基于信息反馈粒子群的高精度锂离子电池模型参数辨识
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摘要
锂离子电池模型参数精度是影响模型仿真电池静态和动态特性的一个重要因素。近年来,粒子群优化(PSO)算法常被应用于模型参数辨识中。然而PSO算法及其改进算法在迭代过程中存在此问题,即粒子位置的更新并未引起其局部最优位置以及种群全局最优位置的更新,从而导致优化算法无法获得更优结果。针对此问题,提出一种基于信息反馈的粒子群(FPSO)算法,其能够根据粒子位置更新的反馈信息重新调整粒子位置,旨在促进粒子局部最优位置和全局最优位置持续更新以提高寻优精度。在利用常用基准函数对本文FPSO算法进行性能验证后,将其应用于锂离子电池模型参数辨识,实验结果表明,相比基于线性PSO、自适应权重PSO以及最小二乘法的模型参数辨识结果,本文提出的FPSO算法能够提高模型精度。
The parameter precision of lithium-ion battery model is an important factor affecting the model to simulate the static and dynamic characteristics of the battery. In recent years, particle swarm optimization(PSO) is often applied to identify the model parameter. However, PSO and its improved algorithm could encounter such problem that, the position of a particle is updating while the local optimal position of the particle and the global optimal position of all the particles stop updating,resulting in the optimization algorithm can't obtain more precision results. In view of such problem,this paper presents an improved feedback PSO(FPSO), the position of the particle can be adjusted according to the feedback information of the particle to continue to update the local position of the particle to improve the optimization precision. Typical benchmark functions are used to validate the performance of FPSO. On the other hand, the FPSO of the paper is applied to identify the parameter of the lithium-ion battery model, and the experimental results show that, comparing with the models based on Linear PSO, Adaptive weight PSO, and Least Square(LS) parameter identification, the model using FPSO of the paper can achieve high precision.
The parameter precision of lithium-ion battery model is an important factor affecting the model to simulate the static and dynamic characteristics of the battery. In recent years, particle swarm optimization(PSO) is often applied to identify the model parameter. However, PSO and its improved algorithm could encounter such problem that, the position of a particle is updating while the local optimal position of the particle and the global optimal position of all the particles stop updating,resulting in the optimization algorithm can't obtain more precision results. In view of such problem,this paper presents an improved feedback PSO(FPSO), the position of the particle can be adjusted according to the feedback information of the particle to continue to update the local position of the particle to improve the optimization precision. Typical benchmark functions are used to validate the performance of FPSO. On the other hand, the FPSO of the paper is applied to identify the parameter of the lithium-ion battery model, and the experimental results show that, comparing with the models based on Linear PSO, Adaptive weight PSO, and Least Square(LS) parameter identification, the model using FPSO of the paper can achieve high precision.
引文
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[10]刘伟龙,王丽芳,廖承林,等.充电模态下电动汽车动力电池模型辨识[J].电工技术学报,2017,32(11):198-207.Liu Weilong,Wang Lifang,Liao Chenglin,et al.Parameters identification method of battery model for electric vehicles under the charging mode[J].Transactions of China Electrotechnical Society,2017,32(11):198-207.
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[14]吕志锋,张金生,王仕成,等.基于自适应权重PSO算法的磁屏蔽装置优化设计[J].中国惯性技术学报,2017,25(6):799-803.LüZhifeng,Zhang Jinsheng,Wang Shicheng,et al.Optimal design of magnetic shielding device based on adaptive weight PSO algorithm[J].Journal of Chinese Inertial Technology,2017,25(6):799-803.
[15]Tsai Hsing-chih,Tyan Yaw-yauan,Wu Yun-wu.Gravitational particle swarm[J].Applied Mathematics and Computation,2013,219(17):9106-9117.
[16]王力,赵洁,刘涤尘,等.基于遗传粒子群优化算法的调速器执行机构分段线性模型及参数辨识[J].电工技术学报,2016,31(12):204-210.Wang Li,Zhao Jie,Liu Dichen,et al.Governor actuator piecewise linear model and parameter identification based on genetic algorithm-particle swarm optimization[J].Transactions of China Electrotechnical Society,2016,31(12):204-210.
