A data-driven scheduling module for electric vehicle charging - Chen, Henry; Lambrinos, Lambros; Grammenos, Ryan; Karagiannis, Konstantinos; Kfoury, Elie (My University, 2024)
The continuing uprise in the number of electric vehicles in many countries is undoubtedly contributing to the reduction of transportation-related emissions. However, a worrying consequence of this uprise is the additional demand placed on the electricity grid for vehicle charging which often coincides with the time period where household demand for electricity is high. To alleviate this effect, we propose an intelligent data-driven scheduling module for electric vehicle charging. Our goal is to satisfy the needs of electric vehicle owners in terms of the predicted vehicle usage and charging cost while limiting the impact of electric vehicle charging on the electricity grid. One of the most significant inputs to such a system is the availability of vehicle and user drive cycle data. Utilising such data, the scheduling module proposed in this paper demonstrates that deploying intelligent electric vehicle charging recommendation systems can enhance user experience and contribute to a more efficient and sustainable energy ecosystem.
Keywords: Schedules; Schedules; Ecosystems; User experience; Power grids; Internet; State of charge; Security; Recommender systems; Smart charging