Abstract
Electric vehicles (EVs) have become an efficient solution to making a transportation system environmentally friendly. However, as the number of EVs grows, the power demand from charging vehicles increases greatly. An unordered charging strategy for huge EVs affects the stability of a local power grid, especially during peak times. It becomes serious under the rapid charging mode, in which the EVs will be charged fully within a shorter time. In contrast to regular charging, the power quality (e.g.,voltages deviation, harmonic distortion) is affected when multiple EVs perform rapid charging at the same station simultaneously. To reduce the impacts on a power grid system caused by rapid charging, we propose an optimal EV rapid charging navigation strategy based on the internet of things network. The rapid charging price is designed based on the charging power regulation scheme. Both power grid operation and real-time traffic information are considered. The formulated objective of the navigation strategy is proposed to minimize the synthetic costs of EVs, including the traveling time and the charging costs. Simulation results demonstrate the effectiveness of the proposed strategy.
Highlights
With the increasing concern about environmental protection and the energy supply problems, more attention has been focused on the development of electric vehicles (EVs) [1]
We propose an intelligent transport system framework for the rapid charging EV system, the data collection and transmission are processed in the distributed computing manner
We propose an intelligent transport system framework for our optimal charging navigation strategy, while the traffic and power grid operation information are considered
Summary
With the increasing concern about environmental protection and the energy supply problems, more attention has been focused on the development of electric vehicles (EVs) [1]. We develop an optimal EV navigation strategy under the rapid charging mode, while the synthetic costs, including the traveling time and charging expenses are minimized. The rapid charging stations regulate the charging power of each charging pole dynamically according to the power grid operation information, and adjust their charging prices base on TOU tariffs and the queuing number of EVs. During the driving process of EVs, the charging navigation path is updated in real time, according to the power grid status and the traffic conditions. To implement our optimal charging navigation strategy, we use the charging power regulation scheme to reduce the influence for power grid, and charging price adjustment scheme to balance the number of EVs at each rapid charging station.
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