A central challenge of electric mobility is the battery electric range. Potential customers of BEVs are afraid of being restricted in their mobility or stranding far away from the available charging infrastructure. This concern is summarized under the term "range anxiety". In order to keep these restrictions as low as possible, this research project develops a trip planning assistant for electric vehicles.
For journeys that exceed the range of an BEV, the resulting trip time is made up of both travel and charging time. Since the charging time is significantly longer than the refueling time for conventional vehicles, the charging time must be explicitly taken into account when planning the route for EVs. In addition, restrictions on battery capacity must be considered and adhered to. This adds an additional dimension to the classic Shortest Path Problem for finding the shortest or fastest route. The resulting questions can be summarized as follows:
- Which route shall be chosen?
- How much energy is required along this route?
- Where should be charged along this route?
- How long / How much energy is charged at these charging stops?
To answer these questions, optimization-based approaches are developed, which for example allow the minimization of travel time for an EV. The basis for this is an exact consumption forecast. For this reason, identification of consumption-relevant parameters is also performed in this research project.