[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 Guoliang1; Weng Chaoyang2; Lu 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:

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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