基于FFRLS-AEKF的动力锂电池SOC在线估计

针对锂电池等效电路模型参数不准确以及复杂工况噪声不确定导致荷电状态（SOC）估计精确度较低的问题，提出一种自适应扩展卡尔曼滤波（AEKF）融合带遗忘因子最小二乘法（FFRLS）的算法来解决此问题。在每一步SOC估计过程中，首先使用FFRLS算法跟随试验工况环境变化，实时辨识出一阶RC等效电路模型参数，增加模型精确度，准确描述锂电池工作时的动态特性；再使用AEKF算法实时更新与修正系统噪声并在线估计SOC。设计搭建动力锂电池试验平台，在动力动态测试（DST）和北京公交动力动态测试（BBDST）工况下，该方法估计值最大绝对误差均低于0.15%，平均绝对误差在0.077以下，均方根误差在0.007以下，相较于扩展卡尔曼滤波(EKF)算法，所提算法的估计效果有较大提升。

On-Line Estimation of SOC for Power Lithium Battery Based on

To address the problem of inaccurate parameters in equivalent circuit models of lithium batteries and uncertainty in complex operating noise leading to the low accuracy in state-of-charge (SOC) estimation. An adaptive extended Kalman filtering (AEKF) method incorporating recursive least squares with forgetting factor (FFRLS) is proposed. In each step of the SOC estimation process, the FFRLS algorithm is first used to identify the parameters of the first-order RC equivalent circuit model in real time following the changes of the experimental working environment, which increases the model accuracy and accurately describes the dynamic characteristics of the Li-ion battery when it is in operation. Then the AEKF algorithm is used to update and correct the system noise in real time and estimate the SOC on-line. The experimental platform for power lithium batteries was designed and constructed. Under both the dynamic stress test (DST) and the beijing bus dynamic stress test (BBDST) operating conditions, the maximum absolute error of the method''s estimation is lower than 0.15%, the average absolute error is below 0.077, and the root mean square error is below 0.007. Compared with the extended kalman filtering (EKF) method, the estimation effect of the proposed method is greatly improved.