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Access Control Method for ev charging stations Based on State Aggregation and Q-Learning

Journal of Systems Science & Complexity 2022年第6期35卷 2145-2165页

作者： TANG Ziyu LUO Yonglong FANG Daohong ZHAO Chuanxin School of Computer and Information Anhui Normal UniversityWuhu 241002China Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China

This paper presents intelligent access control for a charging station and a framework for dynamically and adaptively managing charging requests from randomly arriving electric vehicles(evs),to increase the revenue of the ***,charging service requests from random ev arrivals are described as an event-driven sequential decision process,and the decision-making relies on an eventextended state that is composed of the real-time electricity price,real-time charging station state,and ev arrival ***,a state aggregation method is introduced to reduce the state space by first aggregating the charging station state in the form of the remaining charging time and then further aggregating it via sort ***,mathematical calculations of the code value are provided,and their uniqueness and continuous integer characteristics are ***,a corresponding Q-learning method is proposed to derive an optimal or suboptimal access control *** results of a case study demonstrate that the proposed learning optimisation method based on the event-extended state aggregation performs better than flat *** space complexity and time complexity are significantly reduced,which substantially improves the learning efficiency and optimisation performance.

关键词： Access control aggregation ev charging station event-extended state Q-learning

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