基于自适应UKF的锂离子动力电池状态联合估计

doi:10.12068/j.issn.1005-3026.2020.11.006

东北大学学报:自然科学版 ›› 2020, Vol. 41 ›› Issue (11): 1557-1563.DOI: 10.12068/j.issn.1005-3026.2020.11.006

基于自适应UKF的锂离子动力电池状态联合估计

章军辉^1,2,3，李庆^1,2，陈大鹏^1,2,3，赵野¹

(1. 中国科学院微电子研究所，北京100029； 2. 江苏物联网研究发展中心，江苏无锡214135； 3. 无锡物联网创新中心有限公司，江苏无锡214135)

收稿日期:2020-03-21 修回日期:2020-03-21 出版日期:2020-11-15 发布日期:2020-11-16
通讯作者: 章军辉
作者简介:章军辉(1985-)，男，安徽合肥人，中国科学院博士后研究人员；李庆(1972-)，男，吉林长春人，中国科学院研究员，博士生导师；陈大鹏(1968-)，男，安徽合肥人，中国科学院研究员，博士生导师; 赵野(1977-)，男，辽宁绥中人，中国科学院研究员，博士生导师.
基金资助:
江苏省博士后科研资助计划(2020Z411)；国家重点研发计划项目(2016YFB0100516).

State Co-estimation Algorithm for Li-Ion Power Batteries Based on Adaptive Unscented Kalman Filters

ZHANG Jun-hui^1,2,3， LI Qing^1,2， CHEN Da-peng^1,2,3， ZHAO Ye¹

1. Institute of Microelectronics， Chinese Academy of Sciences， Beijing 100029， China; 2. Jiangsu R&D Center for Internet of Things， Wuxi 214135， China; 3. Wuxi Internet of Things Innovation Center Co.， Ltd.， Wuxi 214135， China.

Received:2020-03-21 Revised:2020-03-21 Online:2020-11-15 Published:2020-11-16
Contact: CHEN Da-peng
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对由静态的电池模型参数而造成的状态估计累计误差、噪声统计特性的时变不确定性等实用化的问题，基于无迹卡尔曼滤波(unscented Kalman filter， UKF)框架设计了一种自适应UKF的电池状态联合估计算法.在无迹变换(unscented transform，UT)时，对量测方程进行准线性化处理，降低了循环迭代过程中的计算开销；利用带遗忘因子的Sage-Husa自适应估计方法对过程噪声的统计特性参数进行递推估计与修正，提高了UKF估计算法的自适应容错能力；实时跟踪滤波的收敛性，若呈发散趋势时，通过自适应衰减因子对误差协方差进行调整以抑制滤波发散，保证了滤波过程的数值稳定性；采用联合估计策略对一阶Thevenim电池欧姆内阻模型参数进行在线更新，以确保动态测试工况下电池模型的准确性，从而提高了电池荷电状态(state of charge，SOC)以及电池健康状态(state of health，SOH)的估计精度.实验与仿真结果验证了该电池状态联合估计算法的可行性与有效性.

关键词: 荷电状态, 健康状态, 无迹卡尔曼滤波, 自适应滤波, 锂离子动力电池

Abstract: On the practical issues such as accumulative state estimation errors caused by the inaccurate battery model， and time-varying and unknown noise statistical characteristics， a state co-estimation algorithm for Li-ion power batteries based on adaptive unscented Kalman filter (UKF) framework is proposed. During the iteration process， the measurement equation called by unscented transform (UT) each time is quasi-linearized， which could greatly reduce the computational complexity. The statistical characteristic parameters of unknown noises can be estimated by introducing Sage-Husa adaptive estimation method with forgetting factor， which could improve the adaptive fault tolerance of UKF estimation algorithm. If the divergence trend occurs in the filtering process， the error covariance will be penalized by employing adaptive attenuation factor to prevent the divergence of the filter， accordingly guaranteeing the numerical stability of filtering process. By employing co-estimation strategy， the inner ohmic resistance of the 1st-order Thevenin battery model， which could characterize the state of health (SOH) indirectly， can be estimated and adjusted in real time， and therefore a high-precision estimation of the state of charge (SOC) is able to be obtained owing to the accuracy of the battery model during the process of dynamic charge-discharge cycling tests. The comparative experimental results verify the feasibility and effectiveness of the adaptive co-estimation algorithm.

Key words: state of charge(SOC), state of health(SOH), unscented Kalman filter(UKF), adaptive filter, Li-ion power batter

中图分类号:

TM912.9

章军辉，李庆，陈大鹏，赵野. 基于自适应UKF的锂离子动力电池状态联合估计[J]. 东北大学学报:自然科学版, 2020, 41(11): 1557-1563.

ZHANG Jun-hui， LI Qing， CHEN Da-peng， ZHAO Ye. State Co-estimation Algorithm for Li-Ion Power Batteries Based on Adaptive Unscented Kalman Filters[J]. Journal of Northeastern University Natural Science, 2020, 41(11): 1557-1563.

