A Hierarchical Navigation Strategy of EV Fast Charging Based on Dynamic Scene

Abstract

With the development of the EV industry, the growing demand for EV fast charging load has brought enormous impact on the power system. Due to the unbalance of charging time distribution and the high power of EV fast charging, the difference between peak and valley of the load curve will widen. In this paper, a hierarchical navigation strategy (HNS) based on dynamic traffic/temperature data is proposed to decrease the EV fast charging load at peak hours and the time and energy cost during the charging process. The upper layer of the HNS is charging time selection. The optimal selections of charging time, which is based on the habits of EV users, is proposed in this layer. It aims at providing efficient time slots for charging, which can decentralize the fast charging demand and decrease the EV users’ time cost. The underlayer is the route selection layer, which is based on the priority coding genetic algorithm. It proposes the optimal charging routes to decrease EV users’ energy cost and time cost. At the same time, the peak charging load can also be shaved due to the decline in energy cost. The case study under the scene with realistic traffic, temperature, and power grid information shows that the proposed HNS can shave the peak load of the power grid and decrease the energy/time cost during the EV fast charging process. Therefore, the effectiveness of the HNS is proved.