Current vehicles, especially the electric ones, are complex mechatronic devices. The pickup vehicles of small sizes are currently used in transport considerably. They often operate within a repeating scheme of a limited variety of tracks and larger fleets. Thanks to mechatronic design of vehicles and their components and availability of high capacity data connection with computational centers (clouds), there are many means to optimize their performance, both by planning prior the trip and recalculations during the route.Although many aspects of this opportunity were already addressed, the paper shows an approach developed to further increase the range of e-vehicle operation. It is based on prior information about the route profile, traffic density, road conditions, past behaviour, mathematical models of the route, vehicle and dynamic optimization. The most important part of the procedure is performed in the cloud, using both computational power and rich information resources. Suitable route discretization into sections is most important part of the algorithm. The various information resources are used. Accumulated experience coming from fleet operation is very important as well. Methods for automation of this procedure are presented. Subsequently, feasible initial values of section parameters are found using heuristic rules devised from good driver’s practice and backward calculation based on dynamic programming principals. Designed velocity profile is further optimized based on simplified, but very fast energy consumption models, verified and fine-tuned on detailed simulation model of the vehicle. The velocity profile is updated when requested and finally loaded into on-board control unit. Model based predictive controller is used to keep the vehicle with its driver efficiently on defined track. The proposed strategy is verified in simulation environment and prepared to be implemented on test vehicle and cloud system.