Adaptive Kalman filter state-of-charge design for unmanned aerial vehicle battery monitoring based on altitude and current measurement
Suryadi Suryadi, Bernadus Herdi Sirenden, Edi Kurniawan, Hendra Adinanta, Jalu Ahmad Prakosa, Purwowibowo, Rini Khamimatul Ula, Hari Pratomo, Imam Affandi · AIP conference proceedings · 2022
A battery management system is essential for unmanned aerial vehicles (UAVs) to determine the ability to return home or continue the flight mission. State-of-charge (SoC) is the primary parameter for battery management which is traditionally estimated using Kalman filter based on current and voltage measurement of battery. This paper estimate the SoC of UAV batteries using adaptive Kalman filter based on current and altitude measurements. We also compare two adaptive Kalman filter methods, namely Werries-Dollan and Sage-Husa. Root-mean-square error is used as a comparison parameter. The simulation results show Werries-Dollan is better than Sage-Husa.