基于滤波算法的锂电池SOC估算方法对比分析研究  

Lithium Battery SOC Estimation Method Based on Filter Algorithm Comparison Analysis Research

作　　者：张方亮[1] 

机构地区：[1]西南科技大学制造科学与工程学院,四川绵阳621010

出　　处：《电源世界》2016年第6期21-23,27,共4页The World of Power Supply

基　　金：四川省科技支撑计划(2014GZ0078)

摘　　要：电池荷电状态(State of Charge,SOC)作为电池管理系统中的重要参数之一,为保证电池管理系统的安全可靠和延长电池循环使用寿命,准确估算SOC具有重要意义。通过建立戴维宁(Thevenin)等效电路模型,结合卡尔曼滤波(KF)和扩展卡尔曼滤波(EKF)算法,实现对锂电池SOC估算精度进行对比研究。仿真结果表明,EKF算法仿真估算SOC精度明显高于KF的估算精度,估算精度可达2%。Battery state of charge（State of Charge, SOC） as one of the important parameters of a battery management system, in order to ensure the management system＇s safe and reliable and prolong the life cycle, accurately estimate the battery SOC is significant. By establishing Thevenin equivalent circuit model, combined with Kalman Filter（KF） and Extended Kalman Filter（EKF） to achieve comparative study on the accuracy of the lithium battery SOC estimation. Simulation results show that, EKF algorithm simulation to estimate the accuracy of SOC estimation accuracy significantly higher than the KF, and estimation accuracy up to 2%.

关 键 词：锂电池 戴维宁模型 扩展卡尔曼滤波 估算精度 

分 类 号：TM912.9[电气工程—电力电子与电力传动]