磷酸铁锂电池建模及SOC算法研究
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摘要
近年来,随着能源危机、环境问题的日益突出,磷酸铁锂电池以其自身的安全性、无污染等众多性能优点逐渐受到广泛的关注,在汽车,后备电源等领域得到了广泛的应用。为深入对电池的研究,建立符合电池特性的准确模型对于电池管理系统及工程开发有着十分重要的意义,而作为电动汽车的动力电池而言,准确的荷电状态(SOC)的估计对提高电池使用寿命和整车性能都具有重要意义。
为了建立准确的电池模型,本论文进行了大量的充放电试验来对磷酸铁锂电池的性能特性进行研究,在不同温度、不同放电倍率下,不同的荷电状态下进行HPPC复合脉冲充放电测试,获取充放电双方向上的参数。结合现有等效电路模型进行一阶和二阶模型的改进。通过在三种工况下与实际工况响应值进行对比验证,建立的两种模型都具有较高的精度,其中在复杂工况下一阶修正模型的平均误差为0.025V,二阶修正模型的平均误差为0.0103V,为后文扩展卡尔曼滤波算法估算SOC提供了准确的模型。
作为电动汽车发展的核心技术之一的动力电池的荷电状态(SOC)估算,是电动汽车产业化、实用化的关键。本文在前文建立的模型基础上,采用扩展卡尔曼滤波算法对电池的剩余电量进行估算。通过在四种不同工况下对卡尔曼滤波算法进行对比,一阶模型的卡尔曼滤波算法的精度在恒工况下要略高于二阶模型算法的精度,二阶模型算法在变工况下有更高的精度。通过在复杂工况下对二阶模型的收敛性验证发现,在初始误差为20%时,电池经过9分钟的运行后,算法估算的精度收敛到5%左右。初始误差为50%时,算法经过40分钟的调整后,算法的估计精度收敛到10%以内。
文中建立的修正模型能较好的反映电池动态性能,基本能满足实际的仿真需求和精度;电池的荷电状态估算获得了较高的精度,有良好的动态适应性,并且具有较快的收敛速度。
In recent years, the energy crisis and the environmental problems have become more prominent, LiFePO4 batteries to its own security, no pollution and many other performance characteristics are gradually to be concerned, and have a wide range of applications in the car and backup power supply etc .to be the further study of the battery, setting up accurate model of characteristic for batteries battery management system in engineering development has a very important significance, as the electric car in the power battery, accurate state-of-charge (SOC) estimates to improve battery life and vehicle performance is very important.
In order to establish the accurate battery model, this paper made a lot of experiment of charging and discharging LiFePO4 batteries to test the battery performance characteristics, based on the existing first and second order model, the model was improved through the HPPC composite pulse condition acquire the parameters of different temperature、different discharge rate and different state of charge. compared the two models in the three conditions with dynamic response value of the voltage and gained a specific test precision, in the complex conditions, the first-order correction in the model has the average error of 0.025V, the average second-order error correction model is 0.0103V, and later provides accurate models for extended kalman filter algorithm to estimate SOC.
The state of charge (SOC) estimation as power battery is the one of core technology of the electric car development, it’s the key of utility and industrialization. In this paper, basis for setting up the model, battery remaining power are calculated by extended kalman filter. Through compared in varying conditions based on the setting model, First-order model kalman filter algorithm accuracy is better than the second-order in the constant conditions. But in the changing conditions, the second-order models have higher accuracy. Complex conditions through the convergence of second-order model validation found that the initial error of 20%, the battery after a nine-minute run, the algorithm converges to the estimated accuracy of 5% around; the initial error of 50%, After 40 minutes of adjustment algorithm, the algorithm converges to the estimated accuracy of 10% range.
The improved model of this paper can better reflect the dynamic performance of battery, and can meet the practical needs of the simulation, the state of charge estimation of battery also have the high accuracy, good dynamic adaptability, and has a faster convergence speed.
