A Sensory Similarities Approach to Load Disaggregation of Charging Stations in Internet of Electric Vehicles

Intelligent transportation systems (ITSs) have become popular in recent years as an essential requirement for safer and more efficient transportation systems. Internet of Electric vehicles (IoEV) as well as their hybrid forms provide an ideal means of supporting sustainability within an ITS. The control of charging/discharging of EV is still a challenge, despite the tremendous research progress to date in the field. In this paper, the use of charging station data and binary vectorization are proposed in order to provide timely insights on the dynamic behavior of charging processes. A Bag-of-Power-States model has been created for similarity measurement of charging stations within given time periods. The results of experimentations using synthetic data have shown that the proposed Bag-of-Power-States model is computationally feasible and provides useful results for optimizing the scheduling of power supply to charging stations that may be located across a wide range of distances, over the same period of time.