电力系统自动化2024，Vol.48Issue(11)：184-196,13.DOI:10.7500/AEPS20230621004
基于强化学习环境设计策略的电动汽车充电路径规划
Charging Path Planning for Electric Vehicles Based on Reinforcement Learning Environment Design Strategy
摘要
Abstract
An environmental modeling method suitable for reinforcement learning is proposed for the charging path planning problem of electric vehicles.Based on the actual situation of urban road network and geographical distribution of charging stations,this method divides the basic driving path of electric vehicles into three segments for representation.Based on the three-segment expression method,the design scheme of state space,action space,state transition,and reward function is proposed.The charging path planning is modeled as a Markov decision process,and solved by the Q learning method and the deep Q network(DQN)method.The experimental results show that the design scheme of the reinforcement learning environment based on the three-segment expression method is solvable and portable.It takes into account the realistic scenarios such as the deceleration and turning of electric vehicles in the process of driving from the road to the charging station,and simplifies the charging action into a driving direction choice,which improves the efficiency of the reinforcement learning algorithm based on Q learning and DQN.关键词
电动汽车/充电路径规划/强化学习/深度Q网络/环境建模/三段式表达法Key words
electric vehicle/charging path planning/reinforcement learning/deep Q network/environmental modeling/three-segment expression method引用本文复制引用
宋宇航,陈宇帆,魏延岭,高山..基于强化学习环境设计策略的电动汽车充电路径规划[J].电力系统自动化,2024,48(11):184-196,13.基金项目
国家重点研发计划资助项目(2021YFB2501600). This work is supported by National Key R&D Program of China(No.2021YFB2501600). （2021YFB2501600）
