Optimal EV Charging Decisions Considering Charging Rate Characteristics and Congestion Effects

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2024-07-08 DOI:10.1109/TNSE.2024.3424443

Lihui Yi;Ermin Wei

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引用次数: 0

Abstract

With the rapid growth in demand for electric vehicles (EVs), corresponding charging infrastructures are expanding. These charging stations are located at various places with different congestion levels. EV drivers face an important decision in choosing between charging stations to reduce their overall time costs. However, existing literature either assumes a flat charging rate and hence overlooks the physical characteristics of an EV battery where charging rate is typically reduced as the battery charges, or ignores the effect of other drivers on an EV's decision making process. In this paper, we consider both the predetermined exogenous wait cost and the endogenous congestion induced by other drivers' strategic decisions, and propose a differential equation based approach to find the optimal strategies. We analytically characterize the equilibrium strategies and find that co-located EVs may make different decisions depending on the charging rate and/or remaining battery levels. Through numerical experiments, we investigate the impact of charging rate characteristics, modeling parameters and the consideration of endogenous congestion levels on the optimal charging decisions. Finally, we apply real-world data and find that some EV users with slower charging rates may benefit from the participation of fast-charging EVs.

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考虑充电率特征和拥堵效应的电动汽车充电最优决策

随着电动汽车（EV）需求的快速增长，相应的充电基础设施也在不断扩大。这些充电站分布在不同地点，拥堵程度各不相同。电动汽车驾驶员面临着在充电站之间进行选择的重要决策，以降低总体时间成本。然而，现有文献要么假设充电率是统一的，从而忽略了电动汽车电池的物理特性，即充电率通常会随着电池电量的增加而降低；要么忽略了其他驾驶员对电动汽车决策过程的影响。在本文中，我们既考虑了预定的外生等待成本，也考虑了由其他驾驶员的战略决策引起的内生拥堵，并提出了一种基于微分方程的方法来寻找最优策略。我们对均衡策略进行了分析，发现同地行驶的电动汽车可能会根据充电率和/或剩余电池电量做出不同的决策。通过数值实验，我们研究了充电率特征、建模参数和内生拥堵水平对最优充电决策的影响。最后，我们应用真实世界的数据，发现一些充电速度较慢的电动汽车用户可能会从快速充电电动汽车的参与中受益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.