车用动力镍氢电池SOC建模与仿真
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摘要
随着电动汽车的迅速发展,一直以来困扰车用动力电池的成本、寿命问题迫切需要得到解决。现在的动力电池还未达到车载能源的要求,研究电池荷电状态(SOC),在电动汽车运行过程中对镍氢电池SOC实时监测,能让电池的能量得到最大限度的利用,延长电池寿命。本文针对车用镍氢电池SOC估算的主要工作如下:
首先分析了镍氢电池的工作特性、影响SOC的因素,综合比较了几种传统的估算电池SOC的方法如电量累计法,电阻测量法,开路电压法,模糊逻辑推理,神经网络的方法和恢复效应法等,在它们的基础上提出了进一步的建模思想。设计了在不同条件下的充放电实验,通过大量的实验测试数据,研究了镍氢电池充放电过程,详细分析了电池开路电压、电池温度特性和自放电对SOC估计的影响。
实验建模研究工作分静态建模和动态建模两个部分。根据试验采集电池充放电过程中的充放电电流、开路电压和环境温度等数据,分别建立了它们与电池SOC的静态数学模型。然后根据各条件下建立的静态数学模型深入地研究了电动汽车的动力镍氢电池SOC的估算方法,提出了基于状态空间的SOC递推估算法的动态建模思想与实现方法,即在复杂条件下估测电动汽车镍氢电池的SOC。该动态模型考虑了在车载行驶条件下,电流不断变化,环境温度等因素的影响。最后对电池SOC的动态数学模型进行了仿真,并将仿真结果与开路电压法测量的SOC曲线相比较,证明了模型的准确性。
本课题的研究在理论和实践中都取得了进展,对于进一步研究镍氢电池SOC具有重要的意义。
With the fast development of the electric vehicle, the cost of battery and the life problem of the urgent need to be resolved. The power battery had not yet reached car energy demands. Research on SOC of battery, monitoring SOC of MH-Ni battery real-time in the operation of electric vehicles can maximize the use of extended battery life. The main work in this paper in allusion to the soc estimation on electric vehicle MH-Ni battery is as follows:
First the characteristic of the MH-Ni batteries and the factors influenced on the SOC have been analyzed and different kinds of SOC traditional measuring methods have been compared such as electricity, resistance measurement, voltage measurement method, fuzzy logic, neural network method and the restoration of law. Multianalysis the influences among the estimating on SOC and the battery charge-discharge efficiency, the battery terminal voltage, the battery temperature characteristics and the self-discharge rate and so on through the experiments. Designed a series of charge-discharge experiments under different conditions, through a large number of experimental test data on MH-Ni battery charging and discharging process, compared the charge and discharge characteristics of the MH-Ni battery under different conditions, and the impact of environmental temperature on the MH-Ni battery discharge performance.
The work is divided into two parts: the static model and dynamic model. According to gather the voltage of the battery, the temperature and the charging and discharging current historical data, establishes static mathematical model under the dissimilar conditions. Then according to the static mathematical models under various conditions, establishes dynamic mathematical model with the consideration of temperature effect and constantly changing. Finally simulating the dynamic model in the matlab, and then compared the result with the data measured by voltage measurement method. The result proved the model is accurate.
The research on the subject has made some progress in the theory and the practice, and it has vital significance for further studies on SOC of MH-Ni battery.
首先分析了镍氢电池的工作特性、影响SOC的因素,综合比较了几种传统的估算电池SOC的方法如电量累计法,电阻测量法,开路电压法,模糊逻辑推理,神经网络的方法和恢复效应法等,在它们的基础上提出了进一步的建模思想。设计了在不同条件下的充放电实验,通过大量的实验测试数据,研究了镍氢电池充放电过程,详细分析了电池开路电压、电池温度特性和自放电对SOC估计的影响。
实验建模研究工作分静态建模和动态建模两个部分。根据试验采集电池充放电过程中的充放电电流、开路电压和环境温度等数据,分别建立了它们与电池SOC的静态数学模型。然后根据各条件下建立的静态数学模型深入地研究了电动汽车的动力镍氢电池SOC的估算方法,提出了基于状态空间的SOC递推估算法的动态建模思想与实现方法,即在复杂条件下估测电动汽车镍氢电池的SOC。该动态模型考虑了在车载行驶条件下,电流不断变化,环境温度等因素的影响。最后对电池SOC的动态数学模型进行了仿真,并将仿真结果与开路电压法测量的SOC曲线相比较,证明了模型的准确性。
本课题的研究在理论和实践中都取得了进展,对于进一步研究镍氢电池SOC具有重要的意义。
With the fast development of the electric vehicle, the cost of battery and the life problem of the urgent need to be resolved. The power battery had not yet reached car energy demands. Research on SOC of battery, monitoring SOC of MH-Ni battery real-time in the operation of electric vehicles can maximize the use of extended battery life. The main work in this paper in allusion to the soc estimation on electric vehicle MH-Ni battery is as follows:
First the characteristic of the MH-Ni batteries and the factors influenced on the SOC have been analyzed and different kinds of SOC traditional measuring methods have been compared such as electricity, resistance measurement, voltage measurement method, fuzzy logic, neural network method and the restoration of law. Multianalysis the influences among the estimating on SOC and the battery charge-discharge efficiency, the battery terminal voltage, the battery temperature characteristics and the self-discharge rate and so on through the experiments. Designed a series of charge-discharge experiments under different conditions, through a large number of experimental test data on MH-Ni battery charging and discharging process, compared the charge and discharge characteristics of the MH-Ni battery under different conditions, and the impact of environmental temperature on the MH-Ni battery discharge performance.
