Federated learning for smart charging of connected electric vehicles

Due to rising concerns over climate change, air
pollution and clean energy awareness, the demand
for electric vehicles (EVs) and renewable energy
generation has increased in recent years. The main
objective of this research is to design a decentralized
smart charging coordination framework for
EVs based on federated learning (FL) algorithms
in order to provide an acceptable collaboratively
learning model with privacy preservation of EVs,
improve charging scenarios, contribute to smart
grid stabilization, meet EVs energy requirements
wherever and whenever they request, and gain
welfare for EV owners. Moreover, FL is introduced
with the goal of bringing machine learning (ML)
down to the edge level in vehicular networks.
Ultimately, a multimetric routing protocol is also
used to predict the best route for transmitting
messages among EVs, infrastructures, charging
stations (CSs), and central servers.