用基于遗传优化的扩展卡尔曼滤波算法辨识电池模型参数

摘要/Abstract

摘要： 提出了一种基于遗传优化算法(Genetic algorithm, GA)和扩展卡尔曼滤波(Extended Kalman filter, EKF)算法的电池模型参数辨识方法。建立了锂离子动力电池等效电路模型,模型中两个RC网络分别描述电池的电化学极化特性和浓差极化特性,迟滞电压描述电池充放电过程的平衡电势的差异。对于具有耦合关系的模型参数,采用具有最小均方误差估计效果的EKF辨识算法,针对EKF算法通过试验调节难以取得最佳滤波效果的问题,提出基于遗传算法优化EKF噪声矩阵的方法。试验和仿真结果表明:基于遗传优化的EKF算法(GA-EKF)辨识的电池模型满足电动车辆仿真精度要求。

关键词: 电气工程, 锂离子电池, 动态电池模型, 参数辨识, 基于遗传优化的扩展卡尔曼滤波算法

Abstract: A parameter identification method was proposed for the dynamic battery model based on the genetic algorithm(GA) and the extended Kalman filter(EKF) algorithm. An electrical equivalent circuit model was built for the lithium-ion power battery. In the model 2 RC networks describe the electrochemical activation process and the concentration difference polarization process of the battery respectively, and the hysteresis voltage characterizes the difference of equilibrium voltages between the charging and discharging processes. The coupled parametes of the model, i.e. the RC network parameters and the hysteresis coefficient were identified by the EKF algorithm with minimal mean square error estimation effect. For the problem of the EKF which is difficnlt to get the optimal filtering effect by test adjustment, the GA was used to optimizing the EKF noise matrix. The results of simulation and experiment showed that the proposed battery model parameter identification method meets the requirement of the electric vehicle on simulation precision.

Key words: electrical engineering, lithium-ion battery, dynamic battery model, parameter identification, GA-EKF algorthm

中图分类号:

TM912

张彩萍, 姜久春. 用基于遗传优化的扩展卡尔曼滤波算法辨识电池模型参数 [J]. , 2012, (03): 732-737.

ZHANG Cai-ping, JIANG Jiu-chun. Extended Kalman filter algorithm for parameters identification of dynamic battery model based on genetic algorithm optimization[J]. , 2012, (03): 732-737.

参考文献

[1] Chen M, Rinc'on-Mora G A. Accurate electrical battery model capable of predicting runtime and I-V performance［J］. IEEE Trans on Energy Conversion,2006,21(2):504-511.
[2] Kroeze R C, Krein P T. Electrical battery model for use in dynamic electric vehicle simulations［C］//Proceedings of IEEE Power Electronics Specialists Conference,Rhodes,2008:1336-1342.
[3] Dhaouadi R, Mohan N, Norum L. Design and implementation of an extended Kalman filter for the state estimation of a permanent magnet synchronous motor［J］. IEEE Trans Power Electrons,1991,6(3):491-497.
[4] Simon D. Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches［M］. Hoboken, New Jersey: John Wiley &Sons, 2006.
[5] Vasebi A, Bathaee S M T, Partovibakhsh M. Predicting state of charge of lead-acid batteries for hybrid electric vehicles by extended kalman filter［J］. Energy Conversion and Management,2008,49(1):75-82.
[6] 张彩萍. 电传动车辆锂离子电池荷电状态估计与峰值功率预测研究. 北京:北京理工大学机械与车辆学院,2010. Zhang Cai-ping. State of charge estimation and peak power capability predict of lithium-ion batteries for electric transmission vehicles. Beijing: School of Mechatronic Engineering, Beijing Institute of Technology,2010.
[7] Zhang Cai-ping, Zhang Cheng-ning, Liu Jia-zhong, et al. Identification of dynamic model for lithium-ion batteries used in hybrid electric vehicles［J］. High Technology Letters,2010,16(1): 6-12.
[8] 赵万忠,施国标,林逸. 基于遗传算法的EPS系统参数优化［J］. 吉林大学学报:工学版,2009,39(2):286-290. Zhao Wan-zhong,Shi Guo-biao,Lin Yi. Parameter optimization of EPS system based on genetic algorithm［J］. Journal of Jilin University(Engineering and Technology Edition), 2009,39(2):286-290.
[9] Holland J H. Adaptation in natural and artificial systems. Ann Arbor: University of Michigan, 1975.
[10] 王小明, 曹立明. 遗传算法:理论、应用与软件实现［M］. 西安:西安交通大学出版社,2002.
[11] 林琳,王树勋,魏小丽. 基于遗传模糊高斯混合模型的训练方法［J］. 吉林大学学报:工学版,2006,36(6):967-972. Lin Lin,Wang Shu-xun,Wei Xiao-li. Gaussian mixture model training method based on genetic algorithms and fuzzy approach［J］. Journal of Jilin University(Engineering and Technology Edition), 2006,36(6):967-972.
[12] 雷英杰,张善文,李续武,等. Matlab遗传算法工具箱及应用［M］. 西安: 西安电子科技大学出版社,2005.
[13] 张勇军,王京,李华德. 基于遗传算法优化的定子磁链扩展卡尔曼估计法［J］. 电工技术学报,2009,24(9): 64-70. Zhang Yong-jun, Wang Jing, Li Hua-de. A method of the stator flux EKF estimation for induction motors based on genetic algorithm optimizing［J］. Transactions of China Electrotechnical Society,2009,24(9):64-70.

