A Framework for Continuous kNN Ranking of EV Chargers with Estimated Components

In this paper, we present an innovative framework whose objective is to allow drivers to recharge their Electric Vehicles (EVs) from the most environmentally friendly chargers using an intelligent hoarding approach. These chargers maximize renewable (e.g., solar) self-consumption, minimizing this way CO2 production and also the need for expensive stationary batteries on the electricity grid to store renewable energy that cannot be used otherwise. We model our problem as 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. We devise the EcoCharge framework that combines these multiple non-conflicting objectives into an optimization task providing user-defined ranking means through an intuitive mobile GIS application. Particularly, our core algorithm uses lower and upper values derived from the ECs to recommend the top ranked EV chargers and present them through an intuitive map user interface to users. Our experimental evaluation with extensive synthetic and real traces from Germany, China, and USA along with EV charger data from Plugshare shows that EcoCharge meets the objective functions in an efficient manner, allowing continuous recomputation on the edge devices (e.g., Android Automotive OS, Android Auto or Apple Carplay).