为了建立准确的电池模型,本论文进行了大量的充放电试验来对磷酸铁锂电池的性能特性进行研究,在不同温度、不同放电倍率下,不同的荷电状态下进行HPPC复合脉冲充放电测试,获取充放电双方向上的参数。结合现有等效电路模型进行一阶和二阶模型的改进。通过在三种工况下与实际工况响应值进行对比验证,建立的两种模型都具有较高的精度,其中在复杂工况下一阶修正模型的平均误差为0.025V,二阶修正模型的平均误差为0.0103V,为后文扩展卡尔曼滤波算法估算SOC提供了准确的模型。
作为电动汽车发展的核心技术之一的动力电池的荷电状态(SOC)估算,是电动汽车产业化、实用化的关键。本文在前文建立的模型基础上,采用扩展卡尔曼滤波算法对电池的剩余电量进行估算。通过在四种不同工况下对卡尔曼滤波算法进行对比,一阶模型的卡尔曼滤波算法的精度在恒工况下要略高于二阶模型算法的精度,二阶模型算法在变工况下有更高的精度。通过在复杂工况下对二阶模型的收敛性验证发现,在初始误差为20%时,电池经过9分钟的运行后,算法估算的精度收敛到5%左右。初始误差为50%时,算法经过40分钟的调整后,算法的估计精度收敛到10%以内。
文中建立的修正模型能较好的反映电池动态性能,基本能满足实际的仿真需求和精度;电池的荷电状态估算获得了较高的精度,有良好的动态适应性,并且具有较快的收敛速度。
In recent years, the energy crisis and the environmental problems have become more prominent, LiFePO4 batteries to its own security, no pollution and many other performance characteristics are gradually to be concerned, and have a wide range of applications in the car and backup power supply etc .to be the further study of the battery, setting up accurate model of characteristic for batteries battery management system in engineering development has a very important significance, as the electric car in the power battery, accurate state-of-charge (SOC) estimates to improve battery life and vehicle performance is very important.
In order to establish the accurate battery model, this paper made a lot of experiment of charging and discharging LiFePO4 batteries to test the battery performance characteristics, based on the existing first and second order model, the model was improved through the HPPC composite pulse condition acquire the parameters of different temperature、different discharge rate and different state of charge. compared the two models in the three conditions with dynamic response value of the voltage and gained a specific test precision, in the complex conditions, the first-order correction in the model has the average error of 0.025V, the average second-order error correction model is 0.0103V, and later provides accurate models for extended kalman filter algorithm to estimate SOC.
The state of charge (SOC) estimation as power battery is the one of core technology of the electric car development, it’s the key of utility and industrialization. In this paper, basis for setting up the model, battery remaining power are calculated by extended kalman filter. Through compared in varying conditions based on the setting model, First-order model kalman filter algorithm accuracy is better than the second-order in the constant conditions. But in the changing conditions, the second-order models have higher accuracy. Complex conditions through the convergence of second-order model validation found that the initial error of 20%, the battery after a nine-minute run, the algorithm converges to the estimated accuracy of 5% around; the initial error of 50%, After 40 minutes of adjustment algorithm, the algorithm converges to the estimated accuracy of 10% range.
The improved model of this paper can better reflect the dynamic performance of battery, and can meet the practical needs of the simulation, the state of charge estimation of battery also have the high accuracy, good dynamic adaptability, and has a faster convergence speed.
引文
[1]王秉刚. 2010高技术发展报告[R].北京:中国科学院, 2010: 266-272.
[2]蒋志君.锂离子电池正极材料磷酸铁锂:进展与挑战[J].功能材料.2010,41(3):365～368.
[3]阮勇,吴罡,滕达磷.酸铁锂电池及其在通信行业中的应用[J].邮电设计技术,2011,03:73-76.
[4]陈全世,林成涛.电动汽车用电池性能模型研究综述[J].汽车技术,2005,(3):1-5.
[5]邵海岳等.电动汽车动力电池模型及SOC预测方法[J].电源技术,2004,(10): 637-640.
[6] Min Chen, Gabriel A, Rincon-Mora. Accurate Electrical Battery Model Capable of Predicting Run time and I-V Performance[J].IEEE Transations on Energy Conversion. Vol.21, No.2, June 2006: 504-511.
