动力电池的荷电状态(state of charge,SOC)是预估电动汽车剩余有效行驶里程的重要参数之一。为提高锂电池SOC的估算精度,考虑了温度对锂电池特性的影响。通过实验得到温度对电池容量的关系曲线,以及得到OCV-SOC-T的函数映射关系,基于二...动力电池的荷电状态(state of charge,SOC)是预估电动汽车剩余有效行驶里程的重要参数之一。为提高锂电池SOC的估算精度,考虑了温度对锂电池特性的影响。通过实验得到温度对电池容量的关系曲线,以及得到OCV-SOC-T的函数映射关系,基于二阶RC等效电路模型,利用带遗忘因子递推最小二乘法(forgetting factor recursive least square,FFRLS)对模型进行实时在线参数辨识。在不同温度和工况条件下,采用扩展卡尔曼滤波(extended Kalman filter,EKF)和无迹卡尔曼滤波(unscented Kalman filter,UKF)算法对锂电池的SOC进行估算并对比验证,结果表明,EKF在动态压力测试工况(DST)和美国联邦城市运行工况(FUDS)的均方根误差分别在4.93%和4.69%以内,UKF在DST和FUDS工况下的均方根误差分别在1.47%和1.49%以内。研究结果表明,FFRLS联合EKF和UKF都可以实时估算SOC,且在不同温度和不同工况条件下,UKF算法相较于EKF算法,抗干扰能力更强,估算精度更高,收敛性更好。展开更多
When a pico satellite is under normal operational condi- tions, whether it is extended or unscented, a conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliab...When a pico satellite is under normal operational condi- tions, whether it is extended or unscented, a conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunc- tions in the estimation system, the Kalman filter gives inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms, robust extended Kalman filter （REKF） and robust unscented Kalman filter （RUKF）, for the case of measurement malfunctions. In both filters, by the use of de- fined variables named as the measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight, and the estimations are corrected without affecting the characteristic of the accurate ones. The proposed robust Kalman filters are applied for the attitude estimation process of a pico satel- lite, and the results are compared.展开更多
文摘When a pico satellite is under normal operational condi- tions, whether it is extended or unscented, a conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunc- tions in the estimation system, the Kalman filter gives inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms, robust extended Kalman filter （REKF） and robust unscented Kalman filter （RUKF）, for the case of measurement malfunctions. In both filters, by the use of de- fined variables named as the measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight, and the estimations are corrected without affecting the characteristic of the accurate ones. The proposed robust Kalman filters are applied for the attitude estimation process of a pico satel- lite, and the results are compared.
