基于IALO算法的蓄电池参数辨识

doi:10.3969/j.issn.1000-1158.2021.09.14

摘要
图/表
参考文献(27)
相关文章 (15)

全文: PDF (1305 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要合理的等效电路模型及准确的模型参数对蓄电池荷电状态(SOC)的准确估计具有重要影响。针对蓄电池三阶Thevenin等效电路模型,基于改进蚁狮优化算法,提出了一种模型参数辨识方法。引入混沌Logistic映射初始化,使初始化群体遍及解空间,有利于寻找全局最优解;引入自适应惯性权重加随机柯西变异策略,有效提高了算法收敛速度;引入精英反向学习策略,有效提高了群体的多样性,避免算法陷入局部最优解。5个测试函数的测试结果表明:相比于蚁狮优化算法、粒子群算法与樽海鞘优化算法,改进蚁狮优化算法收敛速度更快,精度更高。对蓄电池三阶Thevenin等效电路模型进行参数辨识,结果表明:改进蚁狮优化算法相比蚁狮优化算法具有更高的辨识精度。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	吴忠强
	王国勇
	谢宗奎
	卢雪琴
	何怡林

关键词 ：计量学, 蓄电池, 参数辨识, 改进蚁狮优化算法, 自适应权重, 随机柯西变异, 精英反向学习

Abstract：A reasonable equivalent circuit model and accurate model parameters have an important impact on the accurate estimation of the battery SOC. Aiming at the third-order Thevenin equivalent circuit model of battery, a parameter identification method based on ant lion optimization algorithm was proposed. The introduction of chaotic logistic map initialization could make the initialization population spread over the solution space, which was beneficial to find the global optimal solution. The introduction of adaptive inertia weight and random Cauchy mutation strategy could effectively improve the convergence speed of the algorithm. Elite reverse learning strategy was introduced to effectively improve the diversity of population and avoid the algorithm trapping into local optimal solution. The test results of five test functions showed that compared with ant lion optimization algorithm, particle swarm optimization algorithm and salp optimization algorithm, the improved ant lion optimization algorithm had the faster convergence speed and higher accuracy. The parameter identification of third-order Thevenin equivalent circuit model of battery showed that the improved ant lion optimization algorithm had the higher identification accuracy than ant lion optimization algorithm.

Key words： metrology battery parameter identification IALO adaptive weight random Cauchy variation elite reverse learning

收稿日期: 2019-08-27 发布日期: 2021-09-24

PACS:

TB971

基金资助:河北省自然科学基金(F2020203014)

通讯作者: 王国勇(1995-),男,山东潍坊人,硕士研究生,主要研究方向为动力电池的SOC估计。Email: 3026179994@qq.com E-mail: mewzq@163.com

作者简介: 吴忠强(1966-),男,上海市人,燕山大学教授,从事动力电池SOC估计的研究工作。Email: mewzq@163.com

引用本文:

吴忠强,王国勇,谢宗奎,卢雪琴,何怡林. 基于IALO算法的蓄电池参数辨识[J]. 计量学报, 2021, 42(9): 1206-1213.
WU Zhong-qiang,WANG Guo-yong,XIE Zong-kui,LU Xue-qin,HE Yi-lin. Parameter Identification of Battery Based on IALO Algorithm. Acta Metrologica Sinica, 2021, 42(9): 1206-1213.

链接本文:

http://jlxb.china-csm.org:81/Jwk_jlxb/CN/10.3969/j.issn.1000-1158.2021.09.14 或 http://jlxb.china-csm.org:81/Jwk_jlxb/CN/Y2021/V42/I9/1206

