基于EKF算法的铅酸蓄电池SOC在线估计  

Estimating SOC of lead-acid battery based on extended Kalman filtering

作　　者：高玉峰[1] 孙磊[1,2] 刘亚龙[1] 杨亚丽[1] 

机构地区：[1]装甲兵工程学院控制工程系 [2]73089部队

出　　处：《电源技术》2014年第2期303-306,共4页Chinese Journal of Power Sources

摘　　要：针对快速充电设备需要快速、准确检测蓄电池荷电状态(SOC)的应用需求,在分析传统SOC估计方法不足的基础上,采用了扩展卡尔曼滤波法进行铅酸蓄电池SOC的估计。通过对铅酸蓄电池充放电过程的分析,基于改进的Thevenin模型,建立了7-HK-182型铅酸蓄电池的等效电路模型。通过Matlab仿真,对比安时积分法估计SOC数据,验证了扩展卡尔曼滤波法能够实时、准确估计蓄电池SOC的变化。The demand of fast and accurate charging equipment＇s detecting SOC was considered. The traditional state of charge （SOC） estimating methods were analyzed. The extended Kalman filtering to estimate the SOC was taken. The process of charging and discharging was analyzed. The ameliorator Thevenin modeling was improved. The equivalent circuit model about the tape of 7-HK-182 battery was established. Through the Matlab, by contrast to the data of ampere hour measurement, the SOC change could be timely and exactly estimated using extended Kalman filtering.

关 键 词：铅酸蓄电池 荷电状态 扩展卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]