[7] SA Khateeb, MM Farid, JR Selman, et al. Mechanical-electrochemical modeling of Li-ion battery designed for an electric scooter [J]. Journal of Power Sources, 2006, 158 (1): 673-678.
[8] John Newman, Karen E Thomas, Hooman Hafezi, et al. Modeling of Lithium-ion batteries [J]. Journal of Power Sources, 2003, 119 (1): 838-843.
[9] HALLAJ S A, MALEKI H, HONG J S, et al.Thermal modeling and design considerations of lithium-ion batteries[J]. J Power Sources, 1999, 83: 1-8.
[10] SATO N. Thermal behavior analysis of lithium-ion batteries for electric and hybrid vehicles[J]. J Power Sources, 2001, 99: 70-77.
[11] Bergveld H J. Battery Management Systems Design by Modelling [M]. springer, 2001.
[12]李革臣,古艳磊.电化学阻抗谱法预测锂电池荷电状态[J].电源技术.2008,32(9):599-602.
[13] Rodrigues S, Muniehandraiah N, Shukla A K. Review of state-of-charge Indieation of batteries by means of a.c.impedance measurements[J]. 2000, 87(1):12-20.
[14] GREGORY L P. LiPB dynamic cell models for kalman-filter SOC estimation[A]. Proceedings of the 19th International Electric Vehicle Symposium[C], Pusan: EVS, 2002. 1860-1871.
[15] O'GORMAN C C,INGERSOLL D,JUNGST R G, et al. Artificial neural network simulation of battery performance[A]. Proc 31st An-nual Hawaii International Conference on System Sciences[C], Hawaii: ICSS, 1998. 461-472.
[16] Chen Y F, Evans JW. Heat Transfer Phenomena in Lithium/Polymer-Electrolyte Batteries for Electric Vehicle Application [J].Electrochem.Soc. 1993.
[17] HALLAJ S A,SELMAN J R. Thermal modeling of secondary lithium batteries for electric vehicle/hybrid electric vehicle applications[J]. J Power Sources, 2002, 110: 341-348.
[18] CHEN S C,WANG Y Y,WANG C C. Thermal analysis of spirally wound lithium batteries[J]. J Electrochemical Society, 2006, 153(4): 637-648.
[19] KIM G H,PESARAN. A.Battery thermal management system design modeling[C]//The 22nd International Battery,Hybrid and Fuel Cell Electric Vehicle Conference and Exhibition.Yokohama, Japan: Japan Automobile Research Institute, 2006.
[20] US Department of Energy. USABC Electric Vehicle Battery Test Procedure Manual[M].Washington: US Department of Energy, 1996. 33-135.
[21] GREWAL S, GRANT D A. A novel technique for modeling the state of charge of lithium ion batteries using artificial neural networks[J]. IEE Intelec Conference, 2000, 484: 174-179.
[22]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.
[23] Zhu C B, Co leman M, Hurley W G. State o f charge determination in a lead-acid battery: Combined EMF estimation and ah balance approach[C]. IEEE Conf on Power Electronics Specialists. Aachen, 2004: 1908-1914.
[24] Caumont O, Le Moigne P, Rombaut C, et al. Energy gauge for lead-acid batteries in electric vehicles[J]. IEEE Trans on Energy Conversion, 2000, 15(3): 354-360.
[25]李哲,仝猛,卢兰光,等.动力型铅酸电池及LiFePO4锂离子电池的容量特性[J].电池,2009,39(1):30-32.
[26]田硕,李哲,卢兰光,等. H EV用动力蓄电池的最大充放电性能[J].电池,2008,38(1):27-30.
[27] L ee S J, Kim J H , Lee J M , et al. The state and parameter estimation of a Li-ion battery using a new OCV-SOC concept[C], Proceedings of 2007 IEEE Power Electronics Specialists Conference. Orlando, USA, 2007.
[28]胡明辉,秦大同.混合动力汽车镍氢电池组的充放电效率分析[J].重庆大学学报,2009,32( 3):279-282.
[29]张金灵.电动汽车智能电池系统的研究[D].北京:北京交通大学, 2010.
