Driving preference analysis and electricity pricing strategy comparison for electric vehicles in smart city

Abstract With the increasing population density and relatively limited space and resources, cities are becoming more intelligent to provide adequate provision of services for the inhabitants. The utilization of the Internet-of-Things and Edge-of-Things technologies presents a significant foundation for the development of smart cities where intelligent transportation system is one of the most important applications. Due to the obvious advantages of reducing energy consumption and carbon emissions, electric vehicles are playing an increasingly important role in the intelligent transportation system. However, there is no shared framework for the interaction between electric vehicles and intelligent transportation system in smart cities. To handle this issue, this work proposes a practical framework to collect trajectory data of electric vehicles via edge devices and use a novel modified dynamic time warping method to analyze drivers’ preference. The analysis based on real data shows that a certain percentage of electric vehicle drivers have driving preference. That is, they tend to go through specific routes or locations during commuting. Furthermore, a few simulation experiments are conducted to compare the system performance between the time-of-use and load-of-use pricing strategies of the charging stations. The results demonstrate that the load-of-use pricing strategy can effectively divert the traffic flow and balance the load differences between different charging stations.