charging station location-capacity problem. The proposed approach simultaneously determines the location and capacity of charging stations (i.e., number of charging piles), and assigns piles to electric vehicles based on their level of charge. The problem is formulated as a bi-objective mixed-integer nonlinear programming model to minimize the total cost of establishing charging stations together with the average customers’ waiting time. The proposed solution combines queueing theory with mathematical modelling to estimate the average waiting time. A deep learning algorithm is then developed to enhance the precision of waiting time estimation. Another contribution is involving a deep neural network model in improving NSGA-II algorithm. Numerical experiments are conducted in Halifax, Canada to assess the performance of the proposed framework. The results demonstrate the strong predictive performance of the deep learning algorithm and highlight the limitations of traditional queueing models in estimating waiting times in charging stations (i.e., 99.8% improvement in computation time, as well as accuracy improvement of time estimations from 13% to 1.6% deviation). Several valuable insights are obtained to improve the operational performance of charging stations such as achieving a significant (i.e., 61.5%) drop in the average waiting time across the network by a modest (i.e., 29.2%) increase in the initial investments. Also, it revea...