[30] POP V. Accuracy analysis Of the state-of charge and remaining run time determination for lithium-ion batterie [J]. Measurement, 2008, 10: 1016-1022.
[31]赵克刚,罗玉涛,裴锋.基于神经网络的电池荷电状态估计方法[J].中南大学学报:自然科学版,2007,38(增刊1): 931-936.
[32]胡建明,陈渊睿.应用灰色理论预测混合动力汽车蓄电池的剩余容量[J].通信电源技术,2009,26(5): 54-58.
[33]黄怡然.基于监控系统的蓄电池容量检测的研究[J].通信电源技术,2007,24(4): 21-23.
[34] WANG TW, YANG M J, SHYU K K, et al. Design fuzzy SOC estimation for sealed lead-acid batteries of electric vehicles in reflexTM [C], 2007 IEEE International Symposium on Industrial Electronics Proceedings, Piscataway, NJ. 2007: 95-99.
[35] BHANGU B, BENTLEY P, STONE D, et al. Nonlinear observers for predicting state of charge and state of health of lead acid batteries for hybrid electric vehicles [ J ] . IEEE Transactions on Vehicular Technology, 2005,54(3): 783-795.
[36] HAN J,KIM D,SUNWOO M. State of charge estimation of lead-acid batteries using an adaptive extended Kalman filter[J]. Journal of Power Source, 2009, 188(2): 606-612.
[37]胡建明,陈渊睿.应用灰色理论预测混合动力汽车蓄电池的剩余容量[J].通信电源技术,2009,26(5): 54-58.
[38]付进军,齐铂金等.动力电池组管理系统单总线测量技术研究[J],中国测试技术, 2004, 30(6): 10-12.
[39] Shi P, Bu C G, Zhao Y W. The ANN models for SOC/BRC estimation of Li-ion battery[C]. IEEE 1 nt Conf Acquisition. Hong Kong, 2005: 560-564.
[40] Li I H, Wang W Y, Su S F, et al. A merged fuzzy neural net work and its applications in battery state of charge estimation[J]. IEEE Trans on Energy Conversion, 2007, 22(3): 697-708.
[41] Sarkka S. On unscented Kalman filtering for state estimation of continuous time nonlinear systems [J]. IEEE Transactions on Automatic Control, 2007, 52( 9): 1631-1641.
[42] Zhang F, Liu G J, Fang L J. Battery state estimation using unscented Kalm an filter [A]. IEEE International Conference on Robotics and Automation[C]. 2009: 1863- 1868.
[43]史宗科.最优估计的计算方法[M].北京:科学出版社, 2001.
[44] Padhi A K, Najundaswamy K S, Goodenough J B[J]. J ElectrochemSoc, 1997 (144): 1188-1194.
[45] Chung S Y, Bloking J T, Chiang Y M. [J]. Nat Mater, 2002, 1: 123-128.
[46] Delacourt C, Laffont L, Bouchet R, et al. [J]. J Electro-chem Soc, 2005, 152: A913-A921.
[47]吴宇平,戴晓兵.锂离子电池应用与实践[M].北京:化学工业出版社,2004.
[48] Partnership for a New Generation of Vehicles . PNGV Battery Test Manual [EB/OL], February, 2001.
[49] Xie H M, Wang R S, Ying J R, et al.[J]. Adv Mater, 2006, 18: 2609-2613.
[50] Zou Z G, Li X M, Wu Y, et al.Effect of Heating on the Structural and Electrochemical Properties of LiFePO4 by Molten Salt Method [C]. Changsha: 5th China Interna-tional Conference on High-Performance Ceramics (CICC-5), 2007.
[51]冯国彪,邓宏.锂离子电池正极材料磷酸铁锂研究进展[J].无机盐工业2011,43(3): 14-17.
[52] Huang Y H, Goodenough J B. [J].Chem Mater, 2008, 20: 7237-7241.
[53]张文保,伲生麟.化学电源导论[M].上海交通大学出版社,1992.
[54] H.L Chan, D.Sutanto. A New Battery Model for use with Battery Energy Storage Systems and Electric Vehicles Power Systems [J]. IEEE, 2000, 470-475.
