摘要
锂离子电池最常用电路模型为二阶RC等效电路模型,其参数辨识存在所需辨识参数多的缺点,荷电状态(SOC)估计中状态方程存在复杂指数运算等问题。为此,设计了一种改进二阶电池模型,综合运用RLS参数辨识与线性卡尔曼滤波(KF)方法对电池实施SOC联合估计,有利于多个串联锂电池SOC的实时估计。确保系统满足SOC估计精度的条件下获得更快处理速率,再同时运用递推最小二乘法(RLS)与KF算法实现SOC估计的功能。研究结果表明:相对二阶RC模型,采用改进二阶模型辨识结果获得更小的MRE,获得更小整体误差,大幅缩短算法处理时间,具备更强整体性能。实施SOC联合估计时获得较精确的SOC估计结果,达到更快SOC估计速率,显著改善算法综合性能。该研究有助于提高锂电池的使用寿命,具有很高的使用价值。
The most commonly used circuit model for lithium-ion batteries is the second-order RC equivalent circuit model.Its parameter identification requires too many parametersand the state of charge(SOC)equation estimation involves complex exponential calculation.Therefore,an improved second-order battery model integrating the RLS parameter identification and linear KF method to implement joint SOC estimation is designed to facilitate real-time SOC estimation of multiple series lithium batteries.To ensure that the system meets the SOC estimation accuracy and is able to obtain faster processing rate,the RLS and KF algorithm are used to achieve SOC estimation.The results show that compared with the second-order RC model,the improved second-order model results in a smaller MRE,a smaller overall error,and significantly shortens the algorithm processing time,with stronger overall performance.When implementing SOC joint estimation,more accurate SOC estimation results are obtained,the SOC estimation rate is faster,and the comprehensive performance of the algorithm is significantly improved.This research helps improve the service life of lithium batteries.
作者
徐健
孙晓明
秦亮
常允艳
XU Jian;SUN Xiaoming;QIN Liang;CHANG Yunyan(College of Electric Power Engineering,Chongqing Water Resources and Electric Technology College,Chongqing 402160,China;School of Electrical Engineering,Wuhan University,Wuhan 430072,China)
出处
《机械设计与研究》
CSCD
北大核心
2024年第6期315-318,323,共5页
Machine Design And Research
基金
重庆市自然科学基金资助项目(cstc2021jcyj-msxmX1173)
重庆市教委科学技术研究项目(KJZD-K202203801)
重庆水利电力职业技术学院科研项目(K202023、K202026)。
关键词
锂电池
改进二阶模型
参数辨识
SOC估计
lithium battery
improved second-order model
parameter identification
SOC estimation
