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.

This work was supported by the Spanish Government under research project “Enhancing Communication Protocols with Machine Learning while Protecting Sensitive Data (COMPROMISE)” PID2020-113795RBC31/AEI/10.13039/501100011033.

Peer Reviewed