摘要
本文深入探讨了强化学习在电动汽车有序充电领域的潜力,并详尽地审视了当前的研究现状和存在的挑战。首先阐述了电动汽车有序充电的背景和重要性,并深入解析了传统充电策略的局限性,从而突出强化学习方法的优越性和潜力。接下来,详细讨论了强化学习在电动汽车有序充电中的研究现状,包括关键问题如状态空间参数、动作选择策略以及奖励函数设计。对相关文献和研究成果进行全面审视,总结出强化学习在电动汽车有序充电领域取得的成果以及面临的挑战,并指明了未来研究的方向和发展趋势。最后,提出了一系列未来研究可以关注的问题和可能的解决方法,旨在为电动汽车有序充电领域的研究和实践提供新的视角和启示。
This paper delves into the potential of reinforcement learning in the field of orderly charging for electric vehicles,and thoroughly examines the current state of research and existing challenges.The article firstly elaborates on the background and significance of orderly charging for electric vehicles,and deeply analyzes the limitations of traditional charging strategies,thereby highlighting the superiority and potential of reinforcement learning methods.Next,it discusses in detail the application of reinforcement learning in orderly charging for electric vehicles,including key issues such as state space parameters,action selection strategies,and reward function design.A comprehensive review of related literature and research achievements summarizes the results and challenges faced by reinforcement learning in the field of orderly charging for electric vehicles,and points out the direction and development trend of future research.Finally,a series of questions and possible solutions that future research can focus on are proposed,aiming to provide new perspectives and insights for research and practice in the field of orderly charging for electric vehicles.
作者
孙晓龙
李婷
马添翼
于明洋
王志远
SUN Xiaolong;LI Ting;MA Tianyi;YU Mingyang;WANG Zhiyuan(School of Mechanical and Electrical Engineering,Beijing Institute of Graphic Communication,Beijing 102600,China)
出处
《北京印刷学院学报》
2024年第8期36-41,共6页
Journal of Beijing Institute of Graphic Communication
关键词
强化学习
电动汽车
有序充电
reinforcement learning
electric vehicles
orderly charging
