电动汽车混合储能系统的自适应协同控制
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摘要
针对电动汽车混合储能系统在采用协同控制方法时受超级电容等效电阻和电池内阻引起的输入电压波动影响等问题,提出了一种自适应协同控制策略。首先,基于直流变换器理想状态空间平均模型设计了协同控制器,并对其稳定性进行了分析;其次,基于超级电容等效电阻和电池内阻建立了系统的精确数学模型,定义了自适应观测函数和基于Lyapunov函数的自适应控制律,并对负载及输入电压进行估计;进一步,选取了协同控制宏变量并推导出自适应协同控制的占空比函数;最后,在MATLAB/Simulink中搭建了混合储能系统的仿真模型,验证了自适应协同控制的有效性。仿真结果表明:自适应协同控制只有轻微的超调和因负载变化造成的电流波动,且响应速度较快,约为4 ms,电流跟踪误差不超过1%。与协同控制和滑模控制相比,自适应协同控制系统有更好的稳定性和抗干扰能力,能有效提高电动汽车混合储能系统的稳定性和鲁棒性,保证功率分配策略的有效实施。
An adaptive synergetic control strategy is proposed to solve the problem that the control effect of the synergetic control adopted in battery-ultracapacitor hybrid energy storage system of electric vehicles is affected by the equivalent resistance of the ultracapacitor and the input voltage fluctuation caused by the internal resistance of the battery. A synergetic controller is designed based on the ideal state space average model of the DC-DC converter, and its stability is analyzed. Then, an accurate mathematical model of the system is established by considering the equivalent resistance of the ultracapacitor and the battery internal resistance. An adaptive observation function is designed and the adaptive control law is obtained based on the Lyapunov function. The unknown load and varying input voltage are then estimated. Furthermore, a macro-variable is designed, and a duty cycle function of the adaptive synergetic control is derived. Finally, a simulation model of the hybrid energy storage system is established in MATLAB/Simulink to verify the effectiveness of the adaptive synergetic control. Results show that the proposed adaptive synergetic control has slight overshoot and current fluctuation caused by load change, and its response speed is fast(the settling-time is about 4 ms). Moreover, the tracking error is less than 1% in current tracking control. Comparisons with the synergetic control and the sliding mode control show that the adaptive synergetic control has better stability and anti-interference ability. Therefore, the adaptive synergetic control effectively improves the stability and robustness of the hybrid energy storage system, and ensures the effectiveness of power allocation strategy.
An adaptive synergetic control strategy is proposed to solve the problem that the control effect of the synergetic control adopted in battery-ultracapacitor hybrid energy storage system of electric vehicles is affected by the equivalent resistance of the ultracapacitor and the input voltage fluctuation caused by the internal resistance of the battery. A synergetic controller is designed based on the ideal state space average model of the DC-DC converter, and its stability is analyzed. Then, an accurate mathematical model of the system is established by considering the equivalent resistance of the ultracapacitor and the battery internal resistance. An adaptive observation function is designed and the adaptive control law is obtained based on the Lyapunov function. The unknown load and varying input voltage are then estimated. Furthermore, a macro-variable is designed, and a duty cycle function of the adaptive synergetic control is derived. Finally, a simulation model of the hybrid energy storage system is established in MATLAB/Simulink to verify the effectiveness of the adaptive synergetic control. Results show that the proposed adaptive synergetic control has slight overshoot and current fluctuation caused by load change, and its response speed is fast(the settling-time is about 4 ms). Moreover, the tracking error is less than 1% in current tracking control. Comparisons with the synergetic control and the sliding mode control show that the adaptive synergetic control has better stability and anti-interference ability. Therefore, the adaptive synergetic control effectively improves the stability and robustness of the hybrid energy storage system, and ensures the effectiveness of power allocation strategy.
引文
[1]IBRAHIM M,JEMEI S,WIMMER G,et al.Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles[J].Electric Power Systems Research,2016,136:262-269.
[2]HANNAN M A,AZIDIN F A,MOHAMED A.Hybrid electric vehicles and their challenges:a review[J].Renewable and Sustainable Energy Reviews,2014,29:135-150.
