Modeling and state of charge estimation of a lithium ion battery using unscented Kalman filter in a nanosatellite

Abstract

State of charge (SOC) estimation is an essential part of battery management system. Dynamic and closed loop model-based methods such as extended Kalman filter (EKF) have been extensively used in SOC estimation. However, the EKF suffers from drawbacks such as requiring Jacobian matrix derivation and linearization accuracy. In this paper, a new SOC estimation method based on square root unscented Kalman filter (Sqrt-UKF) is proposed. With the proposed method, Jacobian matrix calculation is not needed and higher linearization order (2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order) can be achieved. The proposed approach has been validated with the experimental data and has been benchmarked with the Coulomb counting method in terms of accuracy and performance. The experimental results have shown that the proposed method has a mean error of 1.19% and a maximum error of 4.96% and has performed better than the Coulomb counting method.