基于改进卡尔曼滤波算法的磷酸铁锂电池SOC动态估计

SOC Dynamic Estimation of Lithium-iron Battery Based on Improved Kalman Filtering Algorithm

对锂电池荷电状态(SOC)进行快速精确地动态估计能有效提高其使用寿命。针对传统磷酸铁锂电池等效电路模型无法反映其对应的电气动态特性问题,提出了一种改进的戴维南电池模型。考虑到传统卡尔曼滤波算法在磷酸铁锂电池SOC动态估计过程中对模型依赖性较强的局限性,引进算法增益因子及修正观测噪声协方差,提出一种改进的卡尔曼滤波算法对磷酸铁锂电池SOC进行动态估计。仿真结果表明所提算法在锂电池SOC估计上具有很好的精度。

Fastly and accurately dynamic estimation of battery＇s stage of charge can effectively improve it＇s service life. According to the question of the traditional lithium iron battery equivalent circuit model can not react the corresponding dynamic characterislics, proposed an improved Thevenin battery model in this paper. Considering the strong limits of the traditional kalman filtering algorithm in the process of SOC dynamic estimation of lithium iron battery, introduced algorithm gain factor and correcting observation noise covariance ,and an impraved kalman filtering algorithm is proposed to estimate the lead-acid battery＇s SOC. Simulation results show that the proposed algorithm has better precision on battery SOC estimate.