The work is divided into two parts: the static model and dynamic model. According to gather the voltage of the battery, the temperature and the charging and discharging current historical data, establishes static mathematical model under the dissimilar conditions. Then according to the static mathematical models under various conditions, establishes dynamic mathematical model with the consideration of temperature effect and constantly changing. Finally simulating the dynamic model in the matlab, and then compared the result with the data measured by voltage measurement method. The result proved the model is accurate.
The research on the subject has made some progress in the theory and the practice, and it has vital significance for further studies on SOC of MH-Ni battery.
引文
[1]麻友良,陈全世,齐占宁.电动汽车用电池SOC定义与检测方法[J].清华大学学报.2001.41(11):95-98
[2]林成涛,王军平,陈全世.电动汽车SOC估计方法原理与应用[J].电池.2004,34(5):376-378
[3]韩晓东等.判断电动车电池放电终止状态的新标准[J].电池.2004,32(2):117-120
[4]李敬兆,张崇巍.基于模糊神经网络预测技术检测蓄电池剩余电量研究[J].煤矿机械.2002(8):21-24
[5]邓超,史鹏飞.基于神经网络的MH/Ni电池剩余容量预测[J].哈尔滨工业大学学报.2003,35(11):1405-1408
[6]朱元,韩晓东,田光宇.电动汽车动力电池SOC预测技术研究[J].电源技术.2000,24(3):153-156
[7]宫学庚等.电动汽车动力电池模型和SOC估算策略[J].电源技术.2004,28(10):633-636
[8]彭涛,陈全世.并联混合动力电动汽车的模糊能量管理策略[J].中国机械工程.2003,14(9):797-801
[9]Emiliano Paolin,Giuseppe Lodi,David Shaffer.A Battery Management System for Sodium-metal Chloride Batteries.Proceedings of the 26th Intelec.2004
[10]杨朔,何莉萍,钟志华.基于CAN总线的电动汽车电池管理系统[J].贵州工业大学学报.2004,33(2):90-94
[11]杨朔,何莉萍,钟志华.电动汽车蓄电池荷电状态的卡尔曼滤波估计[J].贵州工业大学学报.2004,33(1):99-102
[12]林成涛,仇斌,陈全世.电动汽车电池非线性等效电路模型的研究[J].汽车工程.2006,28(1):38-43
[13]邓文莲,徐鸣谦,沈勇.电动车用Ni-MH电池SOC预测方法的探讨[J].通信电源技术.2004,21(4):33-36
[14]邵海岳,钟志华,何莉萍,钟勇陈,宗璋.电动汽车用Ni-MH电池建模及基于状态空间的SOC预测方法[J].汽车工程.2004,26(5):535-537
[15]黄妙华,龚海峰.车用电池网格模型及其在ADVISOR中的实现[J].武汉理工大学报.2003,25(1):68-71
[16]林成涛等.用改进的安时计量法估计电动汽车动力电池SOC[J].清华大学报.2006,46(2):247-251
[17]陆勇,方杰.电动车用MH-Ni电池SOC模型的研究[J].电池工业.2006,11(5):307-310
[18]邵桂欣等.电动车用氢镍电池特性和工况实验分析[J].电池工业.2006,11(1):307-310
[19]张菊秀,全书海,刘倩.基于CAN总线的蓄电池SOC动态测量系统[J].微计算机息.2006,22(5):108-109
[20]刘倩.基于人工神经网络的电池容量预测[J].武汉理工大学学报.2006,28(3):28-31
[21]陈鸿雁,李山,王培容.镍氢电池剩余容量检测系统的研究[J].重庆工学院报.2006,20(11):50-53
[22]Francesco Vasca,Luigi Glielmo,Osvaldo Barbarisi.State of Charge Estimation for Batteries.Control Engineering Practice.2006,14(3):267-275
[23]王军平等.镍氢电池组的荷电状态估计方法研究[J].机械工程学报.2005,41(12):62-65
[24]张毅等.燃料电池汽车动力蓄电池管理系统硬件在环仿真应用[J].广西大学报.2006,31(4):292-296
[25]John Chiasson,Baskar Vairamohan.Estimating the State of Charge of a Battery.IEEE Power Engineedng Society Winter Meeting.2000(1):470-475
[26]卢居霄等.三类常用电动汽车电池模型的比较研究[J].电源技术.2006,30(7):535-538
[27]Huet F.A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries.J Power Sources,1998,70(1):59-69.
