基于加权多新息FRLS-AEKF的锂电池SOC估计  

Lithium-ion Battery State of Charge estimation based on Weighted Multiple Innovation FRLS-AEKF

作　　者：苏晓峰 时维国[1] SU Xiaofeng;SHI Weiguo(Dalian Jiaotong University,School of Automation and Electrical Engineering,Dalian 116021,China)

机构地区：[1]大连交通大学自动化与电气工程学院,大连116021

出　　处：《自动化与仪器仪表》2025年第3期1-6,共6页Automation & Instrumentation

基　　金：辽宁省教育厅科学研究项目(LJKMZ20220828,LJKZ0489)。

摘　　要：针对利用卡尔曼滤波算法(KF)估计锂离子电池的荷电状态(SOC)在复杂运行工况下估计精度差的问题,提出了一种加权多新息FRLS-AEKF算法估计SOC。采用加权多新息遗忘递推最小二乘法进行参数辨识,结合辨识出的参数采用自适应扩展卡尔曼滤波方法(AEKF)与极大似然估计方法相结合来估计SOC,达到提升SOC估计精度的目的。通过动态应力测试(DST)与联邦城市驾驶(FUDS)工况对算法进行验证,结果表明,加权多新息FRLS-AEKF算法误差均小于0.4%,解决了SOC估计精度差的问题。This paper proposes a Weighted Multiple Innovation FRLS-AEKF algorithm to estimate the State of Charge(SOC) of lithium-ion batteries,aiming to address the issue of reduced estimation accuracy under complex operating conditions when using the Kalman filter(KF) algorithm.The algorithm utilizes Weighted Multiple Innovation forgetting recursive least squares method for parameter identification,and combines the identified parameters with an adaptive extended Kalman filter method(AEKF) and maximum likelihood estimation method to estimate SOC,thereby improving the accuracy of SOC estimation.The algorithm is validated through Dynamic Stress Testing(DST) and Federal Urban Driving Schedule(FUDS) conditions,and the results indicate that the Weighted Multiple Innovation FRLS-AEKF algorithm had an error of less than 0.4%,solving the problem of poor SOC estimation accuracy.

关 键 词：锂离子电池 荷电状态 参数辨识 自适应扩展卡尔曼滤波 加权多新息遗忘递推最小二乘 

分 类 号：TM912[电气工程—电力电子与电力传动]