滤波技术在蓄电池SOC估计中的应用  

SOC Estimation for Battery Using Kalman Filter Technology

作　　者：邓正宏[1] 周岩[1] 高向阳[1] 

机构地区：[1]西北工业大学自动化学院,陕西西安710072

出　　处：《西北工业大学学报》2013年第2期179-182,共4页Journal of Northwestern Polytechnical University

摘　　要：文章针对蓄电池SOC估计精度不高且实时显示效果不佳问题,提出了一种基于卡尔曼滤波技术的SOC估计方法。首先建立了蓄电池等效电路模型,该模型具有建模蓄电池容量和伏安特性功能,其次基于等效电路模型建立了蓄电池状态方程,选取SOC作为内部状态变量,通过测量终端电压和滤波技术求出SOC的最真递归解。最后选择蓄电池的真实放电数据对该方法进行验证,结果表明该方法具有估计精度高,实时显示效果好的优点。Our brief review of existing State of Charge （SOC） estimation methods at the start of our paper reveals that the main questions for SOC estimation are low precision and poor real-time display. In our opinion, the reason for these questions is that no feedback information is used. The purpose of Kalman filter is to provide the closed form recursive solution for the estimation of linear discrete-time dynamic systems, which uses measured data to up- date the current state so that Kalman filter can be used for battery SOC estimation. We now present our research resuits. First, equivalent circuit model for battery is established, which can model battery capacity and characterize I-V performance. Secondly, state equations for battery are established based on equivalent circuit model and SOC is one of the state variables. The closed form recursive solution for the estimation of the SOC is presented using Kalman filter and measured terminal voltage data of Ref. 6. Finally, the method proposed in this paper is verified using the real battery discharge data. Simulation results show preliminarily that this method has high precision and good real-time display.

关 键 词：蓄电池 荷电状态 等效电路模型 滤波技术 

分 类 号：TM912[电气工程—电力电子与电力传动]