[55]陈清泉,孙逢春.混合电动车辆基础.北京:北京理工大学出版社, 2001.
[56] Johnson V H. Battery performance models in ADVISOR[J]. Journal of Power Sources, 2002(110):321.
[57]陈全世,林成涛.电动汽车用电池性能模型研究综述[J].汽车技术,2005,(3):1-5.
[58] Salameh Z M, CasaeeaM A, LynehW A. A mathematie model for lead-acidbatteries[J]. IEEE Translations on Ener Conversions, 1992, 7(l):93-97.
[59] United States Idaho National Engineering&Environmental Laboratory.PNGV Battery Test Manual [M/OL], Revision 3, 2001. http: //avt inJ gov/energy_storage_lib shtml.
[60]林成涛,仇斌,陈全世.电流输入电动汽车电池等效电路模型的比较[J].机械工程学报. 2005,41(12):76-81.
[61]魏学哲,邹广楠,孙泽昌.燃料电池汽车中电池建模及其参数估计[J].电源技术. 2004(10): 605-608.
[62]魏学哲,孙泽昌,田佳卿.锂离子动力电池参数辨识与状态估计[J].同济大学学报(自然科学版). 2008(02): 231-235.
[63] Suleiman Abu-Sharkh,Dennis Doerffel. Rapid test and non-liear model characteristsation of solid-state lithium-ion batteries. Journal of Power source,2004,(130):266-274.
[64]‘863’计划节能与新能源汽车重大项目2007年度HEV用高功率锂离子动力蓄电池性能测试规范[S].Rev .5北京: 2007.
[65]‘863’计划节能与新能源汽车重大项目2007年度HEV用高功率金属氢化物镍动力蓄电池性能测试规范[S].Rev .5北京: 2007.
[66]日本电动车辆协会.混合动力电动汽车用密闭型镍氢电池的输出密度及输入密度试验方法[S]. 2003.
[67]邵海岳,钟志华,何莉萍,等.电动汽车动力电池模型及SOC预测方法[J].电源技术,2004,28(10):637-640.
[68] Gregory L Plett. Extended Kalman filtering for batterymanagement systems of LiPB-based HEV battery packs, Part3: State and parameter estimation[J]. Journal of PowerSources, 2004, 134: 277.
[69]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.
[70]姜久春,文锋,温家鹏, et al.纯电动汽车用锂离子电池的建模和模型参数识别[J].电力科学与技术学报. 2010, 25(01): 67-74.
[71] Jaegul L, Socnjae L, Namgoong E. Dynamic state battery model with Self-adaptive aging factor for EV and HEV applications[A]. Proceedings of the 15 th International Electric Vehicle Symposium[C].1998.
[72] Mofio K, Kazuhiro H, Anil P. Battery SOC and distance to empty of the Honda EV Plus [A]. Proceedings of the 14th International Electric Vehicle Symposium [C]. 1997.
[73]陈鸿雁,李山.王培容二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.
[74]陈清泉,孙逢春,祝嘉光.现代电动汽车技术[M].北京:北京理工大学出版社,2002.
[75]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.
[76] R.E.Kalman.A New Approach to Linear Filtering and Prediction Problems. Journal of basic Engineering[J]. 1960, 82(SeriesD):35-45.
[77]王志贤.最优状态估计与系统辨识[M].西安:西北工业大学出版社,2004,118～194.
[78]裴锋,黄向东,罗玉涛,赵克刚.电动汽车动力电池变流放电特性与荷电状态实时估计[J].中国电机工程学报.2005, 25(9):164-168.
[79] Yonglang Guo, Zheng li Wei. Effects of H2SO4 concentrations on reduction processes of PbO layer[J]. Electrochimica Acta. 1997, 42: 979-984.
[2]蒋志君.锂离子电池正极材料磷酸铁锂:进展与挑战[J].功能材料.2010,41(3):365～368.
[3]阮勇,吴罡,滕达磷.酸铁锂电池及其在通信行业中的应用[J].邮电设计技术,2011,03:73-76.
[4]陈全世,林成涛.电动汽车用电池性能模型研究综述[J].汽车技术,2005,(3):1-5.
