  Electric vehicles are becoming more popular all over the world. With
increasing battery capacities and a growing fast-charging infrastructure, they
are becoming suitable for long distance travel. However, queues at charging
stations could lead to long waiting times, making efficient route planning even
more important. In general, optimal multi-objective route planning is extremely
computationally expensive. We propose an adaptive charging and routing
strategy, which considers driving, waiting, and charging time. For this, we
developed a multi-criterion shortest-path search algorithm using contraction
hierarchies. To further reduce the computational effort, we precompute
shortest-path trees between the known locations of the charging stations. We
propose a central charging station database (CSDB) that helps estimating
waiting times at charging stations ahead of time. This enables our adaptive
charging and routing strategy to reduce these waiting times. In an extensive
set of simulation experiments, we demonstrate the advantages of our concept,
which reduces average waiting times at charging stations by up to 97 %. Even if
only a subset of the cars uses the CSDB approach, we can substantially reduce
waiting times and thereby the total travel time of electric vehicles.
