基于联合扩展卡尔曼滤波法的锂电池SOC估算  

Lithium battery SOC estimation based on joint EKF

作　　者：于仲安[1] 简俊鹏 

机构地区：[1]江西理工大学电气工程与自动化学院,江西赣州341000

出　　处：《电源技术》2016年第10期1941-1942,1949,共3页Chinese Journal of Power Sources

基　　金：国家自然科学基金(51177066)

摘　　要：锂电池荷电状态(SOC)的精确估计一直是电池管理系统的核心任务之一。电流传感器中存在非零均值的电流漂移噪声,这些噪声会造成不可避免的估计误差。为减少电流漂移噪声对估算造成的不利影响,提出了联合扩展卡尔曼滤波法,以Thevenin模型为锂电池等效电路模型,将电流漂移值作为状态变量与电池SOC进行同步预测。实验和仿真结果表明,该方法能有效抑制电流漂移噪声,提高估算精度。Accurately estimating the state of charge of lithium battery is one of the core tasks of BMS. Nonzero mean current drift noise exists in the current sensor, which may inevitably lead to the estimation error. In order to suppress the drift current noise, a joint EKF algorithm based on the battery Thevenin model was proposed, and drift current noise was treated as SOC as the parameters of the model to be estimated. The simulation and test results show that this method can effectively suppress current drift noise, and increase the estimation accuracy.

关 键 词：锂电池 荷电状态 联合卡尔曼滤波 电流漂移噪声 

分 类 号：TM912.9[电气工程—电力电子与电力传动]