State-of-charge estimation method for large unmanned aerial vehicle

Based on the extended Kalman filtering (EKF) algorithm, this chapter analyzes the causes of the algorithm and estimation errors and adopts corresponding countermeasures to solve them. This chapter uses the Sage-Husa adaptive filtering method to improve the impact of noise factors on the EKF algorithm. By adding an adaptive factor, the noise can be adjusted to reduce the impact of noise on the estimation accuracy. To address the drawback of the poor real-time performance of the EKF algorithm in iteratively computing the Jacobi matrix during the estimation process, the finite difference method is used to transform the Jacobi matrix during the estimation process of the algorithm, and the difference quotient is used instead of the microquotient to ensure the accuracy and reduce the computational complexity. A state-of-charge estimation algorithm based on the AFD-EKF algorithm is proposed to address the shortcomings of the EKF algorithm.