The large-scale deployment of electrical vehicles (EV) in the service
industry poses several challenges, one of these being the charging
infrastructure and logistics. In particular the switch to EVs for large
organizations requires an accurate planning of charging tower number
and utilization based on available statistical data.

In this master thesis the candidate will study, evaluate, and develop algorithms to optimize
the scheduling and routing (e.g., parcel delivery) of a fleet of EV and
optimize their charging cycles.

The charging profiles, battery status information, and vehicle details
are provided from real-data gathered from a large-scale experiment.

The evaluation of the developed algorithms will be performed via
simulation (see Simulation of EV Charging Cycles thesis) and possibly
with the deployment on a real EV fleet in a constrained environment.

Mandatory:
- Good programming skills (C++, Python, C).
- Good algorithmic / math background.
- Knowledge of basic simulation strategies.