电气传动2026，Vol.56Issue(1)：57-66,10.DOI:10.19457/j.1001-2095.dqcd26280
基于分层深度强化学习的电动汽车实时充电引导策略
Real-time Charging Guidance Strategy for Electric Vehicles Based on Hierarchical Deep Reinforcement Learning
摘要
Abstract
To realize the real-time charging guidance of electric vehicles and improve the charging efficiency of charging stations,a real-time charging guidance strategy for electric vehicles based on hierarchical deep reinforcement learning was proposed.Considering the mutual coupling characteristics of vehicle-station-road multiple agents,a double-layer electric vehicle charging navigation model was constructed based on the characteristic information of electric vehicles,charging stations,distribution networks and transportation networks.The above-mentioned model was decoupled into a two-layer finite Markov decision process network architecture,the upper network evaluated and recommended charging stations,and the optimal selection result were passed to the lower network.The lower network planed the driving path for the user.The deep Q-network algorithm based on rainbow framework was used to solve the above-mentioned two-layer decision-making process.Finally,the simulation results in a specific urban area show that compared with the disorderly guidance method,the proposed method can reduce the user time cost and save the user cost,and ensure the safe operation of the distribution network.关键词
电动汽车/实时充电引导/推荐充电站/规划行驶路径/双层深度强化学习/深度Q网络算法Key words
electric vehicle(EV)/real-time charging guidance/recommending charging station/planning driving path/two-layer deep reinforcement learning(DRL)/deep Q-network algorithm分类
信息技术与安全科学引用本文复制引用
陆文韬,窦胜,陈良亮,杨凤坤,周瑞超..基于分层深度强化学习的电动汽车实时充电引导策略[J].电气传动,2026,56(1):57-66,10.基金项目
国家电网有限公司科技项目(5400-202312239A-1-1-ZN) （5400-202312239A-1-1-ZN）
