基于改进多新息扩展卡尔曼滤波的电池SOC估计

doi:10.3785/j.issn.1008-973X.2021.10.020

浙江大学学报(工学版)

2021, Vol. 55

Issue (10): 1978-1985 DOI: 10.3785/j.issn.1008-973X.2021.10.020

电气工程

基于改进多新息扩展卡尔曼滤波的电池SOC估计

雷克兵(

),陈自强*(

)

上海交通大学海洋工程国家重点实验室，上海 200240

Estimation of state of charge of battery based on improved multi-innovation extended Kalman filter

Ke-bing LEI(

),Zi-qiang CHEN*(

)

State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

全文: PDF(3657 KB) HTML

摘要：

为了提高SOC估计精度，提出基于遗忘因子改进多新息扩展卡尔曼滤波（FMIEKF）方法. 建立锂离子电池的双极化等效电路模型，开展开路电压测试. 通过递归最小二乘法，实现电池模型参数在线辨识. 提出FMIEKF进行SOC估计，该方法在融合多新息辨识理论和卡尔曼滤波基础上，引入遗忘因子削弱历史数据修正权重，解决数据过饱和问题. 通过实验和硬件在环进行验证. 结果表明，FMIEKF具有较高的准确性和收敛性，最大估计误差为0.948%，平均误差为0.214%，在不同SOC初值下20 s内收敛，可以适用于实际的电池管理系统中.

关键词： 锂离子电池; 多新息辨识; 卡尔曼滤波; SOC估计; 硬件在环

Abstract:

An improved multi-innovation extended Kalman filter was proposed based on the forgetting factor in order to improve the accuracy of SOC estimation. Dual-polarization equivalent circuit model of lithium-ion battery was established, and open-circuit voltage testing was conducted. Recursive least squares method was used to realize online battery model parameter identification. FMIEKF was proposed for SOC estimation based on the fusion of multi-innovation identification theory and Kalman filtering. A forgetting factor was introduced to weaken the historical data correction weight and solve the problem of data oversaturation. The method was verified through experiments and hardware-in-the-loop. The experimental results show that FMIEKF has higher accuracy and convergence. The maximum estimation error was 0.948%, the average error was 0.214%, and the FMIEKF converged within 20 seconds under different initial values of SOC. The method can be applied to the actual battery management system.

Key words: lithium-ion battery multi-innovation recognition Kalman filter estimation of SOC hardware in loop

收稿日期: 2020-11-26 出版日期: 2021-10-27

CLC:

TM 912

基金资助: 国家自然科学基金资助项目（51677119）

通讯作者: 陈自强 E-mail: Kebing_Lei@sjtu.edu.cn;chenziqiang@sjtu.edu.cn

作者简介: 雷克兵（1995—），男，硕士生，从事储能技术的研究. orcid.org/0000-0003-2296-4399. E-mail： 864673032@qq.com

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	作者相关文章
	雷克兵
	陈自强

引用本文:

雷克兵,陈自强. 基于改进多新息扩展卡尔曼滤波的电池SOC估计[J]. 浙江大学学报(工学版), 2021, 55(10): 1978-1985.

Ke-bing LEI,Zi-qiang CHEN. Estimation of state of charge of battery based on improved multi-innovation extended Kalman filter. Journal of ZheJiang University (Engineering Science), 2021, 55(10): 1978-1985.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.10.020 或 https://www.zjujournals.com/eng/CN/Y2021/V55/I10/1978

