基于扩展卡尔曼滤波算法的新型液态金属电池的荷电状态在线估算

On-line Estimation of State of Charge Estimation for Liquid Metal Battery Based on Extended Kalman Filter Algorithm

液态金属电池是一种满足电网大规模储能要求的新型的大容量储能电池,其荷电状态的准确在线估算是其投入规模应用的重要基础,但是目前没有公开发表的针对在线估算此电池荷电状态的文献。以此为目的建立了基于扩展卡尔曼滤波器的电池荷电状态在线估算方法。建立了考虑电池自放电因素的等效物理模型。在此基础上,提出一种基于扩展卡尔曼滤波器的电池荷电状态在线估算方法。实验结果表明,在不同的电池工况下和电池荷电状态估算初值设置不同的情况,电池荷电状态的估算值都具有较高的精度。估算值和测量值之间的标准误差和均值误差分别为0.018 3和0.014 5。该算法可以有效地解决电池初始参数对SOC估算值的影响。

The liquid metal battery is a new type of large capacity energy storage battery meeting requirements of large scale energy storage of power grid and the accurate on line estimation of its charge state is an important foundation for its large scale application.However,there is presently no published literature for on-line estimating the state of the battery.For this purpose,the on-line estimation method of the charge state of the battery is established based on extended Kalman filter algorithm.The equivalent physical model considering the self-discharge factor of the battery is established and,on this basis,the on-line estimation method of charge state based on extended Kalman filter is proposed.It is shown by the experimental result that under different battery working conditions and different initial value of the state of charge,the estimation of the state of charge of the battery has good accuracy.The standard error between the calculated and measured value is about 0.018 3 and the average error between them is about 0.014 5.This algorithm can effectively solve the problem caused by inaccurate estimation of initial battery parameters.