基于自适应CKF的老化锂电池SOC估计

doi:10.19799/j.cnki.2095-4239.2020.0005

储能科学与技术 ›› 2020, Vol. 9 ›› Issue (4): 1193-1199.doi: 10.19799/j.cnki.2095-4239.2020.0005

基于自适应CKF的老化锂电池SOC估计

郑涛(), 张里, 侯杨成, 陈薇

合肥工业大学电气与自动化工程学院，安徽合肥 234000

收稿日期:2020-01-05 修回日期:2020-02-27 出版日期:2020-07-05 发布日期:2020-06-30
作者简介:郑涛（1981—），男，副研究员，研究方向为先进工业控制与优化，E-mial：zhengtao201007@163.com。
基金资助:
国家重点研发计划项目(2017YFB0903502);2017年安徽省科技重大专项(17030701041);安徽高校协同创新项目(PA2019AGXC0126)

SOC estimation of aging lithium battery based on adaptive CKF

ZHENG"Tao(), ZHANG"Li, HOU"Yangcheng, CHEN"Wei

School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 234000, Anhui, China

Received:2020-01-05 Revised:2020-02-27 Online:2020-07-05 Published:2020-06-30

摘要/Abstract

摘要：

锂电池的荷电状态(SOC)估算是电动汽车的系统管理与能量控制的重要参数。在SOC估算过程中，电池参数变化和老化问题会对结果造成很大影响。针对这一问题，在递推最小二乘法算法(RLS)辨识锂电池模型的参数的基础上更新电池容量,通过容积卡尔曼滤波(CKF)估算电池SOC，结合RLS和CKF实现在电池参数发生变化时准确估计SOC。以锂离子电池作为对象，应用所提出的算法实现锂电池的SOC在线估计，验证算法的准确性。

关键词: 锂电池, 荷电状态（SOC）, 老化, CKF

Abstract:

The state of charge (SOC) of a lithium battery is an important parameter associated with electric vehicle system management and energy control. During SOC estimation, the changes in battery parameters and aging problems will considerably affect the results. Therefore, the recursive least square (RLS) algorithm is used to identify the parameters of the lithium battery model and update the battery capacity. The SOC of the lithium battery can be estimated based on the cubature Kalman filter (CKF). Further, the SOC can be accurately estimated by combining RLS and CKF even though this causes the battery parameters to changes. The accuracy of the proposed algorithm can be verified by estimating the SOC of the lithium iron phosphate battery online.

Key words: lithium battery, state of charge (SOC), ageing, CKF

中图分类号:

TM 912

郑涛, 张里, 侯杨成, 陈薇. 基于自适应CKF的老化锂电池SOC估计[J]. 储能科学与技术, 2020, 9(4): 1193-1199.

ZHENG Tao, ZHANG Li, HOU Yangcheng, CHEN Wei. SOC estimation of aging lithium battery based on adaptive CKF[J]. Energy Storage Science and Technology, 2020, 9(4): 1193-1199.

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估计方法	平均误差/%	最大误差/%	执行时间/s
CKF	0.68	0.92	0.472
UKF	0.87	1.37	1.024
EKF	0.96	1.48	0.387

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基于自适应CKF的老化锂电池SOC估计

SOC estimation of aging lithium battery based on adaptive CKF

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