［1］吴忠强,尚梦瑶,申丹丹,等. 基于BSA-RELM的纯电动汽车锂离子电池SOC估计［J］. 计量学报, 2019, 40(4): 693-699.
Wu Z Q,Shang M Y,Shen D D, et al. Estimation of SOC of Li-ion Battery in Pure Electric Vehicle by BSA-RELM［J］. Acta Metrologica Sinica, 2019, 40(4): 693-699.
［2］吴忠强,申丹丹,尚梦瑶,等. 基于改进蝗虫优化算法的光伏电池模型参数辨识［J］. 计量学报, 2020, 41(12): 1536-1543.
Wu Z Q, Shen D D, Shang M Y,et al.Parameter Identification of Photovoltaic Cell Model Based on Improved Grasshopper Optimization Algorithm［J］. Acta Metrologica Sinica, 2020, 41(12): 1536-1543.
［3］吴忠强,谢宗奎,刘重阳,等. 基于混沌搜索的改进狮群算法及其在光伏电池参数辨识中的应用［J］. 计量学报, 2021, 42(4): 415-423.
Wu Z Q, Xie Z K,Liu C Y, et al. Lion Swarm Optimization Based on Chaotic Search Strategy and Application in Parameters Identification of Photovoltaic Cell Models［J］. Acta Metrologica Sinica, 2021, 42(4): 415-423.
［4］陈息坤, 孙冬. 锂离子电池建模及其参数辨识方法研究［J］. 中国电机工程学报, 2016, 36(22): 6254-6261.
Chen X K, Sun D. Research on lithiumion battery modeling and model parameter identification methods［J］. Proceedings of the CSEE, 2016, 36(22): 6254-6261.
［5］Rahimi-Eichi H, Ojha U, Baronti F, et al. Battery management system: an overview of its application in thesmart grid and electric vehicles［J］. IEEE Industrial Electronics Magazine, 2013, 7(2): 4-16.
［6］Hu Y, Yurkovich S, Guezennec Y, et al. A technique for dynamic battery model identification in automotive applications using linear parameter varying structures［J］. Control Engineering Practice, 2009, 17(10): 1190-1201.
［7］Chen M, Rincon-Mora G A. Accurate electrical battery model capable of predicting runtime and I-V performance［J］. IEEE Transactionson Energy Conversion, 2006, 21(2): 504-511.
［8］Wu Z, Shang M, Shen D, et al. Prediction of SOC of lead-acid battery in pure electric vehicle based on BSA-RELM［J］. Journal of Renewable and Sustainable Energy, 2018, 10(5):054103.
［9］陈则王, 杨丽文, 赵晓兵, 等. 基于改进无迹卡尔曼滤波的锂电池SOC在线估计［J］. 计量学报, 2019, 40(1): 40-48.
Chen Z W, Yang L W, Zhao X B, et al. Online estimation of lithium battery SOC based on improved traceless kalman filter［J］. Acta Metrologica Sinica, 2019, 40(1): 40-48.
［10］苗壮. 动力锂离子电池动态特性研究及SOC估算［D］. 哈尔滨:哈尔滨工业大学, 2017.
［11］张禹轩. 电动汽车动力电池模型参数在线辨识及SOC估计［D］. 长春:吉林大学, 2014.
［12］陈忠霞. 锂离子电池等效模型参数辨识研究［D］. 青岛:山东科技大学, 2017.
［13］程兴婷. 铅酸电池与锂离子电池的建模与参数辨识方法研究［D］. 长沙:湖南大学, 2015.
［14］Ghorbani N, Kasaeian A, Toopshekan A, et al. Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability［J］. Energy, 2018, 154(1): 581-591.
［15］夏飞, 袁博, 彭道刚, 等. 基于信息量准则的锂离子电池变阶 RC 等效电路模型建模及优化方法［J］. 中国电机工程学报, 2018, 38(21): 6441-6451.
Xia F, Yuan B, Peng D G, et al. Modeling and optimization of variable-order RC equivalent circuit Model for lithium ion batteries based on information criterion［J］. Proceedings of the CSEE, 2018, 38(21): 6441-6451.
［16］Wu Z Q, Yu D Q, Kang X H. Parameter Identification of Photovoltaic Cell Model based on Improved Ant Lion Optimizer［J］. Energy Conversion and Management, 2017, 151: 107-115.
［17］菅志宇, 陈华. 一种基于布谷鸟算法的质子交换膜燃料电池湿度辨识方法［J］. 电子器件, 2018, 41(3): 679-683.
Zhai Z Y, Chen H. A Method for Humidity Identification of Proton Exchange Membrane Fuel Cell Based on Cuckoo Algorithm［J］. Electronic Devices, 2018, 41(3): 679-683.
［18］Zhang Y, Lyden S, Leon de la barra B A, et al. Optimization of Tremblay's battery model parameters for plug-in hybrid electric vehicle applications［C］//2017 Australasian Universities Power Engineering Conference (AUPEC). Melbourne, VIC, Australia, 2017.
［19］Seyedali Mirjalili. The Ant Lion Optimizer［J］. Advances in Engineering Software, 2015, 83: 80-98.
［20］赵辉, 李牧东, 翁兴伟. 具有自适应全局最优引导快速搜索策略的人工蜂群算法［J］. 控制与决策, 2014, 29(11): 2041-2047.
Zhao H, Li M D, Weng X W. Artificial bee colony algorithm with adaptive global optimal guided fast search strategy［J］. Control and Decision, 2014, 29(11): 2041-2047.
［21］马济乔, 李越, 华明宇, 等. 一种自适应混沌蜂群优化算法研究［J］. 计算机与数字工程, 2019, 47(5): 1060-1066.
Ma J Q, Li Y, Hua M Y, et al. Research on an Adaptive Chaos Bee Colony Optimization Algorithm［J］. Computer and Digital Engineering, 2019, 47(5): 1060-1066.
［22］张永, 陈锋. 一种改进的鲸鱼优化算法［J］. 计算机工程, 2018, 44(3): 208-213.
Zhang Y, Chen F. A Modified Whale Optimization Algorithm［J］. Computer Engineering, 2018, 44(3): 208-213.
［23］王永骥,苏婷婷,刘磊.基于柯西变异的多策略协同进化粒子群算［J］.系统仿真学报,2018,30(8):2875-2883.
Wang Y J, Su T T, Liu L. Multi-strategy Coevolutionary Particle Swarm Optimization Algorithm Based on Cauchy Variation［J］.Journal of System Simulation,2018,30(8):2875-2883.
［24］Chen Y X, Chen Z C, Wu L J, et al. Parameter extraction of PV models using an enhanced shuffled complex evolution algorithm improved by opposition based learning［J］. Energy Procedia,2019,158: 991-997.
［25］杨慎涛,刘文波.基于粒子群的神经网络测试生成算法［J］. 计量学报,2015,36(2):197-201.
Yang S T, Liu W B. Test generation algorithm of neural network based on particle swarm optimization ［J］. Acta Metrologica Sinica,2015,36(2):197-201.
［26］胡春海,李涛,刘永红,等.基于改进差分进化算法的运动想象脑机接口频带选择［J］. 计量学报,2018,39(2):276-279.
Hu C H, Li T, Liu Y H, et al. Motor Imagrey Brain Computer Interface Band Selection Based on Improved Differential Evolution Algorithm［J］. Acta Metrologica Sinica,2018,39(2):276-279.
［27］刘欣博,边亚伟,王慧娴.蓄电池模型参数辨识及在SOC估计中的应用［J］. 北方工业大学学报,2018,30(2):27-35.
Liu X B, Bian Y W, Wang H Xn. Parameter Identification of Battery Model and It's application in SOC Estimation［J］. Journal of North China University of Technology,2018,30(2):27-35.