Real-time distributed charging station recommendation for electric vehicles: A federated meta-RL approach

Abstract

The growth of Electric Vehicles (EVs) places an increasingly heavy burden on the limited charging infrastructure, necessitating an effective charging station recommendation strategy that assists EVs in finding the most suitable charging stations. Deep reinforcement learning is a promising technology that has been applied to optimize EVs' charging recommendations. However, existing schemes have low scalability and high communication costs as they usually require collecting real-time information on both charging requests and charger availability at various stations during policy training or execution. To address this challenge, we develop a real-time distributed charging station recommendation approach, named ReDirect, to minimize the charging duration experienced by EVs, considering dynamic charging requests of EVs and fluctuating availability at charging stations. ReDirect employs federated meta-reinforcement learning (RL) to empower distributed stations to collaboratively learn effective recommendation strategies and make decisions without sharing their local information, yielding improved scalability, reduced communication overhead, and enhanced data privacy. Furthermore, we conduct a rigorous theoretical analysis of the convergence performance of ReDirect. Extensive experimental results on real-world datasets demonstrate that ReDirect performs closely to the centralized recommendation algorithm and outperforms several state-of-the-art distributed algorithms in EV charging duration while realizing a balanced distribution of charging requests across multiple stations.