EcoCharge: A Framework for Sustainable Electric Vehicles Charging

In this demonstration paper, we present an innovative framework for sustainable Electric Vehicles (EVs) charging, dubbed EcoCharge, which utilizes an intelligent energy hoarding approach. Particularly, EcoCharge employs a Continuous k-Nearest Neighbor query, where the distance function is computed using Estimated Components (ECs) (i.e., a query we term CkNN-EC). An EC defines a function that can have a fuzzy value based on some estimates. Specific ECs used in this work are: (i) the (available clean) power at the charger, which depends on the estimated weather; (ii) the charger availability, which depends on the estimated busy timetables that show when the charger is crowded; and (iii) the derouting cost, which is the time to reach the charger depending on estimated traffic. Our framework combines these multiple non-conflicting objectives into an optimization task providing user-defined ranking means through an intuitive spatial application. The algorithm utilizes lower and upper interval values derived from ECs to recommend the top ranked EV chargers and present them through a map interface to users. We demonstrate EcoCharge using a complete prototype system developed using the Leaflet - OpenStreetMap library. In our demonstration scenario, attendees will have the opportunity to observe through mobile devices the benefits of EcoCharge by simulating its execution over various scheduled trips with real data retrieved from API requests (i.e., ECs).