摘要
对锂电池荷电状态(state of charge,SOC)进行准确地估算十分重要。由于SOC呈非线性特征,并且受多种因素的动态影响,准确估计困难。本文利用高斯-厄米特滤波(Gauss-Hermite filter,GHF)的思想,结合Thevenin等效电路模型,提出一种自适应高斯-厄米特滤波(adaptive Gauss-Hermite filter,AGHF)算法对SOC实时估计更新。利用MATLAB/Simulink建立仿真模型,并与扩展卡尔曼滤波(extended Kalman filter,EKF)算法及传统的高斯-厄米特滤波算法相比较。通过分析对比可以发现该算法的估算精度较高,可以有效地控制滤波发散。
It is very important to estimate the state of charge(SOC)of a lithium battery accurately.SOC is dynamically affected by many factors.It is difficult to estimate accurately because of the nonlinear characteristics of SOC.An adaptive Gauss-Hermite filter(AGHF)algorithm is proposed to update the SOC real-time estimation,Using the idea of Gauss-Hermite filter(GHF)and Thevenin equivalent circuit model.The simulation analysis show that the algorithm has higher estimation accuracy and can effectively control the filter divergence,which compared with the extended Kalman filter and traditional Gauss-Hermite filter al⁃gorithm in MATLAB/Simulink environment.
作者
张凤博
孙桓五
杨淇
ZHANG Feng-bo;SUN Huan-wu;YANG Qi(Taiyuan University of Technology,College of Mechanical and Vehicle Engineering,Shanxi Taiyuan 030024,China)
出处
《机械设计与制造》
北大核心
2021年第11期147-150,共4页
Machinery Design & Manufacture
基金
山西省科技重大专项项目—重卡燃料电池动力系统及整车集成技术(20181102009)。
