The work performed during this period was driven by the R&D areas of the project and the demonstration of its results by experiments and field trials: a) ADAS was developed, and released in its 1st stable version via Google Play. ADAS is divided into parts: - the ADAS UI, the graphical interface, mobile app for Android phones that provides EV drivers with routing and navigation capabilities, customized for EVs. At the beginning, the app requests certain information to the driver (e.g. the car model) and origin and destination of its trip. The ADAS shows different routing options based on green, fast and shortest route; - the ADAS AI, the smart routing engine that collects the input from the app, from the environment of the vehicle (charging stations location, max. power capacity, renewable %, route elevation, etc) and from the car itself (range, past charging modes) and generates the routing options. b) the analysis of the continuous usage of an EV, driver’s behaviour (acceleration), charging modes (slow, fast) is formulated in a charging algorithm, able to calculate the amount of energy needed to reach the next destination, more accurate than the range calculation provided by the vehicle/battery manufacturer (SoC model). At the same time, these variables – among others – are considered for analysing the behaviour (possible degradation) of the battery due to charging processes, creating recommendations for a “healthier” way of charging. c) Using the SoC model, the ELECTRIFIC Smart Scheduler in its 1st version was delivered within this period. d) ELECTRIFIC is able to incentivize users to charge when more renewables are available. The incentives schema has been defined during this period and tested, and will be further developed during the next. However, the calculation of the actual REN % available in the grid and in the charging stations cannot be accurate due to the lack of data. e) In this period, we demonstrated how a charging station can be reactive to possible grid issues. On the one hand, thanks to predictions of power demand from the DSO, the CS can ensure the needed power availability. On the other hand, the CS can react in real-time to issues coming from the grid side once the charging process is being performed (ELECTRIFIC Smart Charger). f) Through surveys and field trials (eco-button trial and ADAS trial) we analysed which kind on incentives schemas should be defined in order to foster users’ behaviour towards a more sustainable mobility. In addition, user profiling variables have been identified.
