基于改进自适应卡尔曼滤波算法的锂离子电池荷电状态估计

State of charge estimation of lithium-ion batteries based on improved adaptive Kalman filter algorithm

针对锂离子电池荷电状态(state of charge,SOC)估计过程中传统卡尔曼滤波算法噪声特性难以确定、收敛速度慢及精度差等一系列问题,提出了一种改进自适应卡尔曼滤波算法。首先,建立了电池等效电路模型,并在不同温度和SOC状态下,对模型参数进行了辨识和精度验证。然后,对传统自适应卡尔曼滤波算法系统过程噪声协方差矩阵计算方式进行了正定性优化。此外,在状态估计结果的修正过程中,引入了对模型等误差变化进行补偿的增益因子。最后,通过实验电池的仿真和测试验证了所提算法的有效性。结果表明,在不同温度和工况条件下,SOC的估计误差均在4%以内,改进自适应卡尔曼滤波算法的估计精度和收敛速度均优于改进前的算法和常用的扩展卡尔曼滤波(extendedkalmanfilter,EKF)算法,具有较强的实用性。

An improved adaptive Kalman filter algorithm is proposed to tackle a series of issues in estimating the state of charge(SOC)for lithium-ion batteries,including the uncertainty of noise characteristics,sluggish convergence,and poor accuracy in traditional Kalman filter methods.First,an equivalent circuit model of batteries is established,with the model parameters identified and accuracy verified across various temperature and SOC states.Subsequently,positive definiteness optimization is conducted on the calculation method of the covariance matrix for process noise in the traditional adaptive Kalman filter algorithm.In addition,a gain factor is introduced to compensate for the the change of model error in correcting the state estimation results.Finally,the effectiveness of the the proposed algorithm is verified through simulation and testing of experimental batteries.The results indicate that the SOC estimation error is within 4%in different temperature and operating conditions.Compared with the algorithm before improvement and common extended Kalman filter(EKF)algorithm,the improved adaptive Kalman filter algorithm shows better estimation accuracy and convergence speed with relatively strong applicability.This work is supported by the National Key Research and Development Program of China(No.2021YFB3201305).