[1]盛国良,翁朝阳,陆宝春.基于改进型自适应强跟踪卡尔曼滤波的电池SOC估算[J].南京理工大学学报(自然科学版),2020,44(06):689-695.[doi:10.14177/j.cnki.32-1397n.2020.44.06.008]
 Sheng Guoliang,Weng Chaoyang,Lu Baochun.Battery SOC estimation based on improved adaptivestrong tracking Kalman filter[J].Journal of Nanjing University of Science and Technology,2020,44(06):689-695.[doi:10.14177/j.cnki.32-1397n.2020.44.06.008]
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基于改进型自适应强跟踪卡尔曼滤波的电池SOC估算()
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
44卷
期数:
2020年06期
页码:
689-695
栏目:
出版日期:
2020-12-31

文章信息/Info

Title:
Battery SOC estimation based on improved adaptivestrong tracking Kalman filter
文章编号:
1005-9830(2020)06-0689-07
作者:
盛国良1翁朝阳2陆宝春2
1.南京工程学院 工业中心,江苏 南京211167; 2.南京理工大学 机械工程学院,江苏 南京 210094
Author(s):
Sheng Guoliang1Weng Chaoyang2Lu Baochun2
1.Industrial Center,Nanjing Institute of Technology,Nanjing 211167,China; 2.School of Mechanical Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
荷电状态 扩展卡尔曼滤波 自适应滤波器 强跟踪滤波器
Keywords:
state of charge extended Kalman filter adaptive filter strong tracking filte
分类号:
TH137; TP273.4
DOI:
10.14177/j.cnki.32-1397n.2020.44.06.008
摘要:
为解决扩展卡尔曼滤波算法估算锂电池荷电状态(State of charge,SOC)时存在的系统噪声统计不确定性和电池模型不准确性问题,该文提出了一种基于改进型Sage-Husa自适应强跟踪卡尔曼滤波的SOC估算算法。以参数辨识得到的锂电池等效电路模型为基础,在扩展卡尔曼滤波算法中引入一个强跟踪滤波器的渐消因子来加强跟踪能力,结合可对时变噪声进行特征统计的Sage-Husa自适应滤波器来调整系统噪声参数,实现了锂电池SOC的估算。最后通过锂电池模拟工况实验,验证了该算法相比于扩展卡尔曼滤波具有更高的精度和实用性。
Abstract:
In order to solve the problem of systematic noise statistical uncertainty and battery model inaccuracy in estimating the state of charge(SOC)of lithium battery by the extended Kalman filter algorithm. A state-of-charge estimation algorithm based on the improved Sage-Husa adaptive strong tracking Kalman filter is proposed. Based on the equivalent circuit model of lithium battery obtained by parameter identification,a fading factor of strong tracking filter is introduced into the extended Kalman filter algorithm to enhance the tracking ability of the system. Combined with the sage Husa adaptive filter which can be used to analyze the characteristics of time-varying noise,the system noise parameters are adjusted,and the SOC estimation of lithium battery is realized. Through the lithium battery simulation working condition experiment,it is verified that the algorithm is more accurate and practical than the extended Kalman filter.

参考文献/References:

