The rapid shift toward Battery Electric Vehicles (BEVs) makes every consumed kilowatt-hour critical: on the same route, a sporty driving style can drain the State of Charge (SoC) far faster than an eco approach, with direct repercussions on cost and emissions. This thesis demonstrates that a coordinated set of algorithms for mission optimization, real-time monitoring, and adaptive coaching can steer drivers toward more efficient behaviors, substantially cutting energy demand without sacrificing comfort or increasing travel time. The mission-optimization algorithm, developed in MATLAB/Simulink, computes once per mission an energy-optimal speed profile. An adaptability layer automatically re-tunes the cost weights according to comfort metrics extracted from the driver’s history, keeping the profile consistent with individual habits.