参考文献

[1]Shen J N，Shen J J，He Y J，et al.Accurate state of charge estimation with model mismatch for Li-ion batteries:a joint moving Horizon estimation approach［J］.IEEE Transactions on Power Electronics，2019，34(5):4329-4342.
[2]Zhang C，Wang L Y，Li X，et al.Robust and adaptive estimation of state of charge for lithium-ion batteries［J］.IEEE Transactions on Industrial Electronics，2015，62(8):4948-4957.
[3]Paschero M，Storti G L，Rizzi A，et al.A novel mechanical analogy based battery model for SoC estimation using a multi-cell EKF［J］.IEEE Transactions on Sustainable Energy，2016，7(4):1695-1702.
[4]Dai H，Zhao G，Lin M，et al.A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain［J］.IEEE Transactions on Industrial Electronics，2019，66(10):7706-7716.
[5]Ahmed R，Sayed M E，Arasaratnam I，et al.Reduced-order electrochemical model parameters identification and SOC estimation for healthy and aged Li-ion batteries part I:parameterization model development for healthy batteries［J］.IEEE Journal of Emerging and Selected Topics in Power Electronics，2014，2(3):659-677.
[6]Lawsri K，Pongam S.DC electronics load for AH battery testing［C］//2017 International Electrical Engineering Congress (iEECON).Pattaya:IEEE，2017:1-4.
[7]Petzl M，Danzer M A.Advancements in OCV measurement and analysis for lithium-ion batteries［J］.IEEE Transactions on Energy Conversion，2013，28(3):675-681.
[8]Li J，Liu M.State-of-charge estimation of lithium-ion batteries using composite multi-dimensional features and a neural network［J］.IET Power Electronics，2019，12(6):1470-1478.
[9]Afshar S，Morris K，Khajepour A.State-of-charge estimation using an EKF-based adaptive observer［J］.IEEE Transactions on Control Systems Technology，2019，27(5):1907-1923.
[10]Meng J，Luo G，Gao F.Lithium polymer battery state-of-charge estimation based on adaptive unscented Kalman filter and support vector machine［J］.IEEE Transactions on Power Electronics，2016，31(3):2226-2238.
[11]Wang D，Yang F，Tsui K，et al.Remaining useful life prediction of lithium-ion batteries based on spherical cubature particle filter［J］.IEEE Transactions on Instrumentation and Measurement，2016，65(6):1282-1291.
[12]Takeno K，Ichimura M，Takano K，et al.Quick testing of batteries in lithium-ion battery packs with impedance-measuring technology［J］.Journal of Power Sources，2004，128(1):67-75.
[13]Abrahama D P，Knuth J L，Dees D W，et al.Performance degradation of high-power lithium-ion cells-electrochemistry of harvested electrodes［J］.Journal of Power Sources，2007，170(2):65-475.
[14]Ramadass P，Haran B，Whtte R，et al.Mathematical modeling of the capacity fade of Li-ion cells［J］.Journal of Power Sources，2003，123(2):230-240.
[15]Klass V，Behm M，Lindbergh G.A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation［J］.Journal of Power Sources，2014，270:262-272.
[16]EL-Mejdoubi A，Gualous H，Chaoui H，et al.Lithium-ion batteries health prognosis considering aging conditions［J］.IEEE Transactions on Power Electronics，2019，34(7):6834-6844.
[17]Kim J，Cho B H.State-of-charge estimation and state-of-health prediction of a Li-ion degraded battery based on an EKF combined with a per-unit system［J］.IEEE Transactions on Vehicular Technology，2011，60(9):4249-4260.
[18]Lin H T，Liang T J，Chen S M.Estimation of battery state of health using probabilistic neural network［J］.IEEE Transactions on Industrial Informatics，2013，9(2):679-685.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)

基于自适应UKF的锂离子动力电池状态联合估计

State Co-estimation Algorithm for Li-Ion Power Batteries Based on Adaptive Unscented Kalman Filters

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 7

编辑推荐

Metrics

本文评价

[1]	刘芳，刘彦鹏，李静东，卜凡涛. 车载动力电池放电过程健康状态在线估计[J]. 东北大学学报（自然科学版）, 2022, 43(11): 1544-1551.
[2]	刘芳，李卓，苏卫星，刘阳. 阶次自适应AR等效电路模型的锂电池SOC滑模观测[J]. 东北大学学报（自然科学版）, 2021, 42(10): 1376-1385.
[3]	闫士杰，王立华，闫伟航，高文忠. 基于采样数据的大规模储能系统一致性控制[J]. 东北大学学报:自然科学版, 2020, 41(11): 1528-1534.
[4]	刘芳，马杰，苏卫星，何茂伟. 基于模型参数在线辨识技术的SOC估算方法[J]. 东北大学学报:自然科学版, 2020, 41(11): 1543-1549.
[5]	印明昂，王钰烁，孙志礼，郭兵. 一种基于Newton迭代法的累积Logistic回归模型参数估计[J]. 东北大学学报:自然科学版, 2020, 41(1): 79-83.
[6]	伦淑娴;张化光;冯健. 基于神经网络的多传感器自适应滤波及其应用[J]. 东北大学学报(自然科学版), 2003, 24(8): 727-730.
[7]	伦淑娴;冯健;张化光;王占山. 基于遗传算法的多传感器自适应噪声抵消器[J]. 东北大学学报(自然科学版), 2003, 24(7): 627-630.