基于模型参数辨识和扩展卡尔曼滤波的锂电池荷电状态估计

State of Charge Estimation of Lithium-ion Battery Based on Model Parameter Identification and Extended Kalman Filter

锂电池的荷电状态(SOC)估计作为电池管理系统的关键技术之一,直接影响到整个电池管理系统(BMS)的功能和效率。针对一般的简单多项式拟合SOC-OCV曲线提出了改进,采用了分段拟合的方式,能够降低拟合公式的阶数,提高拟合精度。结合戴维南(Thevenin)等效电路,以及充放电实验和混合脉冲功率特性(HPPC)实验进行参数辨识,得到状态方程和观测方程,采用扩展卡尔曼滤波算法(EKF)对锂电池的不同工况进行SOC估计。仿真结果表明,分段拟合SOC-OCV曲线能有效提高SOC估计精度,减少计算量。

State of Charge(SOC)estimation of lithium-ion battery is one of the key technologies of battery management system,which directly affects the function and efficiency of the whole battery management system(BMS).In this paper,the general simple polynomial fitting SOC-OCV curve is improved.The piecewise fitting method is adopted,which can reduce the order of fitting formula and improve the fitting accuracy.Based on Thevenin equivalent circuit,charge-discharge experiment and hybrid pulse power characteristic(HPPC)experiment,the state equation and observation equation are obtained.The extended Kalman filter(EKF)is used to estimate SOC of lithium battery under different operating conditions.