Abstract:

In recent years, the ubiquitous influence of E-mobility, especially electrical vehicles (EVs), has been considered in the electrical power system in which CO2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; none address the total energy consumed by EVs in a city. This situation motivated the authors to develop a novel estimation model in the big-data regime to calculate EVs’ total energy consumption for any desired time interval. This article's main contribution is the identification of demand patterns at any time intervals (day, month, or year) with any desired level of accuracy to adjust the power generation’s schedules and prevent any disturbances due to the consequences of charging a massive amount of EVs simultaneously. The proposed model successfully handled 100 million records of real-world taxi routes and weather condition datasets, demonstrating that energy consumptions are highly correlated to the weekdays’ traffic flow.