摘要
准确估计SOC在防止锂电池过度充放电、提高锂电池能量利用率以及保障电池管理系统安全稳定运行方面具有重要意义。本文以三元锂电池为研究对象,提出一种基于多新息辨识理论的SOC估计方法,通过建立二阶RC等效电路模型,采用遗忘因子多新息最小二乘法(FFMILS)对模型参数进行在线辨识,结合多新息无迹卡尔曼滤波(MIUKF)算法估计锂电池的SOC,通过UDDS实验验证,并和EKF、UKF及MIUKF算法进行对比,实验结果表明,FFMILS-MIUKF算法估计锂电池SOC的误差控制在1.08%左右,其具有高精确性和快速收敛性。
Accurate estimation of SOC plays an important role in preventing excessive charge and discharge of lithium batteries,improving energy utilization rate of lithium batteries and ensuring safe and stable operation of battery management system.In this paper,a SOC estimation method based on multi-innovation identification theory is proposed for ternary lithium batteries.Adopting forgetting factor multi-innovation least square method for model parameter online identification by building a second order RC equivalent circuit model,multi-information unscented Kalman Filter algorithm was used to estimate the SOC of lithium batteries.Through Verified by UDDS experiment and were compared with EKF,UKF and MIUKF algorithm,the results indicate that FFMILS-MIUKF algorithm to estimate the error of the SOC control at around 1.08%,which has high accuracy and fast convergence.
作者
邢丽坤
詹明睿
郭敏
伍龙
仇伟文
Xing Likun;Zhan Mingrui;Guo Min;Wu Long;Qiu Weiwen(School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 23200l,China;School of Mechanical and Electrical Engineering,Huainan Normal University,Huainan 232001,China)
出处
《电子测量技术》
北大核心
2022年第16期53-60,共8页
Electronic Measurement Technology
基金
安徽省高校自然科学基金重点项目(KJ2019A0106)
淮南市2021年重点研究与开发计划项目(2021A249)资助。
关键词
锂电池
多新息辨识理论
遗忘因子最小二乘法
无迹卡尔曼滤波
the lithium battery
multi-innovation identification method
forgetting factor least square method
unscented Kalman filter