State-of-charge estimation of lithium-ion battery using square root spherical unscented kalman filter (Sqrt-UKFST) in nanosatellite

The extended Kalman filter (EKF) has been widely used for attitude determination in various satellite missions. However, it requires an extensive computational power that is not suitable for nanosatellite application. This paper proposes a gain-scheduled EKF (GSEKF) to reduce the computational requirement in the nanosatellite attitude determination process. The proposed GSEKF eliminates the online recursive Kalman gain computation by analytically determining the Kalman gain based on the sensor parameters, such as the gyroscope noise variance, the quaternion variance, the observation matrix, and the satellite rotational speed. Two GSEKF Kalman gains for two satellite operating modes are presented: the Sun-pointing and nadir-pointing modes. The simulation and experimental results show that the proposed method has comparable attitude estimation accuracy to the conventional EKF. In addition, the proposed GSEKF reduces 86.29% and 89.45% of the computation load compared with the multiplicative EKF and Murrell's version.

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