The invention belongs to the field of intelligent transportation, and relates to a charging pile scheduling method and system based on deep reinforcement learning, and in the charging pile scheduling system based on deep reinforcement learning, the states of a request terminal and a charging pile are uploaded to an intelligent control platform through a communication module via a transmission medium; and the intelligent control platform calculates a scheduling scheme of the request terminal based on a deep reinforcement learning algorithm according to the environment (the charging request and the state of the charging pile), and returns an action to the request terminal. According to the invention, the deep reinforcement learning algorithm is embedded in the intelligent control platform, and the deep reinforcement learning algorithm continuously performs interactive learning with the external environment, so that the method has a good adaptive capability, and can significantly improve the service efficiency of the charging pile while meeting the charging demand of the automobile.
本发明属于智能交通领域,涉及一种基于深度强化学习的充电桩调度方法及系统,所述基于深度强化学习的充电桩调度系统中请求终端和充电桩的状态通过通信模块经传输介质上传至智能控制平台;智能控制平台根据环境(充电请求和充电桩的状态)基于深度强化学习算法计算请求终端的调度方案,并返回动作到请求终端。本发明中,深度强化学习算法嵌入在智能控制平台中,并且深度强化学习算法与外界环境不断进行交互学习,具有较好的自适应能力,在满足汽车充电需求的同时,能够显著提高充电桩的服务效率。
Charging pile scheduling method and system based on deep reinforcement learning
一种基于深度强化学习的充电桩调度方法及系统
2024-01-02
Patent
Elektronische Ressource
Chinesisch
IPC: | G06Q Datenverarbeitungssysteme oder -verfahren, besonders angepasst an verwaltungstechnische, geschäftliche, finanzielle oder betriebswirtschaftliche Zwecke, sowie an geschäftsbezogene Überwachungs- oder Voraussagezwecke , DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES / B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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