[17]韩璞,袁世通.基于大数据和双量子粒子群算法的多变量系统辨识[J].中国电机工程学报,2014,34(32):5779-5787.Han Pu,Yuan Shitong.Multivariable system identification based on double quantum particle swarm optimization and big data[J].Proceedings of the CSEE,2014,34(32):5779-5787.
[18]项宇,马晓军,刘春光,等.基于改进的粒子群优化扩展卡尔曼滤波算法的锂电池模型参数辨识与荷电状态估计[J].兵工学报,2014,35(10):1659-1666.Xiang Yu,Ma Xiaojun,Liu Chunguang,et al.Estimation of model parameters and SOC of lithium batteries based on IPSO-EKF[J].Acta Armamentarii,2014,35(10):1659-1666.
[19]王振树,卞绍润,刘晓宇,等.基于混沌与量子粒子群算法相结合的负荷模型参数辨识研究[J].电工技术学报,2014,29(12):211-217.Wang Zhenshu,Bian Shaorun,Liu Xiaoyu,et al.Research on load model parameter identification based on the CQDPSO algorithm[J].Transactions of China Electrotechnical Society,2014,29(12):211-217.
[20]程泽,董梦男,杨添剀,等.基于自适应混沌粒子群算法的光伏电池模型参数辨识[J].电工技术学报,2014,29(9):245-252.Cheng Ze,Dong Mengnan,Yang Tiankai,et al.Extraction of solar cell model parameters based on self-adaptive chaos particle swarm optimization algorithm[J].Transactions of China Electrotechnical Society,2014,29(9):245-252.
[21]Gregory L Plett.Extended Kalman filtering for battery management systems of LiPB-based HEVbattery packs Part 2:modeling and identification[J].Journal of Power Sources,2004,134(2):262-276.
[22]Dubarry M,Liaw B Y.Development of a universal modeling tool for rechargeable lithium batteries[J].Journal of Power Sources,2007,174(2):856-860.
[23]Li Jianwei.Performance-driven behavioral battery modeling for large format batteries[D].Mississippi:Mississippi State University,2012.
[2]何志超,杨耕,卢兰光,等.一种动力电池动态特性建模[J].电工技术学报,2016,31(11):194-203.He Zhichao,Yang Geng,Lu Languang,et al.Amodeling method for power battery dynamics[J].Transactions of China Electrotechnical Society,2016,31(11):194-203.
[3]Xiong Rui,He Hongwen,Zhu Fushun.Identification of dynamic model parameters for ultra-capacitor used in electric vehicles[J].Journal of Beijing Institute of Technology(English Edition),2011,20(2):204-210.
[4]Seaman A,Dao T S,Mcphee J.A survey of mathematics-based equivalent circuit and electrochemical battery models for hybrid and electric vehicle simulation[J].Journal of Power Sources2014,256:410-423.
[5]陈希坤,孙东,陈小虎.锂离子电池建模及其荷电状态鲁棒估计[J].电工技术学报,2015,30(15):141-147.Chen Xikun,Sun Dong,Chen Xiaohu.Modeling and state of charge robust estimation for lithium-ion batteries[J].Transactions of China Electrotechnical Society,2015,30(15):141-147.
[6]Hu Yiran,Yurkovich S.Linear parameter varying battery model identification using subspace methods[J]Journal of Power Sources,2011,196(5):2913-2923.
[7]朱浩,刘云峰,赵策.锂离子电池参数辨识与SOC估算研究[J].湖南大学学报(自然科学版),2014,41(3):37-42.Zhu Hao,Liu Yunfeng,Zhao Ce.Parameter identification and SOC estimation of lithium ion battery[J].Journal of Hunan University(Natural Sciences),2014,41(3):37-42.
[8]商云龙,张奇,崔纳新,等.基于AIC准则的锂离子电池变阶RC等效电路模型研究[J].电工技术学报,2015,30(17):55-62.Shang Yunlong,Zhang Qi,Cui Naxin,et al.Research on variable-order RC equivalent circuit model for lithium-ion battery based on the AIC criterion[J].Transactions of China Electrotechnical Society,2015,30(17):55-62.
[9]刘树林,崔纳新,李岩,等.基于分数阶理论的车用锂离子电池建模及荷电状态估计[J].电工技术学报,2017,32(4):189-195.Liu Shulin,Cui Naxin,Li Yan,et al.Modeling and state of charge estimation of lithium-ion battery based on theory of fractional order for electric vehicle[J].Transactions of China Electrotechnical Society,2017,32(4):189-195.
