Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

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.

A Comprehensive Review on Smart Electromobility Charging Infrastructure

“…The research outputs of [141,142] proposed reinforcement learning-based scheduling methods, optimising the charging and load of largescale EVs and leveraging artificial intelligence to optimise charging schedules based on real-time data adaptively. Beyond optimising charging schedules, studies have delved into specific applications such as electric bus scheduling [143,144], coordinated schedule optimisation for buses' vehicles and charging [145], and the electric vehicle routing problem [146], addressing unique challenges associated with different EV types and operational requirements. The exploration of renewable energy source integration into EV charging has been addressed.…”

Smart and Efficient EV Charging Navigation Scheme in Vehicular Edge Computing Networks

“…• RL-based EV charging algorithm [38,39] : After the EV route is determined, the RL method is applied to find the optimal FCSs. • Randomized FCS selection algorithm: It is a normal scheme.…”

Driving Profile Optimization Using a Deep Q‐Network to Enhance Electric Vehicle Battery Life

“…There is often no obvious "correct" way to solve a problem and no way to check whether learning is appropriate. Reinforcement learning determines the actions that are optimal under the current conditions (Figure 1) [25,26]. A reward is given (in an external environment) whenever an agent takes an action; learning proceeds in directions that maximize the reward.…”