基于自适应拓展卡尔曼滤波的锂电池荷电状态估计

SOC estimation for lithium battery based on adaptive extended Kalman filter

荷电状态(State of Charge,SOC)是评价电池性能的重要指标。准确估计SOC,对于最大化电池容量和性能至关重要。目前,测量SOC的方法较多,同时也较为成熟,但是寻求更为有效与准确的估测方法还存在研究探索空间。提出了一种SOC估计方法,将带有遗忘因子的递归最小二乘(FFRLS)参数识别算法与基于自适应扩展卡尔曼滤波器(AEKF)的在线SOC估计方法相结合。基于二阶RC等效电路模型,采用FFRLS算法实时识别电池参数。利用识别出的参数,AEKF算法动态调整系统噪声参数,以获得更精确的SOC估计结果。所提出的SOC估计方法通过HPPC和UDDS工况验证,得出算法误差约为2%,证明了所提出方法的准确性和鲁棒性。

State of Charge(SOC)is a crucial indicator for evaluating battery performance.Accurate estimation of SOC is essential for maximizing battery capacity and performance.Currently,there are many methods for measuring SOC,and they are also relatively mature.However,there is still room for research and exploration to seek more effective and accurate estimation methods.This article proposes an approach for SOC estimation,combining a recursive least squares(FFRLS)parameter identification algorithm with a forgetting factor and an online SOC estimation method based on adaptive extended Kalman filter(AEKF).The FFRLS algorithm is employed to identify battery parameters in real-time,based on the second-order RC equivalent circuit model.Using the identified parameters,the AEKF algorithm dynamically adjusts the system noise parameters to achieve more precise SOC estimation results.The proposed SOC estimation method is validated through verification under HPPC and UDDS operating conditions,with an algorithm error of approximately 2%,proving the accuracy and robustness of the proposed method.