基于在线参数辨识和AEKF的锂电池SOC估计

SOC estimation of lithium battery based online parameter identification and AEKF

SOC的准确估计对提高电池的动态性能和能量利用效率至关重要,估计过程中,模型参数不准确以及系统噪声的不确定性都会对结果产生较大影响。为减小模型参数辨识和系统噪声对SOC估计精度的影响,本文采用二阶RC等效电路模型,结合自适应扩展卡尔曼滤波算法(AEKF)进行锂电池的SOC估计。用带有遗忘因子的最小二乘法对模型参数进行在线辨识,以减小由参数辨识引起的估计误差,AEKF可以对系统和过程噪声进行修正,从而减小噪声对SOC估计的影响。最后分别用EKF和AEKF进行SOC估计并比较其误差,结果表明,AEKF联合最小二乘法参数在线辨识具有更高的精度和更好的适应性。

The accurate estimation of SOC is very important for improving the dynamic performance and energy utilization efficiency of batteries. In the estimation process, the inaccuracy of model parameters and the uncertainty of system noise will greatly affect the results. In order to reduce the influence of model parameter identification and system noise on the SOC estimation accuracy, this paper adopts the second-order RC equivalent circuit model combined with the adaptive extended kalman filter algorithm (AEKF) to estimate the SOC of lithium batteries. In order to reduce the estimation error caused by parameter identification, the least square method with forgetting factoris used to identify the model parameters online. AEKF can correct the system and process noise, so as to reduce the impact of noise on SOC estimation. At last, EKF and AEKF are used for SOC estimation respectively and their errors are compared. The results show that joint AEKF and least square parameter online identification has higher accuracy and better adaptability.