Charging strategies for a smart home connected battery electric vehicle

Amendments of the two German directives that define feed-in tariffs, the EEG and KWKG, guarantee a higher payment for self-consumption of locally produced electricity than feeding into the public grid. In the context of an emerging market of plug-in battery electric vehicles this legislation is a promising option for additional revenues: Batteries of Plug-In vehicles serving as energy storages in combination with smart charging schedules represent an opportunity to optimize the amount of local energy consumption. The approach used in this paper for evaluating this complex scenario is a model-based optimization using mixed integer linear programming (MILP) algorithms. Underlying data of the model based analysis are real load, PV and CHP generation data, realistic assumptions for driving patterns and a model for battery degradation of the plug-in vehicle. The simulation revealed a significant potential benefit for optimized local operation management. After that, differen t system control concepts for linking the charging times of electric vehicles with fluctuating local renewable energy production are studied. Finally, a prototype system has been developed which is being evaluated in a field trial. The system includes a home energy management and a mobile charging management. In this paper, the communication and system control concepts are being presented, followed by a first proof of principle.