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[1]程晨,顾伟,褚建新,等.基于Sage-Husa自适应滤波算法的锂电池荷电状态估计[J].南京工业大学学报(自然科学版),2016,38(03):126-130.[doi:10.3969/j.issn.1671-7627.2016.03.021]
 CHENG Chen,GU Wei,CHU Jianxin,et al.State of charge estimation of lithium batteries based on Sage-Husa adaptive filter algorithm[J].Journal of NANJING TECH UNIVERSITY(NATURAL SCIENCE EDITION),2016,38(03):126-130.[doi:10.3969/j.issn.1671-7627.2016.03.021]
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基于Sage-Husa自适应滤波算法的锂电池荷电状态估计()
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《南京工业大学学报(自然科学版)》[ISSN:1671-7627/CN:32-1670/N]

卷:
38
期数:
2016年03期
页码:
126-130
栏目:
出版日期:
2016-05-28

文章信息/Info

Title:
State of charge estimation of lithium batteries based on Sage-Husa adaptive filter algorithm
文章编号:
1671-7627(2016)03-0126-05
作者:
程晨顾伟褚建新高迪驹
上海海事大学 科学研究院,上海 201306
Author(s):
CHENG ChenGU WeiCHU JianxinGAO Diju
Scientific Research Academy,Shanghai Maritime University,Shanghai 201306,China
关键词:
扩展卡尔曼滤波 Sage-Husa自适应滤波 锂电池 荷电状态
Keywords:
extended Kalman filter Sage-Husa adaptive filter lithium batteries state of charge
分类号:
TP29
DOI:
10.3969/j.issn.1671-7627.2016.03.021
文献标志码:
A
摘要:
当噪声的统计特性未知时,基于锂电池的戴维宁等效电路模型并使用扩展卡尔曼滤波(EKF)方法进行在线估算锂电池的荷电状态,会导致精确度迅速下降,容易发散。本文使用Sage-Husa自适应滤波算法来代替扩展卡尔曼滤波算法,并利用Matlab进行建模仿真比较。仿真结果误差大幅度减小,曲线趋于平滑。结果表明,在干扰噪声未知的环境下,基于Sage-Husa自适应滤波的荷电状态估计可以提高荷电状态在线估计的精度。
Abstract:
When the statistical characteristics of noise is unknown, the state of charge of lithium batteries in real time can be estimated by the extended Kalman filter(EKF),based on equivalent circuit of Thevenin model, thus the accuracy is decreased rapidly and dispersed easily. Instead of the traditional EKF, Sage-Husa adaptive filter algorithm was used to study the problem, and the data from experiments were modeled and simulated with Matlab software. The simulation results showed that the error was greatly reduced and the curve was smooth. The Sage-Huasa adaptive filter algorithim could improve the real-time estimation of charge state.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-02-23
基金项目:国家自然科学基金(61304186)
作者简介:程晨(1993—),女,安徽池州人,硕士,主要研究方向为锂电池检测; 顾伟(联系人),教授,E-mail: weigu@shmtu.edu.cn.
引用本文:程晨,顾伟,褚建新,等.基于Sage-Husa自适应滤波算法的锂电池荷电状态估计[J].南京工业大学学报(自然科学版),2016,38(3):126-130..
更新日期/Last Update: 2016-05-20