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 implement a simulation of the charging cycles of a
fleet of EVs based on the actual charging profiles measured on EVs.
The charging profiles, battery status information, and vehicle details
are provided from real-data gathered from a large-scale experiment.
The simulation results will be validated against the current
optimization strategies and possibly with the deployment on a real EV fleet in a constrained environment.

Mandatory:
- Good programming skills (C++, Python, C).
- Knowledge of basic simulation strategies.
- Basic knowledge of statistics.