A Method Combining Improved Particle Swarm Optimization and Lyapunov Optimization for Electric Vehicle Charging Scheduling

Jingwen Xu, Fei Chen, Jiawei Zhu, Weidong Lei, Yisheng An

Abstract: In recent years, with the continuous increase in the number of electric vehicles, uncoordinated charging has exacerbated congestion at charging stations and surrounding traffic, as well as reduced charging efficiency. Therefore, there is a pressing need for optimizing the scheduling of electric vehicles. As the user’s charging waiting time is influenced by the charging station management strategy, this paper proposes a joint optimization approach. In this approach, Lyapunov optimization is employed for charging station management, allowing charging stations to maintain the stability of the charging queues while maximizing long-term revenue. Subsequently, a particle swarm optimization method is used to schedule electric vehicles, aiming to minimize travel time and queue waiting time. Recognizing that the original particle swarm optimization algorithm with fixed parameters can significantly reduce the algorithm’s search and convergence capabilities, this paper enhances the algorithm by dynamically adapting the algorithm parameters and introducing velocity perturbation in the particle update phase to improve its ability to escape local optima. Finally, through experimental simulations, the effectiveness of the proposed model and algorithm is validated. The experimental results demonstrate that by utilizing the proposed joint optimization method, it can effectively reduce the time cost for vehicle owners while balancing the load of charging stations and the grid load.