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
Due to the coaxial connection of engine, motor and pump, the dynamic characteristics of hybrid construction machinery are changed, which generates a new torsional vibration problem of multi-power sources. To reduce th...Due to the coaxial connection of engine, motor and pump, the dynamic characteristics of hybrid construction machinery are changed, which generates a new torsional vibration problem of multi-power sources. To reduce the torsional vibration of the hybrid construction machinery complex shafting, torsional vibration active control was proposed. The three-mass model of coaxial shafting of hybrid construction machinery was established. The PID control and the fuzzy sliding mode control were chosen to weaken torsional vibration by controlling the motor speed and torque. The simulation results show that the fuzzy sliding mode control has 12% overshoot of the PID control when the engine torque changes. The active control is effective and can realize smooth power switch.展开更多
为了解决高比例新能源地区电网中新能源不确定性所导致的N-1故障线路过载问题,提出一种计及新能源不确定性并应用混合型潮流控制器(hybrid power flow controller,HPFC)控制模式的电网潮流优化方法。首先,建立了适应于多线路控制的HPFC...为了解决高比例新能源地区电网中新能源不确定性所导致的N-1故障线路过载问题,提出一种计及新能源不确定性并应用混合型潮流控制器(hybrid power flow controller,HPFC)控制模式的电网潮流优化方法。首先,建立了适应于多线路控制的HPFC稳态计算模型,并给出了在不同控制模式下的HPFC运行约束条件。其次,以电网有功网损和线路负载率指标为目标函数,考虑N-1安全约束和HPFC运行约束,建立应用HPFC控制模式的电网潮流优化模型。然后,通过模糊C均值聚类获取反映新能源出力、负荷不确定性的场景集合,并采用多目标多元宇宙优化算法(multi-objective multi-verse optimization,MOMVO)求解所提优化模型。最后,将所提潮流优化方法应用于江苏南通某地区电网。结果表明,所提方法能有效提高电网的经济性与静态安全性,且计算结果具有较好的稳定性。展开更多
为平抑风力发电系统的出力波动,通过调整混合储能系统(hybrid energy storage system,HESS)的控制策略,满足并网波动限制,构建了一种基于风电功率预测的混合储能双层模糊控制策略。首先,利用改进的完全自适应噪声集合经验模态(improved ...为平抑风力发电系统的出力波动,通过调整混合储能系统(hybrid energy storage system,HESS)的控制策略,满足并网波动限制,构建了一种基于风电功率预测的混合储能双层模糊控制策略。首先,利用改进的完全自适应噪声集合经验模态(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)对原始风电数据进行分解;其次,将改进的Adam算法与Transformer模型结合对各分量预测,预测结果叠加作为最终预测结果;最后,基于预测的风电功率波动状态和混合储能荷电状态(state of charge,SOC),对混合储能系统采用双层模糊控制策略进行调节,确保在风电平稳并网前提下,减少混合储能系统的过充、过放情况。结果表明:所提控制策略平抑风电出力达到更低的波动指标,保证可靠并网;并且控制混合储能系统的SOC在合理范围内,使系统整体性能得到提升。展开更多
Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the mai...Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.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.
基金Project(51205415)supported by the National Natural Science Foundation of ChinaProject(14JJ3020)supported by the Natural Science Foundation of Hunan Province,China+2 种基金Project(2013M542129)supported by China Postdoctoral Science FoundationProject(2012QNZT014)supported by the Fundamental Research Funds for the Central Universities,ChinaProject supported by the Postdoctoral Foundation of Central South University,China
文摘Due to the coaxial connection of engine, motor and pump, the dynamic characteristics of hybrid construction machinery are changed, which generates a new torsional vibration problem of multi-power sources. To reduce the torsional vibration of the hybrid construction machinery complex shafting, torsional vibration active control was proposed. The three-mass model of coaxial shafting of hybrid construction machinery was established. The PID control and the fuzzy sliding mode control were chosen to weaken torsional vibration by controlling the motor speed and torque. The simulation results show that the fuzzy sliding mode control has 12% overshoot of the PID control when the engine torque changes. The active control is effective and can realize smooth power switch.
文摘为平抑风力发电系统的出力波动,通过调整混合储能系统(hybrid energy storage system,HESS)的控制策略,满足并网波动限制,构建了一种基于风电功率预测的混合储能双层模糊控制策略。首先,利用改进的完全自适应噪声集合经验模态(improved complete ensemble empirical mode decomposition with adaptive noise,ICEEMDAN)对原始风电数据进行分解;其次,将改进的Adam算法与Transformer模型结合对各分量预测,预测结果叠加作为最终预测结果;最后,基于预测的风电功率波动状态和混合储能荷电状态(state of charge,SOC),对混合储能系统采用双层模糊控制策略进行调节,确保在风电平稳并网前提下,减少混合储能系统的过充、过放情况。结果表明:所提控制策略平抑风电出力达到更低的波动指标,保证可靠并网;并且控制混合储能系统的SOC在合理范围内,使系统整体性能得到提升。
文摘Due to soaring fuel prices and environmental concerns, hybrid electric vehicle(HEV) technology attracts more attentions in last decade. Energy management system, configuration of HEV and traffic conditions are the main factors which affect HEV's fuel consumption, emission and performance. Therefore, optimal management of the energy components is a key element for the success of a HEV. An optimal energy management system is developed for HEV based on genetic algorithm. Then, different powertrain system component combinations effects are investigated in various driving cycles. HEV simulation results are compared for default rule-based, fuzzy and GA-fuzzy controllers by using ADVISOR. The results indicate the effectiveness of proposed optimal controller over real world driving cycles. Also, an optimal powertrain configuration to improve fuel consumption and emission efficiency is proposed for each driving condition. Finally, the effects of batteries in initial state of charge and hybridization factor are investigated on HEV performance to evaluate fuel consumption and emissions. Fuel consumption average reduction of about 14% is obtained for optimal configuration data in contrast to default configuration. Also results indicate that proposed controller has reduced emission of about 10% in various traffic conditions.
