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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Prolonging the lifetime of batteries in Electric Vehicles (EVs) becomes a more and more important issue for private users and fleet operators. In addition to the environmental point of view, a better battery health results in less cost, higher battery capacities and higher performance. To achieve this, the EV drivers or the fleet operators need to get proper information, which kind of actions will increase or decrease the batteries health. To this point, various tips and recommendations exist distributed over literature. Unfortunately, those kind of recommendations are hard to follow in the day-today routine. This paper suggests so called dynamic recommendations for battery health that are able to advise the user in specific situations with respect to battery use. Recommendations from literature are broken down into a list, which can be automatically computed. Recommendations will then be dynamically created in the current context of the EV and displayed to the user just in time. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models; Activity centered design; • Applied computing → Physics; • Information systems → Data analytics;

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