针对基于扩展卡尔曼滤波（EKF）法锂离子电池SOC估计,易受最小二乘法及其改进方法的模型参数在线辨识精度影响,提出一种改进遗忘因子的最小二乘在线参数辨识方法（IMAFFRLS）。以双极化等效电路模型为基础,分析传统的基于遗忘因子的最小二乘法（FFRLS）辨识模型参数时产生误差的原因,指出单一遗忘因子难以准确跟踪多个以不同速率变化的模型参数。通过对FFRLS算法中的协方差和增益矩阵解耦,引入多个可变遗忘因子独立修正不同参数的估计误差;并以移动区间内的输入电流波动程度和输出电压观测误差为依据,实现各遗忘因子的自适应变化。此外,将改进前后的两种参数辨识算法分别与EKF算法联合,实现锂离子电池SOC估计。最后基于Matlab进行对比仿真验证,结果表明,相对于FFRLS-EKF算法,所提出的IMAFFRLS-EKF算法辨识模型参数以及估计SOC的精度更高。
Abstract
A least-squares online parameter identification method with an improved forgetting factor （IMAFFRLS） is proposed for the extended Kalman filter （EKF） method-based lithium-ion battery SOC estimation, which is vulnerable to the online identification accuracy of model parameters by the least-squares method and its improvement methods. Based on the dual-polarized equivalent circuit model, the causes of errors in the traditional forgetting factor-based least squares（FFRLS） method for identifying model parameters are analyzed, and it is noted that it is difficult to accurately track multiple model parameters varying at different rates with a single forgetting factor. By decoupling the covariance and gain matrices in the FFRLS algorithm, multiple variable forgetting factors are introduced to independently correct the estimation errors of different parameters. The adaptive variation of each forgetting factor is achieved based on the degree of input current fluctuation and the output voltage observation error in the moving interval. In addition, the two parameter identification algorithms before and after the improvement are combined with the EKF algorithm to realize the SOC estimation of Li-ion batteries. Finally, a comparative simulation based on Matlab is performed to verify the proposed model. The simulation results show that the proposed IMAFFRLS-EKF algorithm can identify the model parameters and estimate the SOC with higher accuracy than the FFRLS-EKF algorithm.
关键词
锂电池 /
参数辨识 /
状态估计 /
扩展卡尔曼滤波 /
遗忘因子 /
最小二乘法
Key words
lithium battery /
parameter identification /
state estimation /
extended Kalman filters /
forgetting factor /
least squares
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] SCHMUCH R, WAGNER R, HÖRPEL G, et al. Performance and cost of materials for lithium-based rechargeable automotive batteries[J]. Nature energy, 2018, 3(4): 267-278.
[2] 何冰琛, 杨薛明, 王劲松, 等.基于PCA-GPR的锂离子电池剩余使用寿命预测[J].太阳能学报, 2022, 43(5): 484-491.
HE B C, YANG X M, WANG J S, et al.Prediction of remaining useful life of lithium-ion batteries based on PCA-GPR[J]. Acta energiae solaris sinica, 2022, 43(5): 484-491.
[3] 王子毅, 朱承治, 周杨林, 等.基于动态可重构电池网络的OCV-SOC在线估计[J].中国电机工程学报, 2022, 42(8): 2919-2929.
WANG Z Y, ZHU C Z, ZHOU Y L, et al.OCV-SOC estimation based on dynamic reconfigurable battery network[J]. Proceedings of the CSEE, 2022, 42(8): 2919-2929.
[4] 周雅夫, 史宏宇.面向实车数据的电动汽车电池退役轨迹预测[J].太阳能学报, 2022, 43(5) : 510-517.
ZHOU Y F, SHI H Y.Battery retirement trajectory prediction of electric vehicle based on real vehicle data[J]. Acta energiae solaris sinica, 2022, 43(5): 510-517.
[5] 孙鹏宇, 李建良, 陶知非, 等.动态工况电池在线参数辨识及SOC估计研究[J].电子测量与仪器学报, 2021, 35(1): 10-17.
SUN P Y, LI J L, TAO Z F, et al.Research on online parameter identification and SOC estimation of battery under dynamic conditions[J]. Journal of electronic measurement and instrumentation, 2021, 35(1): 10-17.
[6] 胡捷, 苏建徽, 杜燕, 等. 基于最小二乘法的PEMFC电堆模型参数辨识[J]. 太阳能学报, 2021, 42(6): 1-4.
HU J, SU J H, DU Y, et al.Parameter identification of PEMFC stack model based on least square method[J]. Acta energiae solaris sinica, 2021, 42(6): 1-4.
[7] 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 H, GUO L, WU L X, 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.
[8] VAHIDI A, STEFANOPOULOU A, PENG H.Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments[J]. Vehicle system dynamics, 2005, 43(1): 31-55.
[9] 唐佳, 刘士齐, 刘静雯, 等. 基于向量式多遗忘因子最小二乘法的城轨列车储能元件充放电参数辨识[J].武汉大学学报(工学版), 2020, 53(6) : 527-533.
TANG J, LIU S Q, LIU J W, et al.Parameter identification in urban rail train energy storage elements using a vector multi-forgetting factor least square method[J].Engineering journal of Wuhan University, 2020, 53(6): 527-533.
[10] ROZAQI L, RIJANTO E, KANARACHOS S.Comparison between RLS-GA and RLS-PSO for Li-ion battery SOC and SOH estimation: a simulation study[J]. Journal of mechatronics, electrical power, and vehicular technology, 2017, 8(1): 40-49.
[11] HAO X Y, WANG S L, FAN Y C, et al.An improved forgetting factor recursive least square and unscented particle filtering algorithm for accurate lithium-ion battery state of charge estimation[J]. Journal of energy storage, 2023, 59: 106478.
[12] 王浩, 郑燕萍, 虞杨.基于动态优选遗忘因子最小二乘在线辨识的磷酸铁锂电池SOC估算[J].汽车技术, 2021(10): 23-29.
WANG H, ZHENG Y P, YU Y.Lithium iron phosphate battery SOC estimation based on the least square online identification of dynamic optimal forgetting factor[J]. Automobile technology, 2021(10): 23-29.
[13] OUYANG T C, XU P H, CHEN J X, et al.Improved parameters identification and state of charge estimation for lithium-ion battery with real-time optimal forgetting factor[J]. Electrochimica acta, 2020,353: 136576.
[14] RAHIMI-EICHI H, BARONTI F, CHOW M Y.Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells[J]. IEEE transactions on industrial electronics, 2014, 61(4): 2053-2061.
[15] ZHOU Z K, DUAN B, KANG Y Z, et al.A low-complexity state of charge estimation method for series-connected lithium-ion battery pack used in electric vehicles[J]. Journal of power sources, 2019, 441: 226972.
[16] ZHENG F D, XING Y J, JIANG J C, 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.
[17] XING Y J, HE W, PECHT M, et al.State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures[J]. Applied energy, 2014, 113: 106-115.
[18] HE W, WILLIARD N, CHEN C C, et al.State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation[J]. International journal of electrical power & energy systems, 2014, 62: 783-791.
基金
安徽省高校协同创新项目（GXXT-2021-025）; 国家级大学生创新创业训练计划（202110359021; 202210359021）
PDF(3310 KB)