[3]续丹,周欢,王斌,等.一种简化级联电动汽车复合电源及其控制方法[J].汽车工程,2017(12):1368-1374.XU Dan,ZHOU Huan,WANG Bin,et al.A simplified cascading hybrid power and its control scheme for electric vehicles[J].Automotive Engineering,2017(12):1368-1374.
[4]WANG Bin,XU Jun,XU Dan,et al.Implementation of an estimator-based adaptive sliding mode control strategy for a boost converter based battery/supercapacitor hybrid energy storage system in electric vehicles[J].Energy Conversion&Management,2017,151:562-572.
[5]SONG Z,HOFMANN H,LI J,et al.A comparison study of different semi-active hybrid energy storage system topologies for electric vehicles[J].Journal of Power Sources,2015,274:400-411.
[6]SHEN J,KHALIGH A.A supervisory energy management control strategy in a battery/ultracapacitor hybrid energy storage system[J].IEEE Transactions on Transportation Electrification,2015,1(3):223-231.
[7]TIE S F,TN C W.A review of energy sources and energy management system in electric vehicles[J].Renewable&Sustainable Energy Reviews,2013,20(4):82-102.
[8]CAO J,EMADI A.A new battery/ultracapacitor hybrid energy storage system for electric,hybrid,and plug-in hybrid electric vehicles[J].IEEE Transactions on Power Electronics,2011,27(1):122-132.
[9]SONG Z,LI J,HAN X,et al.Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles[J].Applied Energy,2014,135:212-224.
[10]KOLLIMALLA S K,MISHRA M K,NARASAM-MA N L.Design and analysis of novel control strategy for battery and supercapacitor storage system[J].IEEE Transactions on Sustainable Energy,2014,5(4):1137-1144.
[11]WANG Bin,MA Guangliang,XU Dan.et al.Switching sliding-mode control strategy based on multi-type restrictive condition for voltage control of buck converter in auxiliary energy source[J].Applied Energy,2018,228:1373-1384.
[12]SANTI E,MONTI A,LI D,et al.Synergetic control for DC-DC boost converter:implementation options[J].IEEE Transactions on Industry Applications,2003,39(6):1803-1813.
[13]ZERROUG N,HARMAS M N,BENAGGOUNE S,et al.DSP-based implementation of fast terminal synergetic control for a DC-DC Buck converter[J].Journal of the Franklin Institute,2018,355(5):2329-2343.
[14]SANTI E,MONTI A,LI D,et al.Synergetic control for power electronics applications:a comparison with the sliding mode approach[J].Journal of Circuits Systems&Computers,2004,13(4):737-760.
[15]聂勇文.基于协同控制理论的水轮发电机组调速系统控制研究[D].武汉:华中科技大学,2016:22-29.
[16]NECHADI E,HARMAS M N,ESSOUNBOULI N,et al.Optimal synergetic control based bat algorithm for DC-DC boost converter[J].IFAC:PapersOnline,2016,49(12):698-703.
[17]王前,李韬.基于变结构的协同控制方法及其在DC/DC开关变换器中应用[J].电力自动化设备,2010,30(4):31-35.WANG Qian,LI Tao.Synergetic control based on variable structure and its application in DC/DC converter[J].Electric Power Automation Equipment,2010,30(4):31-35.
[18]BOUCHAMA Z,HARMAS M N.Optimal robust adaptive fuzzy synergetic power system stabilizer design[J].Electric Power Systems Research,2012,83(1):170-175.
[19]王斌,徐俊,曹秉刚,等.升压型电池-超级电容复合电源的自适应滑模控制[J].西安交通大学学报,2016,50(10):36-41.WANG Bin,XU Jun,CAO Binggang,et al.An adaptive sliding-mode control strategy for hybrid power sources of battery-supercapacitor with a boost converter[J].Journal of Xi’an Jiaotong University,2016,50(10):36-41.
[20]ABDERREZEK H,AISSA A,HARMAS M N.Adaptive non-singular terminal synergetic power system control using PSO[C]∥Proceedings of the International Conference on Modelling,Identification and Control.Piscataway,NJ,USA:IEEE,2017:449-454.
[2]HANNAN M A,AZIDIN F A,MOHAMED A.Hybrid electric vehicles and their challenges:a review[J].Renewable and Sustainable Energy Reviews,2014,29:135-150.