[10]刘伟龙,王丽芳,廖承林,等.充电模态下电动汽车动力电池模型辨识[J].电工技术学报,2017,32(11):198-207.Liu Weilong,Wang Lifang,Liao Chenglin,et al.Parameters identification method of battery model for electric vehicles under the charging mode[J].Transactions of China Electrotechnical Society,2017,32(11):198-207.
[11]Moldovan N,Picos R,Garcia-Moreno E.Parameter extraction of a solar cell compact model using genetic algorithms[C]//IEEE Spanish Conference on Electron Devices,Santiago de Compostela,Spain,2009:379-382.
[12]Kennedy J,Eberhart R.Particle swarm optimization[C]//Proceedings of 1995 IEEE International Conference on Neural Networks,Perth,Australia,2011:1942-1948.
[13]Shi Yuhui,Eberhart R C.A modified particle swarm optimizer[C]//IEEE World Congress on Computational Intelligence,Anchorage,AK,USA,1998:69-73.
[14]吕志锋,张金生,王仕成,等.基于自适应权重PSO算法的磁屏蔽装置优化设计[J].中国惯性技术学报,2017,25(6):799-803.LüZhifeng,Zhang Jinsheng,Wang Shicheng,et al.Optimal design of magnetic shielding device based on adaptive weight PSO algorithm[J].Journal of Chinese Inertial Technology,2017,25(6):799-803.
[15]Tsai Hsing-chih,Tyan Yaw-yauan,Wu Yun-wu.Gravitational particle swarm[J].Applied Mathematics and Computation,2013,219(17):9106-9117.
[16]王力,赵洁,刘涤尘,等.基于遗传粒子群优化算法的调速器执行机构分段线性模型及参数辨识[J].电工技术学报,2016,31(12):204-210.Wang Li,Zhao Jie,Liu Dichen,et al.Governor actuator piecewise linear model and parameter identification based on genetic algorithm-particle swarm optimization[J].Transactions of China Electrotechnical Society,2016,31(12):204-210.
[17]韩璞,袁世通.基于大数据和双量子粒子群算法的多变量系统辨识[J].中国电机工程学报,2014,34(32):5779-5787.Han Pu,Yuan Shitong.Multivariable system identification based on double quantum particle swarm optimization and big data[J].Proceedings of the CSEE,2014,34(32):5779-5787.
[18]项宇,马晓军,刘春光,等.基于改进的粒子群优化扩展卡尔曼滤波算法的锂电池模型参数辨识与荷电状态估计[J].兵工学报,2014,35(10):1659-1666.Xiang Yu,Ma Xiaojun,Liu Chunguang,et al.Estimation of model parameters and SOC of lithium batteries based on IPSO-EKF[J].Acta Armamentarii,2014,35(10):1659-1666.
[19]王振树,卞绍润,刘晓宇,等.基于混沌与量子粒子群算法相结合的负荷模型参数辨识研究[J].电工技术学报,2014,29(12):211-217.Wang Zhenshu,Bian Shaorun,Liu Xiaoyu,et al.Research on load model parameter identification based on the CQDPSO algorithm[J].Transactions of China Electrotechnical Society,2014,29(12):211-217.
[20]程泽,董梦男,杨添剀,等.基于自适应混沌粒子群算法的光伏电池模型参数辨识[J].电工技术学报,2014,29(9):245-252.Cheng Ze,Dong Mengnan,Yang Tiankai,et al.Extraction of solar cell model parameters based on self-adaptive chaos particle swarm optimization algorithm[J].Transactions of China Electrotechnical Society,2014,29(9):245-252.
[21]Gregory L Plett.Extended Kalman filtering for battery management systems of LiPB-based HEVbattery packs Part 2:modeling and identification[J].Journal of Power Sources,2004,134(2):262-276.
[22]Dubarry M,Liaw B Y.Development of a universal modeling tool for rechargeable lithium batteries[J].Journal of Power Sources,2007,174(2):856-860.
[23]Li Jianwei.Performance-driven behavioral battery modeling for large format batteries[D].Mississippi:Mississippi State University,2012.
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