[28]周智敏等.充电器电路设计与应用[M].北京:人民邮电出版社.2005,10
[29]B.Powell,T.Pilutti.Measurement of Battery Energy Monitoring System.Ford Scientific Research.2002,12
[30]乔国艳,曾春年.电池管理系统下层ECU的研制[J].武汉理工大学学报.2004,26(4):48-51
[31]孙德新.VRLA蓄电池运行监测管理系统的研究[J].电源世界.2005(12):66-68
[32]Baskar Vairmohan.State of Charge Estimation for Batteries[M].Published By The University of Tennessee.2002
[33]薛建军,唐致远,刘建华,王占良.人工神经网络法预测MH-Ni蓄电池容量[J].低压电源.2003,27(3):305-307
[34]M.A.Casacca,Z.M.Salameh.BCM Battery and Cell Management System.Ford Scientific Research.2002,8
[35]齐智等.利用人工神经网络预测电池SOC的研究[J].电源技术.2005,29(5):325-328
[36]S.R.Ovshinsky,M.A.Fetcenko.On-line Battery Cell Management System.14th International Seminar on Primary and Secondary Batteries.2002
[37]姜久春,牛利勇,张欣.混合动力汽车管理系统的研究[J].高技术通讯.2004(6):75-77
[38]范美强,廖维林,吴伯荣,陈晖,简旭宇.电动车用MH-Ni电池温度特性研究[J].电池业.2004,9(6):287-289
[39]王国玉等.电子系统建模仿真与评估[M].长沙:国防科技大学出版社.1999,9
[40]Sabine P,Marion P,Andreas J.Methods for state-of-charge determination and their applications.J Power Sources,2001,96(1):113-120.
[41]贺秀芸.一种大功率智能充电器的研制[J].山东内燃机.2004,(4):32-34
[42]Sharon Ahlers,Anirban Mukheriee.Advances in Battery Management Using Neural Networks and Fuzzy Logic.Advances in battery management.2001,80:286-292
[43]马竞展,左曙光,何志生.基于Matlab/Simulink环境四轮驱动混合动力汽车建模与仿真[J].新能源汽车.2005.11
[44]张志涌等.精通MATLAB[M].北京航空航天大学出版社.2005,9
[45]Zhu Chunbo,Wang Tiecheng,Hurl Eywg.A New State of Charge Determination Method for Battery Management System.Harbin Institute of Technology 2004,11(6)
[46]Darren Lim,Adnan Anbuky.A Distributed Industrial Battery Management Network.IEEE Transactions On Industrial Electronics.2004,51(6)
[47]HUANG K D,TZENG S C.Development of a hybridpneumatic-power vehicle[J].Appliced Energy,2005,80(1):56.
[2]林成涛,王军平,陈全世.电动汽车SOC估计方法原理与应用[J].电池.2004,34(5):376-378
[3]韩晓东等.判断电动车电池放电终止状态的新标准[J].电池.2004,32(2):117-120
[4]李敬兆,张崇巍.基于模糊神经网络预测技术检测蓄电池剩余电量研究[J].煤矿机械.2002(8):21-24
[5]邓超,史鹏飞.基于神经网络的MH/Ni电池剩余容量预测[J].哈尔滨工业大学学报.2003,35(11):1405-1408
[6]朱元,韩晓东,田光宇.电动汽车动力电池SOC预测技术研究[J].电源技术.2000,24(3):153-156
[7]宫学庚等.电动汽车动力电池模型和SOC估算策略[J].电源技术.2004,28(10):633-636
[8]彭涛,陈全世.并联混合动力电动汽车的模糊能量管理策略[J].中国机械工程.2003,14(9):797-801
[9]Emiliano Paolin,Giuseppe Lodi,David Shaffer.A Battery Management System for Sodium-metal Chloride Batteries.Proceedings of the 26th Intelec.2004
[10]杨朔,何莉萍,钟志华.基于CAN总线的电动汽车电池管理系统[J].贵州工业大学学报.2004,33(2):90-94
[11]杨朔,何莉萍,钟志华.电动汽车蓄电池荷电状态的卡尔曼滤波估计[J].贵州工业大学学报.2004,33(1):99-102
[12]林成涛,仇斌,陈全世.电动汽车电池非线性等效电路模型的研究[J].汽车工程.2006,28(1):38-43
[13]邓文莲,徐鸣谦,沈勇.电动车用Ni-MH电池SOC预测方法的探讨[J].通信电源技术.2004,21(4):33-36
[14]邵海岳,钟志华,何莉萍,钟勇陈,宗璋.电动汽车用Ni-MH电池建模及基于状态空间的SOC预测方法[J].汽车工程.2004,26(5):535-537
[15]黄妙华,龚海峰.车用电池网格模型及其在ADVISOR中的实现[J].武汉理工大学报.2003,25(1):68-71