[3]续丹,周欢,王斌,等.一种简化级联电动汽车复合电源及其控制方法[J].汽车工程,2017(12):1368-1374.XU Dan,ZHOU Huan,WANG Bin,et al.A simplified cascading hybrid power and its control scheme for electric vehicles[J].Automotive Engineering,2017(12):1368-1374.
[4]WANG Bin,XU Jun,XU Dan,et al.Implementation of an estimator-based adaptive sliding mode control strategy for a boost converter based battery/supercapacitor hybrid energy storage system in electric vehicles[J].Energy Conversion&Management,2017,151:562-572.
[5]SONG Z,HOFMANN H,LI J,et al.A comparison study of different semi-active hybrid energy storage system topologies for electric vehicles[J].Journal of Power Sources,2015,274:400-411.
[6]SHEN J,KHALIGH A.A supervisory energy management control strategy in a battery/ultracapacitor hybrid energy storage system[J].IEEE Transactions on Transportation Electrification,2015,1(3):223-231.
[7]TIE S F,TN C W.A review of energy sources and energy management system in electric vehicles[J].Renewable&Sustainable Energy Reviews,2013,20(4):82-102.
[8]CAO J,EMADI A.A new battery/ultracapacitor hybrid energy storage system for electric,hybrid,and plug-in hybrid electric vehicles[J].IEEE Transactions on Power Electronics,2011,27(1):122-132.
[9]SONG Z,LI J,HAN X,et al.Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles[J].Applied Energy,2014,135:212-224.
[10]KOLLIMALLA S K,MISHRA M K,NARASAM-MA N L.Design and analysis of novel control strategy for battery and supercapacitor storage system[J].IEEE Transactions on Sustainable Energy,2014,5(4):1137-1144.
[11]WANG Bin,MA Guangliang,XU Dan.et al.Switching sliding-mode control strategy based on multi-type restrictive condition for voltage control of buck converter in auxiliary energy source[J].Applied Energy,2018,228:1373-1384.
[12]SANTI E,MONTI A,LI D,et al.Synergetic control for DC-DC boost converter:implementation options[J].IEEE Transactions on Industry Applications,2003,39(6):1803-1813.
[13]ZERROUG N,HARMAS M N,BENAGGOUNE S,et al.DSP-based implementation of fast terminal synergetic control for a DC-DC Buck converter[J].Journal of the Franklin Institute,2018,355(5):2329-2343.
[14]SANTI E,MONTI A,LI D,et al.Synergetic control for power electronics applications:a comparison with the sliding mode approach[J].Journal of Circuits Systems&Computers,2004,13(4):737-760.
[15]聂勇文.基于协同控制理论的水轮发电机组调速系统控制研究[D].武汉:华中科技大学,2016:22-29.
[16]NECHADI E,HARMAS M N,ESSOUNBOULI N,et al.Optimal synergetic control based bat algorithm for DC-DC boost converter[J].IFAC:PapersOnline,2016,49(12):698-703.
[17]王前,李韬.基于变结构的协同控制方法及其在DC/DC开关变换器中应用[J].电力自动化设备,2010,30(4):31-35.WANG Qian,LI Tao.Synergetic control based on variable structure and its application in DC/DC converter[J].Electric Power Automation Equipment,2010,30(4):31-35.
[18]BOUCHAMA Z,HARMAS M N.Optimal robust adaptive fuzzy synergetic power system stabilizer design[J].Electric Power Systems Research,2012,83(1):170-175.
[19]王斌,徐俊,曹秉刚,等.升压型电池-超级电容复合电源的自适应滑模控制[J].西安交通大学学报,2016,50(10):36-41.WANG Bin,XU Jun,CAO Binggang,et al.An adaptive sliding-mode control strategy for hybrid power sources of battery-supercapacitor with a boost converter[J].Journal of Xi’an Jiaotong University,2016,50(10):36-41.
[20]ABDERREZEK H,AISSA A,HARMAS M N.Adaptive non-singular terminal synergetic power system control using PSO[C]∥Proceedings of the International Conference on Modelling,Identification and Control.Piscataway,NJ,USA:IEEE,2017:449-454.