Energy consumption of electric vehicles: models’ estimation using big data (FCD)

The paper presents a framework to estimate energy consumption of Electric Vehicles (EVs) by combining: (a) the use of models derived from traffic flow theory and from mechanics of locomotion and (b) the great amount of Floating Cara Data (FCD) from available Information and Communications Technology (ICT) devices. Existing energy consumption models may be classified into aggregate vs. disaggregate, according to the level of aggregation of variables related to driver, vehicle, and infrastructure. The proposed models have a hybrid nature: the aggregate component allows to estimating the values of vehicular speed and acceleration on a road link; the disaggregate one allows to estimating the discrete variability of EVs’ energy consumption inside a spatial-temporal domain. The energy consumption models are estimated using traffic data extracted from FCD. The proposed framework is structured into four steps: FCD processing, estimation of vehicular speeds and accelerations, estimation of resistance/energy consumption. The framework is applied in a pilot study area, composed by the backward (sub-)urban area of the port of “Porto delle Grazie” of Roccella Jonica (South of Italy). The preliminary results show that the methodology allows relative inexpensive and accurate calculation of EVs’ energy consumption and that it can be integrated into Intelligent Transportation System (ITS) applications.