燃料电池汽车运用有限状态机优化能量管理系统的研究
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摘要
为了保证燃料电池汽车的动力性及功率平衡控制,充分发挥燃料电池与蓄电池的优势,需对功率分配进行合理控制。针对"燃料电池+蓄电池(FC+B)"混合储能系统电动汽车,提出一种改进的状态机能量管理优化策略,引入燃料电池最小输出功率与蓄电池最佳充电功率,根据负载需求功率及蓄电池的荷电状态(SOC)动态调整能量流在不同动力源的分配,提高整车经济性。通过MATLAB/Simulink对所提出的动态能量管理策略进行验证,并与功率追踪策略作对比。仿真结果证明,所提出的能量管理策略能满足实际工况中的功率需求,控制更加灵活,具有一定的实用价值。
In order to ensure the dynamic performance and power balance of fuel cell vehicles,give full play to the advantages of fuel cell and battery,it is necessary to control the power distribution reasonably. An improved finite state machine energy management optimization strategy was proposed for electric vehicles with hybrid energy storage system of"fuel cell + battery( FC + B) ". The minimum output power of fuel cell and the best charging power of battery were introduced. The state of charge( SOC) of battery and load demand power were used to dynamically adjust the distribution of energy flow in different power sources to improve the vehicle economy.Through MATLAB/Simulink,the proposed dynamic energy management strategy was verified and compared with the power tracking strategy. The simulation results show that the proposed energy management strategy can meet the power demand in the actual working condition. The control is more flexible and has certain practical value.
In order to ensure the dynamic performance and power balance of fuel cell vehicles,give full play to the advantages of fuel cell and battery,it is necessary to control the power distribution reasonably. An improved finite state machine energy management optimization strategy was proposed for electric vehicles with hybrid energy storage system of"fuel cell + battery( FC + B) ". The minimum output power of fuel cell and the best charging power of battery were introduced. The state of charge( SOC) of battery and load demand power were used to dynamically adjust the distribution of energy flow in different power sources to improve the vehicle economy.Through MATLAB/Simulink,the proposed dynamic energy management strategy was verified and compared with the power tracking strategy. The simulation results show that the proposed energy management strategy can meet the power demand in the actual working condition. The control is more flexible and has certain practical value.
引文
[1]刘宗巍,史天泽,郝瀚,等.中国燃料电池汽车发展问题研究[J].汽车技术,2018(1):1-9.
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[12]赵泽昆,韩晓娟,马会萌.基于BP神经网络的储能电池衰减容量预测[J].电器与能效管理技术,2016(19):68-72.
[13]刘佳,周强.我国燃料电池汽车及用氢发展现状浅析[J].太阳能,2017(4):24-29.
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[15]陈维荣,张国瑞,孟翔,等.燃料电池混合动力有轨电车动力性分析与设计[J].西南交通大学学报,2017,52(1):1-8.
[16] MOKRANI Z,REKIOUA D,MEBARKI N,et al.Proposed energy management strategy in electric vehicle for recovering power excess produced by fuel cells[J]. International Journal of Hydrogen Energy,2017,42(30):19556-19575.
[17]王凯,夏国廷,董鹏,等.一种改进的超级电容器模型及其热行为研究[J].电器与能效管理技术,2018(11):28-33.
[18] MUOZ P M,CORREA G,GAUDIANO M E,et al.Energy management control design for fuel cell hybrid electric vehicles using neural networks[J].International Journal of Hydrogen Energy,2017,42(48):28932-28944.
[19] XU L,OUYANG M,LI J,et al. Application of pontryagi’ s minimal principle to the energy management strategy of plugin fuel cell electric vehicles[J]. International Journal of Hydrogen Energy,2013,38(24):10104-10115.
[20] XIE C,OGDEN J M,QUAN S,et al. Optimal power management for fuel cell-battery full hybrid powertrain on a test station[J]. International Journal of Electrical Power&Energy Systems,2013,53:307-320.
[2]高辉,陈良亮.纯电动汽车能量补给技术现状及发展趋势[J].电器与能效管理技术,2015(7):63-66.
[3]王诚,王树博,张剑波,等.车用燃料电池耐久性研究[J].化学进展,2015,27(4):424-435.
[4]余晓玲,王春玲,韩晓销.基于数据挖掘技术的电池储能系统SOC状态评估[J].电器与能效管理技术,2017(13):68-73.
[5]柳丹,袁晓冬,李强,等.储能用锂离子电池健康状态预测方法研究[J].电器与能效管理技术,2017(11):36-39.
[6]王诚,王树博,张剑波,等.车用质子交换膜燃料电池材料部件[J].化学进展,2015,27(2):310-320.
[7]李欣然,黄际元,陈远扬,等.大规模储能电源参与电网调频研究综述[J].电力系统保护与控制,2016,44(7):145-153.
[8] PAN L,LIU P,LI Z. A system dynamic analysis of China’s oil supply chain:Over-capacity and energy security issues[J]. Applied Energy,2017,188:508-520.
[9]李通,郑松林,陈铁,等.基于灰色系统理论的燃料电池轿车可靠性分配[J].机械强度,2017,39(5):1072-1078.
[10]陈永翀,李爱晶,刘丹丹,等.储能技术在能源互联网系统中应用与发展展望[J].电器与能效管理技术,2015(24):39-44.
[11]元勇伟,许思传,万玉.燃料电池汽车动力总成方案分析[J].电源技术,2017,41(1):165-168.
[12]赵泽昆,韩晓娟,马会萌.基于BP神经网络的储能电池衰减容量预测[J].电器与能效管理技术,2016(19):68-72.
[13]刘佳,周强.我国燃料电池汽车及用氢发展现状浅析[J].太阳能,2017(4):24-29.
[14]陈明帅,华青松,张洪伟,等.燃料电池/蓄电池混合动力汽车能量管理系统研究[J].青岛大学学报(工程技术版),2018,33(1):21-27.
[15]陈维荣,张国瑞,孟翔,等.燃料电池混合动力有轨电车动力性分析与设计[J].西南交通大学学报,2017,52(1):1-8.
[16] MOKRANI Z,REKIOUA D,MEBARKI N,et al.Proposed energy management strategy in electric vehicle for recovering power excess produced by fuel cells[J]. International Journal of Hydrogen Energy,2017,42(30):19556-19575.
[17]王凯,夏国廷,董鹏,等.一种改进的超级电容器模型及其热行为研究[J].电器与能效管理技术,2018(11):28-33.
[18] MUOZ P M,CORREA G,GAUDIANO M E,et al.Energy management control design for fuel cell hybrid electric vehicles using neural networks[J].International Journal of Hydrogen Energy,2017,42(48):28932-28944.
[19] XU L,OUYANG M,LI J,et al. Application of pontryagi’ s minimal principle to the energy management strategy of plugin fuel cell electric vehicles[J]. International Journal of Hydrogen Energy,2013,38(24):10104-10115.
[20] XIE C,OGDEN J M,QUAN S,et al. Optimal power management for fuel cell-battery full hybrid powertrain on a test station[J]. International Journal of Electrical Power&Energy Systems,2013,53:307-320.