图 1 双极化等效电路模型

表 1 不同多项式的拟合结果统计

图 2 Uocv-SOC特性曲线的拟合

表 2 100%SOC下的HPPC参数辨识结果

图 3 滑动数据窗口的示意图

图 4 FFRLS-FMIEKF联合SOC估计流程

图 5 电池充放电实验装置结构

表 3 实验电池的参数表

图 6 FFRLS参数辨识结果

图 7 SOC估计准确性的对比

表 4 不同算法的SOC估计误差统计

图 8 FMIEKF收敛结果

图 9 不同新息长度下的SOC估计结果

图 10 不同遗忘因子的SOC估计结果

图 11 硬件在环验证平台

图 12 硬件在环与实验结果的对比

表 5 改进MIEKF的HIL结果统计

1	GRUOSSO G, GAJANI G, RUIZ F, et al A virtual sensor for electric vehicles’ state of charge estimation[J]. Electronics, 2020, 9 (2): 278 doi: 10.3390/electronics9020278
2	LI Y, CHATTOPADHYAY P, XIONG S, et al Dynamic data-driven and model-based recursive analysis for estimation of battery state-of-charge[J]. Applied Energy, 2016, 184 (12): 266- 275
3	周诗尧, 陈自强, 郑昌文, 等全海深深潜器所用动力锂离子电池电气特性[J]. 上海交通大学学报, 2019, 53 (1): 49- 54 ZHOU Shi-yao, CHEN Zi-qiang, ZHENG Chang-wen, et al Electrical characteristics of power lithium-ion batteries used in all-sea deep submersibles[J]. Journal of Shanghai Jiaotong University, 2019, 53 (1): 49- 54
4	韩雪冰. 车用锂离子电池机理模型与状态估计研究[D]. 北京: 清华大学, 2014. HAN Xue-bing. Research on mechanism model and state estimation of lithium-ion batteries for vehicles [D]. Beijing: Tsinghua University, 2014.
5	ZHENG C, CHEN Z, HUANG D Fault diagnosis of voltage sensor and current sensor for lithium-ion battery pack using hybrid system modeling and unscented particle filter[J]. Energy, 2020, 191 (1): 116504
6	刘征宇, 杨俊斌, 张庆, 等基于QPSO-BP神经网络的锂电池SOC预测[J]. 电子测量与仪器学报, 2013, 27 (3): 44- 48 LIU Zheng-yu, YANG Jun-bin, ZHANG Qing, et al Lithium battery SOC prediction based on QPSO-BP neural network[J]. Journal of Electronic Measurement and Instrument, 2013, 27 (3): 44- 48
7	KLASS V, BEHM M, LINDBERGH G Capturing lithium-ion battery dynamics with support vector machine-based battery model[J]. Journal of Power Sources, 2015, 298 (8): 92- 101
8	PING S, OUYANG M, LU L, et al The co-estimation of state of charge, state of health, and state of function for lithium-ion batteries in electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2018, 67 (1): 92- 103 doi: 10.1109/TVT.2017.2751613
9	XIONG R, GONG X, MI C C, et al A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter[J]. Journal of Power Sources, 2013, 243 (6): 805- 816
10	DUAN J, WANG P, MA W, et al State of charge estimation of lithium battery based on improved correntropy extended Kalman filter[J]. Energies, 2020, 13 (16): 4197 doi: 10.3390/en13164197
11	WASSILIADIS N, ADERMANN J, FRERICKS A, et al Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: a use-case life cycle analysis[J]. The Journal of Energy Storage, 2018, 19 (10): 73- 87
12	LI W, YANG Y, WANG D, et al The multi-innovation extended Kalman filter algorithm for battery SOC estimation[J]. Ionics, 2020, 26 (10): 1- 12 doi: 10.1007/s11581-020-03716-0
13	吴佳铭, 陈自强可变低温环境锂电池组内部短路故障诊断[J]. 浙江大学学报:工学版, 2020, 54 (7): 1433- 1439 WU Jia-ming, CHEN Zi-qiang Fault diagnosis of internal short circuit of lithium battery pack in variable low temperature environment[J]. Journal of Zhejiang University: Engineering Science Edition, 2020, 54 (7): 1433- 1439
14	葛云龙, 陈自强, 郑昌文 UTSTF锂离子电池时变参数估计与故障诊断[J]. 浙江大学学报: 工学版, 2018, 52 (6): 1223- 1230 GE Yun-long, CHEN Zi-qiang, ZHENG Chang-wen Time-varying parameter estimation and fault diagnosis of UTSTF lithium-ion battery[J]. Journal of Zhejiang University: Engineering Science Edition, 2018, 52 (6): 1223- 1230
15	HUANG D, CHEN Z, ZHENG C, et al A model-based state-of-charge estimation method for series-connected lithium-ion battery pack considering fast-varying cell temperature[J]. Energy, 2019, 185 (C): 847- 861

