AH-IUKF融合算法下Ni-MH动力电池的SOC估计

SOC Estimation of Ni-MH Power Battery Using AH-IUKF Fusion Algorithms

针对Ni-MH动力电池系统非线性的特点,提出一种Thevenin电路改进后的状态模型.根据动力池电流变化显著的特征,采用融合改进后UKF(IUKF)算法和安时(AH)算法的AH-IUKF融合算法,对动力电池荷电状态(SOC)进行估计,并对AH-IUKF融合算法在SOC预测中的收敛速度、估计精度和复杂度进行分析和比较.结果表明:AH-IUKF融合算法不仅复杂度低、精度高,而且能实现Ni-MH动力电池SOC的快速估计,在各种工况下估计误差可平稳在1%~3%范围内,解决了动力电池SOC实时在线估计误差较大和计算复杂的问题.

For the non-linear characteristics of Ni-MH power battery system,an improved state model of Thevenin circuit is proposed.According to the characteristics of significant changes in power pool current,the AH-IUKF fusion algorithm of improved unscented kalmanfilter(IUKF) algorithm and amperohour(AH) algorithm are used to estimate the power battery state of change(SOC) in the power battery.The convergence speed,estimation accuracy and complexity of AH-IUKF fusion algorithm in SOC prediction are analyzed and compared.The results show that the AH-IUKF fusion algorithm not only has low complexity and high precision,but also can realize Ni-MH power battery SOC rapid estimation.The estimation error can be stable in the range of 1%-3% under various operating conditions,which solves the problems of larger estimation error and complex calculation under real-time online condiction of power battery SOC.