GreenFlux is always looking for brilliant minds!
Graduate on smart charging
Are you a student (MSc or HBO) in Physics, Engineering, Computer Science or Artificial Intelligence? GreenFlux offers exciting graduation projects in a rapidly growing, high-tech market that enable you to be part of the electric future.
What others did before you
Assessing the smart charging of multiple electric vehicles with a cloud-based algorithm
In this graduation project, simulation software was created and then used to assess possible improvements on the existing GreenFlux smart charging algorithm.With the use of artificial intelligence, better parameters for the algorithm were found that, according to simulations, can lead to up to 26 percent charging time reduction in some cases. Furthermore, data costs for communication between the smart charging server and the charge points can be reduced, whilst charging performance is kept intact.
Bringing the smart charging algorithm to infrastructure networks with complex topologies
In this graduation project, a branched approach was introduced by implementing a tree structure to divide the charging points in demand groups. Different alternatives, which are based on the tree structure, were proposed. They are accompanied with algorithms that distribute the capacity to the different demand groups, and in turn to the different EVs in a fair way. A simulation model was developed to find and evaluate the alternative that concurrently provides a fair capacity distribution and performance enhancement. The simulation outcome provides valuable results for GreenFlux about extension of the existing distribution algorithm to more complex topologies.
What you can work on
Let us mention just a few topics that you could work on. Of course, we are open to your own ideas as well!
- Active harmonic filtering in a charging network
- Building more realistic simulation models that require detailed simulations of people, electric vehicles, and communication using .NET
- Building virtual charge points that can interact with different smart charging algorithms
- Using machine learning to predict charging behavior and charge point occupancy