基于高阶非线性模型的铅酸蓄电池SOC估计

SOC estimation of lead-acid battery based on high-order-nonlinear fitting model

铅酸蓄电池在电动汽车和蓄电池储能系统等领域有着广泛的应用,提高铅酸蓄电池荷电状态(SOC)估算的精度具有重要的意义。本文针对目前SOC估算方法中电池等效模型复杂、相关参数难以确定等问题,提出了一种新型高阶非线性拟合开路电压的SOC估计方法,通过拟合恒流充放电工况下的开路电压(OCV)–SOC曲线,建立适用于变电流充放电工况下的铅酸蓄电池模型,并结合扩展卡尔曼滤波算法(EKF)对电池的SOC进行估算。仿真和实验结果表明该方法能够实现铅酸蓄电池的高精度SOC估算。

Lead-acid batteries have been widely used in the fields of electric vehicles and battery energy storage systems. It is significant to improve the estimation accuracy of state of charge（SOC） of them. The SOC estimation methods based on the current battery equivalent model are complex and difficult to determine the relevant parameters and other issues. This paper proposed a new high-order nonlinear fitting open circuit voltage SOC estimation method. An alternating current charge-discharge lead-acid battery model which was fitted for a constant current charge and discharge condition of the open circuit voltage（OCV）–SOC curve had been established. The SOC was estimated by combining with EKF algorithm. Simulation and experimental results showed that the proposed method did well in high precision lead-acid battery SOC estimates.