[16]林成涛等.用改进的安时计量法估计电动汽车动力电池SOC[J].清华大学报.2006,46(2):247-251
[17]陆勇,方杰.电动车用MH-Ni电池SOC模型的研究[J].电池工业.2006,11(5):307-310
[18]邵桂欣等.电动车用氢镍电池特性和工况实验分析[J].电池工业.2006,11(1):307-310
[19]张菊秀,全书海,刘倩.基于CAN总线的蓄电池SOC动态测量系统[J].微计算机息.2006,22(5):108-109
[20]刘倩.基于人工神经网络的电池容量预测[J].武汉理工大学学报.2006,28(3):28-31
[21]陈鸿雁,李山,王培容.镍氢电池剩余容量检测系统的研究[J].重庆工学院报.2006,20(11):50-53
[22]Francesco Vasca,Luigi Glielmo,Osvaldo Barbarisi.State of Charge Estimation for Batteries.Control Engineering Practice.2006,14(3):267-275
[23]王军平等.镍氢电池组的荷电状态估计方法研究[J].机械工程学报.2005,41(12):62-65
[24]张毅等.燃料电池汽车动力蓄电池管理系统硬件在环仿真应用[J].广西大学报.2006,31(4):292-296
[25]John Chiasson,Baskar Vairamohan.Estimating the State of Charge of a Battery.IEEE Power Engineedng Society Winter Meeting.2000(1):470-475
[26]卢居霄等.三类常用电动汽车电池模型的比较研究[J].电源技术.2006,30(7):535-538
[27]Huet F.A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries.J Power Sources,1998,70(1):59-69.
[28]周智敏等.充电器电路设计与应用[M].北京:人民邮电出版社.2005,10
[29]B.Powell,T.Pilutti.Measurement of Battery Energy Monitoring System.Ford Scientific Research.2002,12
[30]乔国艳,曾春年.电池管理系统下层ECU的研制[J].武汉理工大学学报.2004,26(4):48-51
[31]孙德新.VRLA蓄电池运行监测管理系统的研究[J].电源世界.2005(12):66-68
[32]Baskar Vairmohan.State of Charge Estimation for Batteries[M].Published By The University of Tennessee.2002
[33]薛建军,唐致远,刘建华,王占良.人工神经网络法预测MH-Ni蓄电池容量[J].低压电源.2003,27(3):305-307
[34]M.A.Casacca,Z.M.Salameh.BCM Battery and Cell Management System.Ford Scientific Research.2002,8
[35]齐智等.利用人工神经网络预测电池SOC的研究[J].电源技术.2005,29(5):325-328
[36]S.R.Ovshinsky,M.A.Fetcenko.On-line Battery Cell Management System.14th International Seminar on Primary and Secondary Batteries.2002
[37]姜久春,牛利勇,张欣.混合动力汽车管理系统的研究[J].高技术通讯.2004(6):75-77
[38]范美强,廖维林,吴伯荣,陈晖,简旭宇.电动车用MH-Ni电池温度特性研究[J].电池业.2004,9(6):287-289
[39]王国玉等.电子系统建模仿真与评估[M].长沙:国防科技大学出版社.1999,9
[40]Sabine P,Marion P,Andreas J.Methods for state-of-charge determination and their applications.J Power Sources,2001,96(1):113-120.
[41]贺秀芸.一种大功率智能充电器的研制[J].山东内燃机.2004,(4):32-34
[42]Sharon Ahlers,Anirban Mukheriee.Advances in Battery Management Using Neural Networks and Fuzzy Logic.Advances in battery management.2001,80:286-292
[43]马竞展,左曙光,何志生.基于Matlab/Simulink环境四轮驱动混合动力汽车建模与仿真[J].新能源汽车.2005.11
[44]张志涌等.精通MATLAB[M].北京航空航天大学出版社.2005,9
[45]Zhu Chunbo,Wang Tiecheng,Hurl Eywg.A New State of Charge Determination Method for Battery Management System.Harbin Institute of Technology 2004,11(6)
[46]Darren Lim,Adnan Anbuky.A Distributed Industrial Battery Management Network.IEEE Transactions On Industrial Electronics.2004,51(6)
[47]HUANG K D,TZENG S C.Development of a hybridpneumatic-power vehicle[J].Appliced Energy,2005,80(1):56.