相关文章 15

[1]	晋超琼, 张葆, 李贤涛, 申帅, 朱枫. 基于扰动观测器的光电稳定平台摩擦补偿策略[J]. 吉林大学学报(工学版), 2017, 47(6): 1876-1885.
[2]	张家旭, 李静. 基于混合优化方法的UniTire轮胎模型参数辨识[J]. 吉林大学学报(工学版), 2017, 47(1): 15-20.
[3]	孙伟, 李健, 贾师. 硬涂层复合结构非线性刚度及阻尼辨识[J]. 吉林大学学报(工学版), 2016, 46(4): 1156-1162.
[4]	秦余钢, 马勇, 张亮, 李腾飞. 基于改进最小二乘算法的船舶操纵性参数辨识[J]. 吉林大学学报(工学版), 2016, 46(3): 897-903.
[5]	孙淑琴, 刘骏妍, 蒋川东, 孟庆云, 林君, 彭良玉. 最小二乘权值平滑滤波技术在核磁共振信号处理中的应用[J]. 吉林大学学报(工学版), 2016, 46(3): 985-995.
[6]	包广清, 毛振鹏. 基于超导储能系统的不对称电压跌落补偿方法[J]. 吉林大学学报(工学版), 2015, 45(4): 1234-1241.
[7]	周逢道，董威，包志强，薛开昶，胡越，孙彩堂. 基于开关电源的三相电磁干扰滤波器[J]. 吉林大学学报(工学版), 2015, 45(1): 216-221.
[8]	陈燕虹, 吴伟静, 刘宏伟, 沈帅, 李策园, 耿焕亮. 纯电动汽车电池箱的热特性[J]. 吉林大学学报(工学版), 2014, 44(4): 925-932.
[9]	龚敏明, 时玮, 张言茹, 姜君, 张维戈, 姜久春. 纯电动公交车锂离子动力电池的使用条件控制[J]. 吉林大学学报(工学版), 2014, 44(4): 1081-1087.
[10]	史文库,毛阳,姜雪,陈志勇,马利红,潘斌. 半主动液压悬置参数识别与动态特性[J]. 吉林大学学报(工学版), 2014, 44(3): 605-611.
[11]	王帅夫,刘景林. 基于大脑情感学习模型的步进电机控制系统[J]. 吉林大学学报(工学版), 2014, 44(3): 765-770.
[12]	刘敬猛, 孟艳艳, 陈伟海, 吴星明. 三自由度永磁球形电机的建模和电压控制[J]. 吉林大学学报(工学版), 2014, 44(2): 560-566.
[13]	王德成, 林辉. 电机四象限运行的直接转矩控制动态加载[J]. 吉林大学学报(工学版), 2014, 44(01): 276-280.
[14]	薛开昶, 董威, 林君, 周逢道. 逆m序列随机相位PWM逆变器的EMI峰值抑制方法[J]. 吉林大学学报(工学版), 2013, 43(06): 1589-1595.
[15]	赵飞翔, 张建伟, 郭孔辉, 张立浩, 郑重. 基于重构电压的感应电机等效参数离线辨识[J]. 吉林大学学报(工学版), 2013, 43(06): 1596-1600.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed