插电式混合动力车PHEV(plug-in hybrid electric vehicle)可视为电网中的移动储能装置,大量的成规模的PHEV引入电网可以为电网提供额外的调节服务能力。首先阐述了单个PHEV并网充电的基本原理,然后建立了规模化PHEV的充电功率模型,接着...插电式混合动力车PHEV(plug-in hybrid electric vehicle)可视为电网中的移动储能装置,大量的成规模的PHEV引入电网可以为电网提供额外的调节服务能力。首先阐述了单个PHEV并网充电的基本原理,然后建立了规模化PHEV的充电功率模型,接着在此基础上提出了满足系统和用户用电满意度的双目标的规模化PHEV充电管理需求响应控制策略。最后通过仿真算例表明规模化PHEV能够实现系统辅助服务。并分析了用户用电满意度、系统控制起始时刻,两个重要参数的设置对于PHEV调节辅助服务能力的影响。展开更多
为了满足不同的技术和经济目标,从轻度混合动力、插电式混合动力到全电池动力的电动汽车,都将依赖于新型的、先进的(如基于锂的)蓄电池。这些电池在各种应用条件下的性能预测和寿命表征费工、费时,目前尚未得到充分的发展。一些国家已...为了满足不同的技术和经济目标,从轻度混合动力、插电式混合动力到全电池动力的电动汽车,都将依赖于新型的、先进的(如基于锂的)蓄电池。这些电池在各种应用条件下的性能预测和寿命表征费工、费时,目前尚未得到充分的发展。一些国家已投入资金和人力进行相关的研究,其实通过国际合作,这些努力和花费也许能发挥更大的作用,例如目前正在国际能源机构(The International Energy Agency,IEA)框架内开展的准备工作。正在致力于开发一套标准化的、加速的测试程序,将允许各个测试机构合作分析电池的测量数据。该文评述了欧洲、日本和美国在加速寿命测试程序上的最新进展。以国际合作为目标,搜集、对比和分析现有的测试程序。展开更多
This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimi...This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.展开更多
文摘插电式混合动力车PHEV(plug-in hybrid electric vehicle)可视为电网中的移动储能装置,大量的成规模的PHEV引入电网可以为电网提供额外的调节服务能力。首先阐述了单个PHEV并网充电的基本原理,然后建立了规模化PHEV的充电功率模型,接着在此基础上提出了满足系统和用户用电满意度的双目标的规模化PHEV充电管理需求响应控制策略。最后通过仿真算例表明规模化PHEV能够实现系统辅助服务。并分析了用户用电满意度、系统控制起始时刻,两个重要参数的设置对于PHEV调节辅助服务能力的影响。
文摘为了满足不同的技术和经济目标,从轻度混合动力、插电式混合动力到全电池动力的电动汽车,都将依赖于新型的、先进的(如基于锂的)蓄电池。这些电池在各种应用条件下的性能预测和寿命表征费工、费时,目前尚未得到充分的发展。一些国家已投入资金和人力进行相关的研究,其实通过国际合作,这些努力和花费也许能发挥更大的作用,例如目前正在国际能源机构(The International Energy Agency,IEA)框架内开展的准备工作。正在致力于开发一套标准化的、加速的测试程序,将允许各个测试机构合作分析电池的测量数据。该文评述了欧洲、日本和美国在加速寿命测试程序上的最新进展。以国际合作为目标,搜集、对比和分析现有的测试程序。
基金Project(KF2029)supported by the State Key Laboratory of Automotive Safety and Energy(Tsinghua University),ChinaProject(102253)supported partially by the Innovate UK。
文摘This paper studied a supervisory control system for a hybrid off-highway electric vehicle under the chargesustaining(CS)condition.A new predictive double Q-learning with backup models(PDQL)scheme is proposed to optimize the engine fuel in real-world driving and improve energy efficiency with a faster and more robust learning process.Unlike the existing“model-free”methods,which solely follow on-policy and off-policy to update knowledge bases(Q-tables),the PDQL is developed with the capability to merge both on-policy and off-policy learning by introducing a backup model(Q-table).Experimental evaluations are conducted based on software-in-the-loop(SiL)and hardware-in-the-loop(HiL)test platforms based on real-time modelling of the studied vehicle.Compared to the standard double Q-learning(SDQL),the PDQL only needs half of the learning iterations to achieve better energy efficiency than the SDQL at the end learning process.In the SiL under 35 rounds of learning,the results show that the PDQL can improve the vehicle energy efficiency by 1.75%higher than SDQL.By implementing the PDQL in HiL under four predefined real-world conditions,the PDQL can robustly save more than 5.03%energy than the SDQL scheme.
