A robust prediction model is developed for reliably estimating vehicular consumption. This model is distinguished from other models proposed so far for the following reasons: it detects the factors contributing into vehicular consumption, it applies machine learning functionality for approximating the nonlinearities and the specificities between the contributing factors, and it is capable of implicitly adapting to the characteristics of the vehicle, the road network and the contextual conditions through its learning process. The authors validated its efficiency by applying it on measurements collected during a data acquisition campaign, which was performed by a fully electric vehicle (FEV) in an urban road network.