应用Kalman滤波法估计铅酸蓄电池SOC  

Kalman filter for estimating state-of-charge of VRLA batteries

作　　者：马文伟 郑建勇[1] 尤鋆[1] 张先飞[1] 

机构地区：[1]东南大学电气工程学院,江苏南京210096

出　　处：《蓄电池》2010年第1期19-23,共5页Chinese LABAT Man

基　　金：国家863计划资助项目(2008AA052421)

摘　　要：在蓄电池的充/放电过程中,正确估计其剩余容量,对充/放电控制器采取下一步的动作有着非常重要的指导作用。针对蓄电池充/放电的实际情况,在分析了传统SOC估计方法不足的基础上,引入Kalman滤波法作为蓄电池荷电状态(SOC)估计的主要算法。本文采用Randles等效电路模型,详细给出了Kalman滤波法估计SOC的算法推导,通过Matlab软件仿真验证了运用Kalman滤波法可以有效跟踪蓄电池SOC的变化,并且控制精度优于传统方法。During the charge or discharge of battery, accurate estimation of residucal capacity is very im- portant to the controller which will take the next action. In consideration of actual charge /discharge of lead-acid battery and deficiencies of traditional methods, a new approach based on the well-known Kalman Filter is introduced. This paper adopts Randles circuit, and describes in detail the deduction to estimate SOC of lead-aeid battery by using Kalman filtering algorithm. By using Matlab software, it can be proved that this method can track real-time prediction of SOC, which is superior to traditional measurements in terms of control accuracy.

关 键 词：铅酸蓄电池 荷电状态 卡尔曼滤波法 

分 类 号：TM912[电气工程—电力电子与电力传动]