Deep reinforcement learning hierarchical energy management strategy for hybrid electric vehicles
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.
国家科技期刊平台
NSTL
万方数据
维普
