e existing electromobility (EM) is still in its edgling stage and multiple challenges have to be overcome to make Electric Vehicles (EVs) as convenient as combustion engine vehicles. Users and Electric Vehicle Fleet Operators (EFOs) want their EVs to be charged and ready for use at all times. is straightforward goal, however, is counteracted from various sides: e range of the EV depends on the status and depletion of the EV ba ery which is in uenced by EV use and charging characteristics. Also, most convenient charging from the user's point of view, might unfortunately lead to problems in the power grid. As in the case of a power peak in the evening when EV users return from work and simultaneously plug in their EVs for charging. Last but not least, the mass of EV ba eries are an untapped potential to store electricity from intermi ent renewable energy sources. In this paper, we propose a novel approach to tackle this multilayered problem from di erent perspectives. Using on-board EV data and grid prediction models, we build up an information model as a foundation for a back end service containing EFO and Charging Station Provider (CSP) logic as well as a central Advanced Drivers Assistant System (ADAS). ese components connect to both battery management and user interfaces suggesting various routing and driving behaviour alternatives customized and incentivized for the current user pro le optimizing above mentioned goals. CCS CONCEPTS •Applied computing →Transportation; •Hardware →Smart grid; Energy distribution; •Social and professional topics →User characteristics; •So ware and its engineering →So ware architectures;

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