EcoGem claims that the success and user acceptability of Fully Electric Vehicles (FEVs) will predominantly depend on their electrical energy consumption rate and the corresponding degree of autonomy that they can offer.EcoGem aims at providing efficient ICT-based solutions to this great issue, by designing and developing a FEV-oriented highly-innovative Advanced Driver Assistance System (ADAS), equipped with suitable monitoring, learning, reasoning and management capabilities that will help increase the FEV's autonomy and energy efficiency.EcoGem will base its approach on rendering the FEV:(i)\tcapable of reaching the desired destinations through the most energy efficient routes possible;(ii)\tfully aware of surrounding recharging points/stations while on move.To achieve its goals, EcoGem will develop and employ novel techniques:(i)\ton-going learning-based traffic prediction;(ii)\toptimised route planning;(iii)\tinteractive and inter-operative traffic, fleet and recharging management via V2V and V2I interfaces and communication.EcoGem's key-objective is to infuse intelligence and learning functionalities to on-board systems, enabling autonomous as well as interactive learning through V2X interfacing. EcoGem vehicles will learn over time to predict (and thus avoid) congested routes, based on experience that they gather. This learning process will eventually render each EcoGem FEV capable of autonomously classifying routes according to their degree of congestion, enabling energy-driven route planning optimisation.The EcoGem ADAS will additionally cater for the complete planning of the vehicle's recharging strategy. This optimisation process will typically include automated battery monitoring and various levels of pro-activeness, optimised scheduling according to several parameters (battery levels, energy consumption rate, desired destination, present location, daytime, traffic, user agenda, etc.), and real-time booking of recharging points.