Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different ...Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.展开更多
剖析了 Active X Autom ation技术和 Auto CAD R14新提供的 Active X Autom ation编程接口 ,在此基础上介绍了基于 Active X Autom ation技术从 Auto CA D的 DWG文件中自动提取标题栏和明细栏管理信息的方法 ,并给出了关键技术的源程序 ...剖析了 Active X Autom ation技术和 Auto CAD R14新提供的 Active X Autom ation编程接口 ,在此基础上介绍了基于 Active X Autom ation技术从 Auto CA D的 DWG文件中自动提取标题栏和明细栏管理信息的方法 ,并给出了关键技术的源程序 .在此技术上开发出的软件包在实践中得到了较好的应用 .展开更多
剖析了 Active X技术和 Auto CAD R1 4新提供的 Active X Au-tomation编程接口 ,并在此基础上介绍了基于 Active X automation技术从Auto CAD的 DWG文件中自动提取标题栏和明细栏信息的方法和基于 Ac-tive X控件的图纸浏览技术。据此技...剖析了 Active X技术和 Auto CAD R1 4新提供的 Active X Au-tomation编程接口 ,并在此基础上介绍了基于 Active X automation技术从Auto CAD的 DWG文件中自动提取标题栏和明细栏信息的方法和基于 Ac-tive X控件的图纸浏览技术。据此技术开发出的软件包在实践中得到了较好的应用 。展开更多
文摘Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.
文摘虚拟仪器技术与传统仪器技术相比较具有开放性、易用性、价格便宜等特点 ,目前已经在各行各业中得到广泛的应用 .文中从虚拟仪器软件的开发的角度出发 ,分析了 Active X控件在开发虚拟仪器中的意义 ,阐述了基于虚拟仪器的 Active X控件的开发及在 Lab VIEW与 Visual C++中的应用过程 ,探讨了 Active
文摘剖析了 Active X Autom ation技术和 Auto CAD R14新提供的 Active X Autom ation编程接口 ,在此基础上介绍了基于 Active X Autom ation技术从 Auto CA D的 DWG文件中自动提取标题栏和明细栏管理信息的方法 ,并给出了关键技术的源程序 .在此技术上开发出的软件包在实践中得到了较好的应用 .
文摘剖析了 Active X技术和 Auto CAD R1 4新提供的 Active X Au-tomation编程接口 ,并在此基础上介绍了基于 Active X automation技术从Auto CAD的 DWG文件中自动提取标题栏和明细栏信息的方法和基于 Ac-tive X控件的图纸浏览技术。据此技术开发出的软件包在实践中得到了较好的应用 。
