基于模型参数在线辨识的蓄电池SOC估算

Battery SOC Estimation Based on Online Parameter Identification

以铅酸蓄电池为研究对象,通过构造一种综合型策略来实现对储能蓄电池荷电状态(SOC)的实时准确估算。本方法首先引入递推最小二乘(RLS)算法实现对蓄电池模型参数的在线辨识,进而将该参数辨识策略与无迹卡尔曼滤波(UKF)算法相结合,这样一来,在采用UKF对SOC进行估算时所用模型参数为在线辨识的结果,本研究将此两种算法巧妙地结合起来实现了对蓄电池SOC的实时准确估算。由于在线模型参数辨识的实现,使得该策略具有较强的自适应性,故此可称之为自适应SOC估算技术。仿真结果表明,该方法可实现对蓄电池SOC的准确估算。

With the research object of lead-acid battery, this paper aims to correctly estimate the battery state of charge(SOC) by constructing a comprehensive SOC estimation strategy. Firstly, recursive least square(RLS) algorithm is adopted to realize online parameter identification of the equivalent battery model; and then an elaborate combination of RLS and Unscented Kalman Filter(UKF) is established, thus the battery model parameters used in UKF are actually obtained recursively by RLS; finally, SOC can be estimated by UKF. This strategy has an obvious adaptability due to the adoption of online parameter identification, so it is also called adaptive SOC estimation technique. Simulation results show that battery SOC can be correctly estimated by using this strategy.