Optimal charging guidance strategies for electric vehicles by considering dynamic charging requests in a time-varying road network

TL;DR

This paper develops optimal EV charging guidance strategies that account for dynamic charging requests and a time-varying road network, improving station reachability and service satisfaction.

Contribution

It introduces two novel strategies for EV charging guidance that consider travel demands and station operation impacts in a dynamic, time-varying environment.

Findings

Strategies ensure reachability in dynamic networks

Simulation shows improved charging station utilization

Recommendations for real-world application scenarios

Abstract

Electric vehicles (EVs) have enjoyed increasing adoption because of the global concerns about the petroleum dependence and greenhouse gas emissions. However, their limited driving range fosters the occurrence of charging requests deriving from EV drivers in urban road networks, which have significant uncertain characteristic from time dimension in the real-world situation. To tackle the challenge brought by the dynamic charging requests, this study is devoted to proposing optimal strategies to provide guidance for EV charging. The time-varying characteristic of road network is further involved in the problem formulation. Based on the charging request information, we propose two charging guidance strategies from different perspectives. One of the strategies considers the travel demands of EV drivers and uses the driving distance as the optimization criterion. In contrast, the other strategy focuses on the impacts of EV number on the charging station operation and service satisfaction. The reachable charging stations with minimum vehicle number are selected as the optimal ones. More importantly, both the strategies have the ability to ensure the reachability of selected charging stations in a time-varying road network. In addition, we conduct simulation examples to investigate the performance of the proposed charging guidance strategies. Besides, the insights and recommendations on application scenarios of the strategies are introduced according to the simulation results under various parameter scenarios.