[5]邵海岳等.电动汽车动力电池模型及SOC预测方法[J].电源技术,2004,(10): 637-640.
[6] Min Chen, Gabriel A, Rincon-Mora. Accurate Electrical Battery Model Capable of Predicting Run time and I-V Performance[J].IEEE Transations on Energy Conversion. Vol.21, No.2, June 2006: 504-511.
[7] SA Khateeb, MM Farid, JR Selman, et al. Mechanical-electrochemical modeling of Li-ion battery designed for an electric scooter [J]. Journal of Power Sources, 2006, 158 (1): 673-678.
[8] John Newman, Karen E Thomas, Hooman Hafezi, et al. Modeling of Lithium-ion batteries [J]. Journal of Power Sources, 2003, 119 (1): 838-843.
[9] HALLAJ S A, MALEKI H, HONG J S, et al.Thermal modeling and design considerations of lithium-ion batteries[J]. J Power Sources, 1999, 83: 1-8.
[10] SATO N. Thermal behavior analysis of lithium-ion batteries for electric and hybrid vehicles[J]. J Power Sources, 2001, 99: 70-77.
[11] Bergveld H J. Battery Management Systems Design by Modelling [M]. springer, 2001.
[12]李革臣,古艳磊.电化学阻抗谱法预测锂电池荷电状态[J].电源技术.2008,32(9):599-602.
[13] Rodrigues S, Muniehandraiah N, Shukla A K. Review of state-of-charge Indieation of batteries by means of a.c.impedance measurements[J]. 2000, 87(1):12-20.
[14] GREGORY L P. LiPB dynamic cell models for kalman-filter SOC estimation[A]. Proceedings of the 19th International Electric Vehicle Symposium[C], Pusan: EVS, 2002. 1860-1871.
[15] O'GORMAN C C,INGERSOLL D,JUNGST R G, et al. Artificial neural network simulation of battery performance[A]. Proc 31st An-nual Hawaii International Conference on System Sciences[C], Hawaii: ICSS, 1998. 461-472.
[16] Chen Y F, Evans JW. Heat Transfer Phenomena in Lithium/Polymer-Electrolyte Batteries for Electric Vehicle Application [J].Electrochem.Soc. 1993.
[17] HALLAJ S A,SELMAN J R. Thermal modeling of secondary lithium batteries for electric vehicle/hybrid electric vehicle applications[J]. J Power Sources, 2002, 110: 341-348.
[18] CHEN S C,WANG Y Y,WANG C C. Thermal analysis of spirally wound lithium batteries[J]. J Electrochemical Society, 2006, 153(4): 637-648.
[19] KIM G H,PESARAN. A.Battery thermal management system design modeling[C]//The 22nd International Battery,Hybrid and Fuel Cell Electric Vehicle Conference and Exhibition.Yokohama, Japan: Japan Automobile Research Institute, 2006.
[20] US Department of Energy. USABC Electric Vehicle Battery Test Procedure Manual[M].Washington: US Department of Energy, 1996. 33-135.
[21] GREWAL S, GRANT D A. A novel technique for modeling the state of charge of lithium ion batteries using artificial neural networks[J]. IEE Intelec Conference, 2000, 484: 174-179.
[22]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.
[23] Zhu C B, Co leman M, Hurley W G. State o f charge determination in a lead-acid battery: Combined EMF estimation and ah balance approach[C]. IEEE Conf on Power Electronics Specialists. Aachen, 2004: 1908-1914.
[24] Caumont O, Le Moigne P, Rombaut C, et al. Energy gauge for lead-acid batteries in electric vehicles[J]. IEEE Trans on Energy Conversion, 2000, 15(3): 354-360.
[25]李哲,仝猛,卢兰光,等.动力型铅酸电池及LiFePO4锂离子电池的容量特性[J].电池,2009,39(1):30-32.
[26]田硕,李哲,卢兰光,等. H EV用动力蓄电池的最大充放电性能[J].电池,2008,38(1):27-30.
[27] L ee S J, Kim J H , Lee J M , et al. The state and parameter estimation of a Li-ion battery using a new OCV-SOC concept[C], Proceedings of 2007 IEEE Power Electronics Specialists Conference. Orlando, USA, 2007.
