基于扩展卡尔曼滤波的LiFePO_4电池荷电状态估计  

State-of-charge estimation of LiFePO_4 battery using extended Kalman filter

作　　者：袁闪闪[1] 刘和平[1] 杨飞[1] 

机构地区：[1]重庆大学输配电装备及系统安全与新技术国家重点实验室,重庆400044

出　　处：《电源技术》2012年第3期325-327,共3页Chinese Journal of Power Sources

基　　金：中央高校基本科研业务费资助项目(CDJXS11151156)

摘　　要：为了准确估计电池的荷电状态(state-of-charge),在研究电池电化学阻抗谱模型的基础上得出了适合工程应用的电池简化等效电路模型,通过实验对模型进行了参数估计,并在模型的基础上采用扩展卡尔曼滤波法(EKF)对电池的SOC进行估计并使用Matlab进行了仿真验证,结果证明扩展卡尔曼滤波法能准确地估计电池的SOC。To accurately estimate the state-of-charge （SOC） of the battery, based on the study of electrochemical impedance spectroscopy model, a simple equivalent circuit model was gained, which was appropriate to real application. And then based on some experiments, the parameter of the equivalent circuit model was estimated. The extended Kalman filter was used to predict the SOC of battery. In the end, Matlab was used to verify this method, and the result show that the EKF can accurately estimate the SOC of battery.

关 键 词：荷电状态 等效电路模型 参数估计 扩展卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]