联合扩展卡尔曼滤波的滑模观测器SOC估算算法研究

文中提出一种联合扩展卡尔曼滤波的滑模观测器算法用于电动汽车电池的荷电状态(state of charge,SOC)估计。电池模型采用二阶Thevenin等效电路模型,辨识不同温度下的模型参数,分析温度对电池模型参数及精度的影响。针对扩展卡尔曼滤波对模型精度依赖性高及滑模观测器对噪声敏感导致估计结果存在较严重抖振现象的缺陷,提出在扩展卡尔曼滤波算法的状态修正方程中加入防抖函数,依据滑模观测器稳定性约束条件获取函数相关参数,得到一种新的联合扩展卡尔曼滤波的滑模观测器算法。所提算法能够同时综合扩展卡尔曼滤波器和滑模观测器优点,在滤除噪声的同时对建模误差也具有较强的鲁棒性。最后,设计相应的模拟工况进行实验,实验结果证明,所提算法在复杂的车载环境下拥有比扩展卡尔曼滤波和滑模观测器更高的电池SOC估计精度。

Research on the SOC Estimation Algorithm of Combining Sliding Mode Observer With Extended Kalman Filter

A sliding mode observer(SMO) algorithm based on joint Extend Kalman Filter(EKF) was proposed for state of charge(SOC) estimation of electric vehicle batteries. The second-order Thevenin equivalent circuit model was used to describe the characters of the battery, and its parameters at different temperatures were identified. Based on the results, the influence of temperature on the parameters and accuracy of the battery model were analyzed. Aiming at the high dependence of EKF on model accuracy and the serious chattering of estimation results caused by the sensitivity of SMO to noise, a new SOC estimation algorithm of combining sliding mode observer with EKF was proposed, in which a chattering reduction function is added to the state correction equation of EKF and the relevant parameters of the function are obtained according to the stability constraints of SMO. The proposed algorithm can synthesize the advantages of EKF and SMO at the same time, and has strong robustness to the modeling error while filtering noise. Finally, the corresponding simulation conditions were designed and a series of experiments have been carried out. The experimental results show that the proposed algorithm has higher estimation accuracy than both EKF and SMO in complex vehicle operating conditions.