[1] 吴春芳. 动力电池SOC估算综述[J]. 电源技术,2017,41(12):1795-1798.
Wu Chunfang. Review of state of charge estimation for power battery[J]. Chinese Journal of Power Sources,2017,41(12):1795-1798.
[2]刘大同,周建宝,郭力萌,等. 锂离子电池健康评估和寿命预测综述[J]. 仪器仪表学报,2015,36(1):1-16.
Liu Datong,Zhou Jianbao,Guo Limeng,et al. Survey on lithium-ion battery health assessment and cycle life estimation[J]. Chinese Journal of Scientific Instrument,2015,36(1):1-16.
[3]刘皓,胡明昕,朱一亨,等. 基于遗传算法和支持向量回归的锂电池健康状态预测[J]. 南京理工大学学报,2018,42(3):329-334,351.
Liu Hao,Hu Mingxin,Zhu Yiheng,et al. Prediction for state of health of lithium-ion batteries by genetic algorithm and support vector regression[J]. Journal of Nanjing University of Science and Technology,2018,42(3):329-334,351.
[4]陈建新,候建明,王鑫,等. 基于局部信息融合及支持向量回归集成的锂电池健康状态预测[J]. 南京理工大学学报,2018,42(1):48-55.
Chen Jianxin,Hou Jianming,Wang Xin,et al. Prediction for state of health of lithium-ion batteries by local information fusion with ensemble support vector regression[J]. Journal of Nanjing University of Science and Technology,2018,42(1):48-55.
[5]方磊,陈勇,赵理,等. 基于模糊控制的扩展卡尔曼滤波SOC估计研究[J]. 系统仿真学报,2018,30(1):325-331.
Fang Lei,Chen Yong,Zhao Li,et al. SOC estimation with extended Kalman filter based on fuzzy control[J]. Journal of System Simulation,2018,30(1):325-331.
[6]邓凯锋,王耀南,刘东奇.基于小波变换的卡尔曼滤波动力电池SOC估算[J]. 控制工程,2015,22(3):398-403.
Deng Kaifeng,Wang Yaonan,Liu Dongqi. Kalman filter for HEV’s battery SOC estimation based on wavelet transform[J]. Control Engineering of China,2015,22(3):398-403.
[7]Xiong Rui,He Hongwen,Sun Fengchun,et al. Evaluation on state of charge estimation of batteries with adaptive extended Kalman filter by experiment approach[J]. IEEE Transactions on Vehicular Technology,2013,62(1):108-117.
[8]Xing Yinjiao,He Wei,Michael P,et al. State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures[J]. Applied Energy,2014,113(1):106-115.
[9]Xia Bizhong,Zhang Zheng,Lao Zizhou,et al. Strong tracking of a H-infinity filter in lithium-ion battery state of charge estimation[J]. Energies,2018,11(6):1481.
[10]程泽,杨磊,孙幸勉. 基于自适应平方根无迹卡尔曼滤波算法的锂离子电池SOC和SOH估计[J]. 中国电机工程学报,2018,38(8):2384-2393,2548.
Cheng Ze,Yang Lei,Sun Xingmian. State of charge and state of health estimation of Li-ion batteries based on adaptive square-root unscented Kalman filters[J]. Proceedings of the CSEE,2018,38(8):2384-2393,2548.
[11]Bizeray A M,Zhao S,Duncan S R,et al. Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter[J]. Journal of Power Sources,2015,296:400-412.
[12]Wei Jingwen,Dong Guangzhong,Chen Zonghai. Model-based fault diagnosis of lithium-ion battery using strong tracking extended Kalman filter[J]. Energy Procedia,2019,158:2500-2505.
[13]Sage A P,Husa G W. Adaptive filtering with unknown prior statistic[C]//Proceedings of Joint Automatic Control Conference. Boulder,USA:American Society of Mechanical Engineers,1969:760-769.

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备注/Memo

备注/Memo:
收稿日期:2019-10-30 修回日期:2020-10-17
基金项目:国家重点研发计划资助项目(2018YFB1308300)
作者简介:盛国良(1980-),男,实验师,主要研究方向:机械设备的电气控制,E-mail:shenggl@163.com; 通讯作者:陆宝春(1965-),男,博士,教授,主要研究方向:制造装备自动化与智能化、网络化控制与嵌入式系统,E-mail:lbcnust@sina.com。
引文格式:盛国良,翁朝阳,陆宝春. 基于改进型自适应强跟踪卡尔曼滤波的电池SOC估算[J]. 南京理工大学学报,2020,44(6):689-695.
投稿网址:http://zrxuebao.njust.edu.cn
更新日期/Last Update: 2020-12-30