基于无迹卡尔曼滤波法的串联型电池系统 荷电状态估计研究

以锂离子电池为载体的电源系统为航天器稳定可靠运行提供了一种有效的方式.多个电池单体经串联可扩大电池系统容量,即串联型电池系统.为准确估计串联型锂离子电池系统的荷电状态(State of Charge,SOC),针对扩展卡尔曼滤波(Extended Kalman Filter,EKF)计算复杂、精度不高等问题,结合串联型电池系统空间状态方程,提出基于无迹卡尔曼滤波法(Unscented Kalman Filter,UKF)的串联型电池系统荷电状态估计算法.在恒流和脉冲两种工况下,通过对比分析UKF与EKF算法的仿真结果与实验数据的匹配情况,证明了提出算法的准确性和高鲁棒性.

Research on State of Charge Estimation of Parallel-connection Battery System Based on Unscented Kalman Filter

As a valid alternative, power system with Li-ion battery is an effective method to maintain the spacecraft stable and reliable operation.Series-connected battery system (SBS) will supply high capacity when cells are connected in series. To estimate accurately state of charge(SOC)of SBS consists of many of Li-ion cells, and to solve some problems in SOC estimation such as complicated computation in extended Kalman filter (EKF), a method based on unscented Kalman filter (UKF) is presented to estimate SOC of SBS. The SBS space state equations is built also. The effectiveness and robustness of the method is verified by the comparison between UKF and EKF with simulation and experiment results under constant current and pulse current conditions