重庆大学学报2024，Vol.47Issue(1)：41-51,11.DOI:10.11835/j.issn.1000-582X.2022.012
混合动力汽车深度强化学习分层能量管理策略
Deep reinforcement learning hierarchical energy management strategy for hybrid electric vehicles
摘要
Abstract
To improve the fuel economy and control strategy stability of hybrid electric vehicles(HEVs),with taking the third-generation Prius hybrid electric vehicle as the research object,a hierarchical energy management strategy is created by combining an equivalent fuel consumption minimization strategy(ECMS)with a deep reinforcement learning(DRL)method.The simulation results show that the hierarchical control strategy not only enables the agent in reinforcement learning to achieve adaptive energy-saving control without a model,but also ensures that the state of charge(SOC)of the hybrid vehicle meets the constraints under all operating conditions.Compared with the rule-based energy management strategy,this layered control strategy improves the fuel economy by 20.83%to 32.66%.Additionally,increasing the prediction information of the vehicle speed by the agent further reduces the fuel consumption by about 5.12%.Compared with the deep reinforcement learning strategy alone,this combined strategy improves fuel economy by about 8.04%.Furthermore,compared with the A-ECMS strategy that uses SOC offset penalty,the fuel economy is improved by 5.81%to 16.18%under this proposed strategy.关键词
混合动力汽车/动态规划/强化学习/深度神经网络/等效燃油消耗Key words
hybrid vehicle/dynamic programming/reinforcement learning/deep neural networks/equivalent consumption minimization strategy分类
交通工程引用本文复制引用
戴科峰,胡明辉..混合动力汽车深度强化学习分层能量管理策略[J].重庆大学学报,2024,47(1):41-51,11.基金项目
重庆市技术创新与应用重大主题专项资助项目(cstc2019jscx-zdztzxX0047) （cstc2019jscx-zdztzxX0047）
国家自然科学基金资助项目(52072053).Supported by Chongqing Technology Innovation and Application Major Special Project(cstc2019jscx-zdztzxX0047)and National Natural Science Foundation of China(52072053). （52072053）
