最小二乘算法优化及其在锂离子电池参数辨识中的应用研究

doi:10.19595/j.cnki.1000-6753.tces.222214

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摘要

传统最小二乘法（LS）用于锂离子电池模型在线参数辨识精度低,通过带遗忘因子递推最小二乘算法能够有效提高辨识精度,但固定的遗忘因子影响模型动态特性。遗忘因子的自适应处理能提高算法对动态系统的参数辨识能力,而目前的自适应方法容易忽略模型参数稳定性,且方法待定系数范围较大难以确认。为了得到高精度且稳定性良好的模型参数,本文设计了一种精度和稳定性兼优且更简单的自适应遗忘因子递推最小二乘（AFFRLS）改进方法,并与其他AFFRLS、可变遗忘因子递推最小二乘（VFFRLS）进行仿真对比分析。结果表明,本文改进的AFFRLS能够在模型精度和参数稳定性取得更好的平衡,且对不同的在线工况具有良好的适用性。

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关键词 ：锂离子电池模型, 参数辨识, 最小二乘法, 自适应遗忘因子

Abstract：

Off-line and on-line methods are used to identify model parameters, but the model dynamic characteristic obtained by on-line method is better. Recursive least squares method is simple and it's often used for on-line parameter identification of lithium-ion battery models. However, the RLS has a low identification accuracy. Thus, the forgetting factor recursive least square method was proposed to improve the accuracy for parameter identification. In order to improve the dynamic identification ability, the variable forgetting factor least square (VFFRLS) method and adaptive forgetting factor recursive least square (AFFRLS) method appears. Yet the current adaptive methods tend to ignore the stability of model parameters, and the undetermined coefficient range of this method is large and difficult to confirm. The model parameter changes drastically is easy to cause the divergence of the algorithm. To address these issues, this paper proposes a simpler AFFRLS method without undetermined coefficient. And it takes into account the accuracy and stability of the model.
Firstly, based on dynamic stress testing (DST) and Federal City Operating Conditions (FUDS) data, the FFRLS method with fixed forgetting factor value is simulated and analyzed, and the influence trend of different forgetting factor on the accuracy and stability of model parameters is obtained. Secondly, the proposed AFFRLS method is compared with other AFFRLS and VFFRLS, and the stability and accuracy of the identification parameters are analyzed. Finally, the error tracking ability and convergence speed of the three adaptive methods are analyzed, and the adaptive performance of the proposed AFFRLS to DST and FUDS conditions are analyzed.
The FFRLS simulation results with fixed forgetting factor(λ) value show that when λ value decreases, the algorithm has better tracking ability for time-varying parameters, the convergence speed is accelerated, and the identification accuracy is effectively improved. However, when λ value decreases the parameter changes drastically and the stability decreases. It can be seen that obtaining the appropriate λ value is important for the identification ability of the adaptive methods. The three adaptive methods simulation results show that, the improved AFFRLS in this paper has better tracking ability for time-varying parameters and high model accuracy. And it has better stability of the parameter obtained by FFRLS with fixed λ values of 0.98 and 0.985. It can be seen that the proposed AFFRLS can achieve a better balance between accuracy and stability. The relationship between λ value and error of the adaptive methods show that the improved AFFRLS can track the error variation better. By compared the operation time with three methods, the results show that the proposed AFFRLS has the faster convergence rate. According to the relationship between λ value and time in DST and FUDS conditions, the improved AFFRLS method has the majority of λ value near 0.98 in FUDS condition, and the majority of λ value is 1 in DST condition.
The simulation analysis shows that: 1) The proposed AFFRLS method can improve the accuracy of the algorithm and give the stability of model parameters into consideration, and it has good balance between algorithm accuracy and parameter stability. Applying the proposed AFFRLS method and Kalman filter to predict the state of charge can improve the prediction accuracy better. 2) The proposed AFFRLS method has better tracking ability for error variation and faster convergence speed. 3) The proposed method can improve the algorithm accuracy under both slow and drastic conditions, so it's suitable for different on-line conditions.

Key words： lithium-ion battery model parameter identification least square method adaptive forgetting factor

收稿日期: 2022-11-24

PACS:

TM912

基金资助:

国家自然科学基金（61741126）和广西自然科学基金（2022GXNSFAA035533）资助项目

通讯作者: 张鑫, 女,1976年生,硕士,高级实验师,研究方向为智能化电器。E-mail：zhangxin_wt@163.com

作者简介: 范兴明, 男,1978年生,教授,博士生导师,研究方向为智能化电器和高电压新技术。E-mail：fanxm_627@163.com

引用本文:

范兴明, 封浩, 张鑫. 最小二乘算法优化及其在锂离子电池参数辨识中的应用研究[J]. 电工技术学报, 0, (): 9001-1. Fan Xingming, Feng Hao, Zhang Xin. Optimization of Least Squares Method and Its Application in Parameter Identification of Lithium-Ion Battery Model. Transactions of China Electrotechnical Society, 0, (): 9001-1.