[1]	王思鹏,杜昌平,宋广华,郑耀. 基于改进MSCKF的无人机室内定位方法[J]. 浙江大学学报(工学版), 2022, 56(4): 711-717.
[2]	郭承乾,马刚,梅江洲,张贵科,李宏璧,周伟. 基于InSAR与多源数据融合的堆石坝外观变形重构[J]. 浙江大学学报(工学版), 2022, 56(2): 347-355.
[3]	伍川辉,廖家,熊仕勇,牛英杰,周博. 基于激光传感器的槽型轨轮廓匹配方法[J]. 浙江大学学报(工学版), 2021, 55(9): 1607-1614.
[4]	夏雪,赵震,张晋杰,唐亮. 车用圆柱锂电池及模组的机械完整性[J]. 浙江大学学报(工学版), 2021, 55(11): 2134-2141.
[5]	王帅林,盛雷,齐丽娜,方奕栋,李康,苏林. 大型软包锂离子电池的热物性实验研究[J]. 浙江大学学报(工学版), 2021, 55(10): 1986-1992.
[6]	潘斌,董栋,钱东培,钮树强,刘双宇,姜银珠. 磷酸铁锂电池内阻分量快速检测方法[J]. 浙江大学学报(工学版), 2021, 55(1): 189-194.
[7]	朱清,任王瑜,姜孝男,陈卫祥. Bi₂S₃-MoS₂/石墨烯复合材料的合成及电化学储锂性能[J]. 浙江大学学报(工学版), 2019, 53(7): 1306-1314.
[8]	黄钰期,梅盼,陈晓济,邓长水. 基于CFD分析的电动汽车电池包加热方法[J]. 浙江大学学报(工学版), 2019, 53(2): 207-213.
[9]	葛云龙, 陈自强, 郑昌文. UTSTF锂离子电池时变参数估计与故障诊断[J]. 浙江大学学报(工学版), 2018, 52(6): 1223-1230.
[10]	刘景明, 黄平捷, 侯迪波, 张光新, 张宏建. 河流突发污染的污染物浓度动态校正方法[J]. 浙江大学学报(工学版), 2017, 51(12): 2459-2465.
[11]	叶肖伟, 刘坦, 董传智, 陈斌. 基于卡尔曼滤波和中性轴位置的结构损伤识别[J]. 浙江大学学报(工学版), 2017, 51(10): 2012-2018.
[12]	宋开臣,曾瑶,叶凌云. 基于多传感器信息融合的涡街信号处理方法[J]. 浙江大学学报(工学版), 2016, 50(7): 1307-1312.
[13]	刘刚, 靳立强, 王熠. 乘用车稳定性自抗扰控制策略[J]. 浙江大学学报(工学版), 2016, 50(12): 2289-2296.
[14]	莫元富, 于德新, 宋军, 郭亚娟. 基于信道负载阈值的车联网信标消息生成策略[J]. 浙江大学学报(工学版), 2016, 50(1): 21-26.
[15]	朱光明, 蒋荣欣, 周凡, 田翔, 陈耀武. 带测量偏置估计的鲁棒卡尔曼滤波算法[J]. 浙江大学学报(工学版), 2015, 49(7): 1343-1349.

Viewed

Full text

Abstract

Cited

Shared

Discussed