[1]孙叶宁,魏艳君,赵 勇,等.基于改进粒子滤波的LiFePO4电池二元SOC估算[J].燕山大学学报,2019,43(6):511-517.[doi:10.3969/j.issn.1007-791X.2019.06.006]
 SUN Yening,WEI Yanjun,ZHAO Yong,et al.Dual SOC estimation for LiFePO4 battery based on improved particle filter[J].Journal of YanShan University,2019,43(6):511-517.[doi:10.3969/j.issn.1007-791X.2019.06.006]
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基于改进粒子滤波的LiFePO4电池二元SOC估算
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《燕山大学学报》[ISSN:1007-791X/CN:13-1219/N]

卷:
43
期数:
2019年第6期
页码:
511-517
栏目:
电气工程
出版日期:
2019-11-30

文章信息/Info

Title:
Dual SOC estimation for LiFePO4 battery based on improved particle filter
文章编号:
1007-791X(2019)06-0511-07
作者:
孙叶宁1魏艳君1赵 勇1漆汉宏1*陈洪涛2
1.燕山大学 河北省电力电子节能与传动控制重点实验室,河北 秦皇岛 066004;
2.国网吉林省电力有限公司松原供电公司,吉林 松原 138000
Author(s):
SUN Yening1WEI Yanjun1ZHAO Yong1QI Hanhong1CHEN Hongtao2
1.School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China;
2.Songyuan Power Supply Company, Jilin Electric Power Company, Songyuan, Jilin 138000, China
关键词:
LiFePO4电池二元SOC估算改进粒子滤波粒子退化与贫化
Keywords:
LiFePO4 battery dual SOC estimation improved particle filter particle degradation and depletion
分类号:
TM911
DOI:
10.3969/j.issn.1007-791X.2019.06.006
文献标志码:
A
摘要:
准确估算电池荷电状态是电池管理系统的核心技术之一。为提高估算电池荷电状态精度,构建了可描述蓄电池倍率容量特性的二元荷电状态模型,并采用一种改进的粒子滤波算法对LiFePO4电池进行荷电状态估算。从标准粒子滤波结构入手,先引入残差重采样算法,缓解了传统序贯重要性采样的粒子退化问题;而后在重采样过程中,采用Thompson-Taylor算法对粒子进行随机线性组合,并生成新粒子,可以抑制标准粒子滤波算法执行过程中的粒子贫化问题。基于这种改进的粒子滤波算法实现了对LiFePO4电池二元荷电状态估算。实验结果表明,改进的粒子滤波算法相比无迹卡尔曼滤波算法,估算电池荷电状态具有更高的精度,估算误差不超过±0.2%。研究结果对电池管理系统估算电池荷电状态具有现实指导意义。
Abstract:
Accurately estimating the state of charge (SOC) is one of the core technologies of the battery management system. In order to improve the accuracy of battery charge state estimation, dual SOC model which can describe the characteristics of battery capacity is constructed, and an improved particle filter algorithm is used to estimate the SOC of LiFePO4 battery. Starting with the standard particle filter structure, residual resampling algorithm is introduced, the degradation problem of traditional sequential importance sampling is alleviated, and then in the resampling process, Thompson-Taylor algorithm is adopted to carry out random linear combination of particles and generate new particles. It can suppress particle dilution in standard PF process. Based on the improved particle filter algorithm, the dual SOC estimation of LiFePO4 battery is realized. The experimental results show that the improved particle filter algorithm has higher accuracy than the traditional extended Kalman filter algorithm for estimating battery SOC, and the estimation error does not exceed ±0.2%. The research results have practical significance for estimating SOC of battery management system.

备注/Memo

备注/Memo:
收稿日期:2018-09-18        责任编辑:温茂森
基金项目:河北省教育厅高校科技计划青年基金项目(QN2018134);河北省教育厅高校科技计划重点项目(ZD2017081)
作者简介:孙叶宁(1983-),女,河北石家庄人,博士研究生,主要研究方向为电池管理系统;*通信作者:漆汉宏(1968-),男,江西萍乡人,博士,教授,博士生导师,主要研究方向为新能源发电、并网及储能技术,Email:hhqi@ysu.edu.cn
更新日期/Last Update: 2020-01-03