Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

Yongguang Liu,Zhu Huang,Wei Chen

doi:10.1155/2021/1401802

Yongguang Liu, Zhu Huang + Show 1 more

Open Access

https://doi.org/10.1155/2021/1401802

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Abstract

The popularization of electric vehicles faces problems such as difficulty in charging, difficulty in selecting fast charging locations, and comprehensive consideration of multiple factors and vehicle interactions. With the increasingly mature application of navigation technology in vehicle-road coordination and other aspects, the proposal of an optimal dynamic charging method for electric fleets based on adaptive learning makes it possible for edge computing to process electric fleets to effectively execute the optimal route charging plan. We propose a method of electric vehicle charging service scheduling based on reinforcement learning. First, an intelligent transportation system is proposed, and on this basis a framework for the interaction between fast charging stations and electric vehicles is established. Subsequently, a dynamic travel time model for traffic sections was established. Based on the habits of electric vehicle owners, an electric vehicle charging navigation model and a reinforcement learning reward model were proposed. Finally, an electric vehicle charging navigation scheduling method is proposed to optimize the service resources of the fast charging stations in the area. The simulation results show that the method balances the charging load between stations, can effectively improve the charging efficiency of electric vehicles, and increases user satisfaction.

Highlights

With the extensive development of electric vehicles in various countries around the world, the number of electric vehicles is increasing, and problems such as difficulty in charging electric vehicles, serious line losses, voltage drops, charging safety, and severe peaks are expected [1,2,3]
In [10], we studied the uniform charging node in [11] and extended it to the nonuniform charging node in [12] by solving the mixed integer nonlinear programming problem (MINLP) of the single vehicle. e remaining energy of the vehicle on each node is expressed as a dynamic programming (DP) problem for a single electric vehicle path problem, and a DP-based algorithm is provided to determine the optimal path and charging strategy of the electric vehicle subflow level
In [13], we proposed a distributed electric vehicle path selection system based on the distributed ant colony algorithm (ACA). e distributed architecture minimizes the total travel of electric vehicles to the destination by proposing a set of nearest fast charging stations

Summary

Introduction

With the extensive development of electric vehicles in various countries around the world, the number of electric vehicles is increasing, and problems such as difficulty in charging electric vehicles, serious line losses, voltage drops, charging safety, and severe peaks are expected [1,2,3]. Most charging scheduling uses a fixed strategy while ignoring the influence of various factors, such as the increase in the number of electric vehicles and user habits, on electric vehicle charging scheduling for different time periods. In this context, we propose an electric vehicle charging service scheduling method based on reinforcement learning to meet the needs of electric vehicle owners.

Fast Charging Station and Electric Vehicle System Framework

Simulation Results and Discussion

Conclusions