基于DKHF的船舶锂电池荷电状态估算

SOC Estimation of Marine Lithium Battery Based on DKHF

针对扩展卡尔曼滤波(EKF)在船舶锂电池荷电状态(SOC)估计线性化过程中产生的误差问题,结合无迹卡尔曼滤波(UKF)在非线性转换中能良好地近似系统概率分布的优点,基于电池系统的等效电路模型(ECM),提出双卡尔曼混合滤波器(DKHF)。建立船舶锂电池的ECM,并采用混合功率脉冲特性(HPPC)测试方法进行模型参数辨识,分析EKF算法估计误差较大、UKF算法计算较为复杂的问题,在此基础上提出DKHF。仿真结果表明,与EKF算法相比,DKHF能在恒流放电工况、短时大电流充放电工况和变电流工况下快速收敛于真实SOC值,同时提高估算精度,收敛后3种工况下的SOC值最大估计误差分别为1.5%、6.8%和1.6%,满足船舶运行中对电池SOC估算的要求。

In order to solve the error problem of Extended Kalman filter(EKF)in the linearization of SOC estimation of ship lithium battery,combined with the advantage that Unscented Kalman filter(UKF)can well approximate the probability distribution of system in nonlinear conversion,based on the equivalent circuit model(ECM)of battery system,a double Kalman hybrid filter(DKHF)is proposed.The ECM of ship lithium battery is established,and the HPPC test method is used to identify the model parameters.Large estimation error of EKF algorithm and complex calculation of UKF algorithm are analyzed,on the basis of which DKHF is proposed.The simulation results show that,compared with the EKF algorithm,DKHF can quickly converge to the real SOC value under constant current discharge condition,short-term high current charge-discharge condition,and variable current condition,and improve the estimation accuracy at the same time.The maximum estimation error of SOC under the three conditions after convergence is 1.5%,6.8% and 1.6% respectively,which meets the requirements of battery SOC estimation in ship operation.