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
双母管式机组较为广泛地应用于热电联产机组中,但由于多炉多机和2根大容量母管互相影响,导致热电负荷跟踪不及时,母管压力控制自动化水平较低。为此,针对双母管系统的非线性、强耦合、大迟延特性,设计了基于广义扩张状态观测器的多模型...双母管式机组较为广泛地应用于热电联产机组中,但由于多炉多机和2根大容量母管互相影响,导致热电负荷跟踪不及时,母管压力控制自动化水平较低。为此,针对双母管系统的非线性、强耦合、大迟延特性,设计了基于广义扩张状态观测器的多模型预测控制(generalized extended state observer based muti-model predictive control,GESOMMPC)方法。首先,建立了基于间隙度量(gap-metric)的多模型控制对象用于逼近非线性系统;其次,设计了扩张状态观测器估计系统耦合的集总扰动,并作为前馈信号输入到预测控制器中;最后,设计基于扰动前馈的多模型预测控制器实现对双母管系统的控制。实验结果表明,相对于PID方法,所提方法在满足电热负荷的同时,可以在允许范围内保持母管压力稳定,且动态偏差更小,过渡过程时间更短。展开更多
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.
文摘双母管式机组较为广泛地应用于热电联产机组中,但由于多炉多机和2根大容量母管互相影响,导致热电负荷跟踪不及时,母管压力控制自动化水平较低。为此,针对双母管系统的非线性、强耦合、大迟延特性,设计了基于广义扩张状态观测器的多模型预测控制(generalized extended state observer based muti-model predictive control,GESOMMPC)方法。首先,建立了基于间隙度量(gap-metric)的多模型控制对象用于逼近非线性系统;其次,设计了扩张状态观测器估计系统耦合的集总扰动,并作为前馈信号输入到预测控制器中;最后,设计基于扰动前馈的多模型预测控制器实现对双母管系统的控制。实验结果表明,相对于PID方法,所提方法在满足电热负荷的同时,可以在允许范围内保持母管压力稳定,且动态偏差更小,过渡过程时间更短。
