Range-Constrained and Stable Federated Charging Scheme for Electricity Vehicles

Due to the increasing number of electric vehicles and limited charging stations, charging service providers need to consider the minimum range of electric vehicles and the congestion level of charging stations to formulate pricing strategies and guide charging vehicles. However, this service faces the challenges of privacy protection and utility coupling between CSs and EVs. Besides, the minimum driving range is also affected by the time-varying data characteristics of vehicles. Therefore, this paper proposes an FL-assisted stable charging matching strategy considering congestion factors and range constraints. Specifically, we employ a federated learning scheme to predict the congestion factor of each charging station by learning the complex hidden characteristics of vehicles under the condition of privacy protection. Then, the pricing strategy and utility function are updated based on the congestion factor. Furthermore, under the driving range constraint, we transform the impact of the congestion factor on the expected utility into a regret value and use the Gale-Shapley algorithm based on regret minimization to achieve stable matching between vehicles and charging stations. Experimental results demonstrate the scheme’s effectiveness and that the charging success rate is improved by up to 11.7% compared with mainstream methods.