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[1] 庞辉, 郭龙, 武龙星, 等. 考虑环境温度影响的锂离子电池改进双极化模型及其荷电状态估算[J]. 电工技术学报, 2021, 36(10): 2178-2189.
Pang Hui, Guo Long, Wu Longxing, et al.An improved dual polarization model of Li-ion battery and its state of charge estimation considering ambient temperature[J]. Transactions of China Electrotechnical Society, 2021, 36(10): 2178-2189.
[2] 郭向伟, 邢程, 司阳, 等. RLS锂电池全工况自适应等效电路模型[J]. 电工技术学报, 2022, 37(16): 4029-4037.
Guo Xiangwei, Xing Cheng, Si Yang, et al.RLS adaptive equivalent circuit model of lithium battery under full working condition[J]. Transactions of China Electrotechnical Society, 2022, 37(16): 4029-4037.
[3] You H W, Bae J I, Cho S J, et al.Analysis of equivalent circuit models in lithium-ion batteries[J]. AIP Advances, 2018, 8(12).
[4] Han Xuebing, Ouyang Minggao, Lu Languang, et al.Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part I: Diffusion simplification and single particle model[J]. Journal of Power Sources, 2015, 278: 802-813.
[5] Xu Jun, Mi C C, Cao Binggang, et al.A new method to estimate the state of charge of lithium-ion batteries based on the battery impedance model[J]. Journal of Power Sources, 2013, 233: 277-284.
[6] Zhang Shuzhi, Zhang Xiongwen.A comparative study of different online model parameters identification methods for lithium-ion battery[J].Science China Technological Sciences, 2021, 64(10): 2312-2327.
[7] He Lin, Wang Yangyang, Wei Yujiang, et al.An adaptive central difference Kalman filter approach for state of charge estimation by fractional order model of lithium-ion battery[J]. Energy, 2022, 244: 122627.
[8] 谷苗, 夏超英, 田聪颖. 基于综合型卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2019, 34(2): 419-426.
Gu Miao, Xia Chaoying, Tian Congying.Li-ion battery state of charge estimation based on comprehensive Kalman filter[J]. Transactions of China Electrotechnical Society, 2019, 34(2): 419-426.
[9] 黄凯, 郭永芳, 李志刚. 基于信息反馈粒子群的高精度锂离子电池模型参数辨识[J]. 电工技术学报, 2019, 34(增刊1): 378-387.
Huang Kai, Guo Yongfang, Li Zhigang.High precision parameter identification of lithium-ion battery model based on feedback particle swarm optimization algorithm[J]. Transactions of China Electrotechnical Society, 2019, 34(S1): 378-387.
[10] 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]. 吉林大学学报(工学版), 2012, 42(3): 732-737.
Zhang Caiping, Jiang Jiuchun.Identification of battery model parameters by extended Kalman filter algorithm based on genetic optimization[J]. Journal of Jilin University (Engineering and Technology Edition), 2012, 42(3): 732-737.
[11] Zhang Shuzhi, Zhang Xiongwen.A comparative study of different online model parameters identification methods for lithium-ion battery[J].Science China Technological Sciences, 2021, 64(10): 2312-2327.
[12] 陈玉珊, 秦琳琳, 吴刚, 等. 基于渐消记忆递推最小二乘法的电动汽车电池荷电状态在线估计[J]. 上海交通大学学报, 2020, 54(12): 1340-1346.
Chen Yushan, Qin Linlin, Wu Gang, et al.Online state of charge estimation for battery in electric vehicles based on forgetting factor recursive least squares[J]. Journal of Shanghai Jiao Tong University, 2020, 54(12): 1340-1346.
[13] Wei Zhongbao, Meng Shujuan, Xiong Binyu, et al.Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer[J]. Applied Energy, 2016, 181: 332-341.
[14] Sun Xiangdong, Ji Jingrun, Ren Biying, et al.A novel online identification algorithm of lithium-ion battery parameters and model order based on a fractional order model[J]. IET Renewable Power Generation, 2021, 15(11): 2396-2408.
[15] Zhang Cheng, Allafi W, Dinh Q, et al.Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique[J]. Energy, 2018, 142: 678-688.
[16] Feng Tianheng, Yang Lin, Zhao Xiaowei, et al.Online identification of lithium-ion battery parameters based on an improved equivalent-circuit model and its implementation on battery state-of-power prediction[J]. Journal of Power Sources, 2015, 281: 192-203.
[17] 谢文超, 赵延明, 方紫微, 等. 带可变遗忘因子递推最小二乘法的超级电容模组等效模型参数辨识方法[J]. 电工技术学报, 2021, 36(5): 996-1005
Xie Wenchao, Zhao Yanming, Fang Ziwei, et al.Variable forgetting factor recursive least squales based parameter identification method for the equivalent circuit model of the supercapacitor cell module[J]. Transactions of China Electrotechnical Society, 2021, 36(5): 996-1005
[18] Lao Zizhou, Xia Bizhong, Wang Wei, et al.A novel method for lithium-ion battery online parameter identification based on variable forgetting factor recursive least squares[J]. Energies, 2018, 11(6): 1358.
[19] Wang Hao, Zheng Yanping, Yu Yang.Lithium-ion battery SOC estimation based on adaptive forgetting factor least squares online identification and unscented Kalman filter[J]. Mathematics, 2021, 9(15): 1733.
[20] Sun Xiangdong, Ji Jingrun, Ren Biying, et al.Adaptive forgetting factor recursive least square algorithm for online identification of equivalent circuit model parameters of a lithium-ion battery[J]. Energies, 2019, 12(12): 2242.
[21] Zheng Fangdan, Xing Yinjiao, Jiang Jiuchun, et al.Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries[J]. Applied Energy, 2016, 183: 513-525.