Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

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 coor...

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Autores principales:	Yongguang Liu, Wei Chen, Zhu Huang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/352786fea570461fa93d0180103e6591
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id	oai:doaj.org-article:352786fea570461fa93d0180103e6591
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spelling	oai:doaj.org-article:352786fea570461fa93d0180103e65912021-11-29T00:55:47ZReinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling1563-514710.1155/2021/1401802https://doaj.org/article/352786fea570461fa93d0180103e65912021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1401802https://doaj.org/toc/1563-5147The 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.Yongguang LiuWei ChenZhu HuangHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Yongguang Liu Wei Chen Zhu Huang Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling
description	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.
format	article
author	Yongguang Liu Wei Chen Zhu Huang
author_facet	Yongguang Liu Wei Chen Zhu Huang
author_sort	Yongguang Liu
title	Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling
title_short	Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling
title_full	Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling
title_fullStr	Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling
title_full_unstemmed	Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling
title_sort	reinforcement learning-based multiple constraint electric vehicle charging service scheduling
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/352786fea570461fa93d0180103e6591
work_keys_str_mv	AT yongguangliu reinforcementlearningbasedmultipleconstraintelectricvehiclechargingservicescheduling AT weichen reinforcementlearningbasedmultipleconstraintelectricvehiclechargingservicescheduling AT zhuhuang reinforcementlearningbasedmultipleconstraintelectricvehiclechargingservicescheduling
_version_	1718407761194123264

Reinforcement Learning-Based Multiple Constraint Electric Vehicle Charging Service Scheduling

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