A novel navigation and charging strategy for electric vehicles based on customer classification in power-traffic network

• Published in: International journal of electrical power & energy systems Vol. 158; p. 109931
• Main Authors: Zhang, Hengrong, Qiu, Jing
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2024; Elsevier
• Subjects: Customer classification; Elastic charging strategy; Load variance; Navigation of electric vehicles; Queuing theory; Queuing theory; Navigation of electric vehicles; Customer classification; Load variance; Elastic charging strategy
• ISSN: 0142-0615; 1879-3517
• DOI: 10.1016/j.ijepes.2024.109931

With electric vehicles getting increasingly popular, there has been a lot of interest in encouraging future electric vehicle development in terms of greater charging efficiency, greater benefits to the environment, and greater reliability. One of the most prominent research is on the scheduling of electric vehicle charging and navigation. High-quality navigation and charging scheduling strategy must depend on the state of the coupled network between the transportation and power networks, while the outcomes of scheduling can also have a significant impact on these networks. In this paper, we propose a novel dynamic navigation and charging strategy that fully considers the coupled traffic and power distribution network. Firstly, electric vehicles with similar travel and charging requirements are classified. Secondly, an elastic scheme including fixed route navigation and flexible charging scheduling at charging stations is provided. We also propose an information integration framework for the implementation of electric vehicle routing and charging scheduling, information interaction between sectors including the status of heterogeneous electric vehicles, day-ahead power scheduling, and base load demands. The novel strategy aims at optimal electric vehicle navigation and charging at the system level achieving a lower overall travel cost, charging cost, carbon emission, and load variance of the power distribution network. Classification algorithms, stochastic dynamic programming, and queuing theory are used for mathematical modeling. Numerical results demonstrate the effectiveness of the proposed novel strategy for the navigation and charging scheduling of electric vehicles. •A novel navigation and charging strategy for electric vehicles is proposed.•A framework is proposed to integrate optimized information.•Electric vehicles with similar travel and charging requirements can be classified.•The elastic navigation and charging scheme are customized.•The proposed strategy effectively navigates electric vehicle grouping.