[28]胡明辉,秦大同.混合动力汽车镍氢电池组的充放电效率分析[J].重庆大学学报,2009,32( 3):279-282.
[29]张金灵.电动汽车智能电池系统的研究[D].北京:北京交通大学, 2010.
[30] POP V. Accuracy analysis Of the state-of charge and remaining run time determination for lithium-ion batterie [J]. Measurement, 2008, 10: 1016-1022.
[31]赵克刚,罗玉涛,裴锋.基于神经网络的电池荷电状态估计方法[J].中南大学学报:自然科学版,2007,38(增刊1): 931-936.
[32]胡建明,陈渊睿.应用灰色理论预测混合动力汽车蓄电池的剩余容量[J].通信电源技术,2009,26(5): 54-58.
[33]黄怡然.基于监控系统的蓄电池容量检测的研究[J].通信电源技术,2007,24(4): 21-23.
[34] WANG TW, YANG M J, SHYU K K, et al. Design fuzzy SOC estimation for sealed lead-acid batteries of electric vehicles in reflexTM [C], 2007 IEEE International Symposium on Industrial Electronics Proceedings, Piscataway, NJ. 2007: 95-99.
[35] BHANGU B, BENTLEY P, STONE D, et al. Nonlinear observers for predicting state of charge and state of health of lead acid batteries for hybrid electric vehicles [ J ] . IEEE Transactions on Vehicular Technology, 2005,54(3): 783-795.
[36] HAN J,KIM D,SUNWOO M. State of charge estimation of lead-acid batteries using an adaptive extended Kalman filter[J]. Journal of Power Source, 2009, 188(2): 606-612.
[37]胡建明,陈渊睿.应用灰色理论预测混合动力汽车蓄电池的剩余容量[J].通信电源技术,2009,26(5): 54-58.
[38]付进军,齐铂金等.动力电池组管理系统单总线测量技术研究[J],中国测试技术, 2004, 30(6): 10-12.
[39] Shi P, Bu C G, Zhao Y W. The ANN models for SOC/BRC estimation of Li-ion battery[C]. IEEE 1 nt Conf Acquisition. Hong Kong, 2005: 560-564.
[40] Li I H, Wang W Y, Su S F, et al. A merged fuzzy neural net work and its applications in battery state of charge estimation[J]. IEEE Trans on Energy Conversion, 2007, 22(3): 697-708.
[41] Sarkka S. On unscented Kalman filtering for state estimation of continuous time nonlinear systems [J]. IEEE Transactions on Automatic Control, 2007, 52( 9): 1631-1641.
[42] Zhang F, Liu G J, Fang L J. Battery state estimation using unscented Kalm an filter [A]. IEEE International Conference on Robotics and Automation[C]. 2009: 1863- 1868.
[43]史宗科.最优估计的计算方法[M].北京:科学出版社, 2001.
[44] Padhi A K, Najundaswamy K S, Goodenough J B[J]. J ElectrochemSoc, 1997 (144): 1188-1194.
[45] Chung S Y, Bloking J T, Chiang Y M. [J]. Nat Mater, 2002, 1: 123-128.
[46] Delacourt C, Laffont L, Bouchet R, et al. [J]. J Electro-chem Soc, 2005, 152: A913-A921.
[47]吴宇平,戴晓兵.锂离子电池应用与实践[M].北京:化学工业出版社,2004.
[48] Partnership for a New Generation of Vehicles . PNGV Battery Test Manual [EB/OL], February, 2001.
[49] Xie H M, Wang R S, Ying J R, et al.[J]. Adv Mater, 2006, 18: 2609-2613.
[50] Zou Z G, Li X M, Wu Y, et al.Effect of Heating on the Structural and Electrochemical Properties of LiFePO4 by Molten Salt Method [C]. Changsha: 5th China Interna-tional Conference on High-Performance Ceramics (CICC-5), 2007.
[51]冯国彪,邓宏.锂离子电池正极材料磷酸铁锂研究进展[J].无机盐工业2011,43(3): 14-17.
