As the penetration of electric vehicles (EVs) continues to increase, one of the key issues facing EV users is the limitation of range and the lack of charging facilities. These issues have led to widespread mileage anxiety among EV users, who must consider the driving distance when choosing a travel path. To address this challenge, this study proposes a semi-dynamic traffic equilibrium-based EV charging decision-making method that can incorporate time-varying EV user demands in medium-long time scales into the framework of a coupled electricity-transportation system. First, this paper adopts a graph theoretic approach to model the traffic network, simulate real-world user travel patterns, and generate the corresponding charging demand. Then, a charging path selection strategy that takes into account the vehicle owner's perception error is constructed to optimize the overall user travel decision. Finally, the charging decision-making method proposed in this study effectively reduces the overall travel cost of users after experimental validation on a 24-node traffic benchmark road network. This study provides an innovative and effective solution for urban EV charging strategy, which is important for expanding EV usage and optimizing the distribution of smart charging facilities for EVs.
Spatial-Temporal Prediction of Electric Vehicle Charging Load Based on Multi-Time Step Semi-Dynamic Traffic Equilibrium
09.08.2024
711448 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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