State-of-Charge Estimation for Remaining Flying Time Prediction of Small UAV Using Adaptive Robust Extended Kalman Filter

This article proposes anapproach based on the adaptive robust extended Kalman filter (AREKF) suitable for estimating the state-of-charge (SoC) of small unmanned aerial vehicle (sUAV). The SoC of sUAV is a crucial factor directly affecting the remaining flying time (RFT). Existing methods for SoC estimation heavily rely on elaborate battery charge–discharge experiments conducted in complex environments, limiting their applicability to sUAV. This article combines the Shepherd battery model with AREKF to estimate the SoC of sUAV using a small amount of operational data. To verify the effectiveness of the proposed method, this article utilizes publicly available automotive data (Panasonic 18650PF Battery Data) and aviation data (NASA High-Intensity Radiated Field Battery Data). The adaptive extended Kalman filter (AEKF) serves as the control group for evaluating the performance of the SoC estimation. Ultimately, the data obtained from field flight tests are employed to evaluate the RFT predictions of AREKF and AEKF. The feasibility and performance of the proposed method are demonstrated through the offline test using numerical simulation. AREKF yields superior results with lower errors and variations in both SoC estimation and RFT prediction performance compared with AEKF.