[52] Huang Y H, Goodenough J B. [J].Chem Mater, 2008, 20: 7237-7241.
[53]张文保,伲生麟.化学电源导论[M].上海交通大学出版社,1992.
[54] H.L Chan, D.Sutanto. A New Battery Model for use with Battery Energy Storage Systems and Electric Vehicles Power Systems [J]. IEEE, 2000, 470-475.
[55]陈清泉,孙逢春.混合电动车辆基础.北京:北京理工大学出版社, 2001.
[56] Johnson V H. Battery performance models in ADVISOR[J]. Journal of Power Sources, 2002(110):321.
[57]陈全世,林成涛.电动汽车用电池性能模型研究综述[J].汽车技术,2005,(3):1-5.
[58] Salameh Z M, CasaeeaM A, LynehW A. A mathematie model for lead-acidbatteries[J]. IEEE Translations on Ener Conversions, 1992, 7(l):93-97.
[59] United States Idaho National Engineering&Environmental Laboratory.PNGV Battery Test Manual [M/OL], Revision 3, 2001. http: //avt inJ gov/energy_storage_lib shtml.
[60]林成涛,仇斌,陈全世.电流输入电动汽车电池等效电路模型的比较[J].机械工程学报. 2005,41(12):76-81.
[61]魏学哲,邹广楠,孙泽昌.燃料电池汽车中电池建模及其参数估计[J].电源技术. 2004(10): 605-608.
[62]魏学哲,孙泽昌,田佳卿.锂离子动力电池参数辨识与状态估计[J].同济大学学报(自然科学版). 2008(02): 231-235.
[63] Suleiman Abu-Sharkh,Dennis Doerffel. Rapid test and non-liear model characteristsation of solid-state lithium-ion batteries. Journal of Power source,2004,(130):266-274.
[64]‘863’计划节能与新能源汽车重大项目2007年度HEV用高功率锂离子动力蓄电池性能测试规范[S].Rev .5北京: 2007.
[65]‘863’计划节能与新能源汽车重大项目2007年度HEV用高功率金属氢化物镍动力蓄电池性能测试规范[S].Rev .5北京: 2007.
[66]日本电动车辆协会.混合动力电动汽车用密闭型镍氢电池的输出密度及输入密度试验方法[S]. 2003.
[67]邵海岳,钟志华,何莉萍,等.电动汽车动力电池模型及SOC预测方法[J].电源技术,2004,28(10):637-640.
[68] Gregory L Plett. Extended Kalman filtering for batterymanagement systems of LiPB-based HEV battery packs, Part3: State and parameter estimation[J]. Journal of PowerSources, 2004, 134: 277.
[69]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.
[70]姜久春,文锋,温家鹏, et al.纯电动汽车用锂离子电池的建模和模型参数识别[J].电力科学与技术学报. 2010, 25(01): 67-74.
[71] Jaegul L, Socnjae L, Namgoong E. Dynamic state battery model with Self-adaptive aging factor for EV and HEV applications[A]. Proceedings of the 15 th International Electric Vehicle Symposium[C].1998.
[72] Mofio K, Kazuhiro H, Anil P. Battery SOC and distance to empty of the Honda EV Plus [A]. Proceedings of the 14th International Electric Vehicle Symposium [C]. 1997.
[73]陈鸿雁,李山.王培容二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.
[74]陈清泉,孙逢春,祝嘉光.现代电动汽车技术[M].北京:北京理工大学出版社,2002.
[75]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.
[76] R.E.Kalman.A New Approach to Linear Filtering and Prediction Problems. Journal of basic Engineering[J]. 1960, 82(SeriesD):35-45.
[77]王志贤.最优状态估计与系统辨识[M].西安:西北工业大学出版社,2004,118～194.
[78]裴锋,黄向东,罗玉涛,赵克刚.电动汽车动力电池变流放电特性与荷电状态实时估计[J].中国电机工程学报.2005, 25(9):164-168.
[79] Yonglang Guo, Zheng li Wei. Effects of H2SO4 concentrations on reduction processes of PbO layer[J]. Electrochimica Acta. 1997, 42: 979-984.