基于自适应无迹卡尔曼滤波的锂电池SOC估计

doi:10.12028/j.issn.2095-4239.2019.0113

储能科学与技术 ›› 2019, Vol. 8 ›› Issue (5): 856-861.doi: 10.12028/j.issn.2095-4239.2019.0113

基于自适应无迹卡尔曼滤波的锂电池SOC估计

安治国, 田茂飞, 赵琳, 陈星, 李亚坤, 司鑫

重庆交通大学机电与车辆工程学院, 重庆 400074

收稿日期:2019-05-29 修回日期:2019-06-10 出版日期:2019-09-01 发布日期:2019-06-11
通讯作者: 安治国(1976-),男,副教授,硕士生导师,研究方向为新能源电动汽车,E-mail:1663550680@qq.com。
作者简介:安治国(1976-),男,副教授,硕士生导师,研究方向为新能源电动汽车,E-mail:1663550680@qq.com。

SOC estimation of lithium battery based on adaptive untracked Kalman filter

AN Zhiguo, TIAN Maofei, ZHAO Lin, CHEN Xing, LI Yakun, SI Xin

School of Mechatronics & Vehicle Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Received:2019-05-29 Revised:2019-06-10 Online:2019-09-01 Published:2019-06-11

摘要/Abstract

摘要： 电池荷电状态（SOC）的准确估计是电池管理系统的关键问题，对电池的可靠性和安全性至关重要。由于多数情况下建立的电池模型精度不够高、电池系统的噪声统计是未知的或不准确的，这都会对锂离子电池系统的SOC估计会产生较大影响。本文采用二阶RC等效模型，可减小电池模型带来的误差；同时结合SageHusa滤波算法与无迹卡尔曼滤波（UKF）算法提出了一种新的SOC估计方法，基于噪声统计估计器的自适应无迹卡尔曼（AUKF）滤波算法，它可以对系统噪声进行实时修正以提高SOC的估算精度。并通过比较AUKF和UKF来验证SOC估计方法的准确性和有效性。实验结果表明，AUKF具有更高的SOC估计精度和自适应能力，在脉冲放电工况和动态工况下的估计精度均能保持在4.68%以内，可以有效地估计电池的SOC值。

关键词: 荷电状态(SOC), 二阶RC模型, UKF, Sage-Husa滤波, AUKF

Abstract: The accuracy of the state of charge (SOC) estimation of battery is a key issue in battery management system, which is very important to the reliability and safety of battery. In most cases, the accuracy of the battery model established is not high enough and the noise statistics of the battery system are unknown or inaccurate, which will greatly influence the SOC estimation. In this paper, the second-order RC equivalent model is adopted to reduce the error caused by the battery model. At the same time, a new SOC estimation method based on Sage-Husa filtering algorithm and untracked Kalman filtering (UKF) algorithm is proposed, an adaptive no-trace Kalman (AUKF) filtering algorithm based on noise statistical estimator can modify the system noise in real time to improve the estimation accuracy of SOC. And the accuracy and validity of the SOC estimation method are verified by comparing AUKF and UKF. The experimental results show that AUKF has a higher SOC estimation accuracy and adaptive ability, and the estimated accuracy can be kept within 4.68% under both pulse discharge and dynamic conditions, which can effectively estimate the SOC value of the battery.

Key words: state of charge (SOC), second-order RC model, UKF, Sage-Husa filter, AUKF

中图分类号:

TM912

安治国, 田茂飞, 赵琳, 陈星, 李亚坤, 司鑫. 基于自适应无迹卡尔曼滤波的锂电池SOC估计[J]. 储能科学与技术, 2019, 8(5): 856-861.

AN Zhiguo, TIAN Maofei, ZHAO Lin, CHEN Xing, LI Yakun, SI Xin. SOC estimation of lithium battery based on adaptive untracked Kalman filter[J]. Energy Storage Science and Technology, 2019, 8(5): 856-861.

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基于自适应无迹卡尔曼滤波的锂电池SOC估计

SOC estimation of lithium battery based on adaptive untracked Kalman filter

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