Battery Internal State Estimation: A Comparative Study of Non-Linear State Estimation Algorithms

Abstract

The tracking of the internal states of a battery such as the state-of-charge (SoC) is a substantive task in battery management systems. In general, batteries are represented as linear or non-linear mathematical models. The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are widely used for the non-linear battery state estimation but their efficiency is limited. Recently, more efficient non-linear state estimation methods such as the cubature Kalman filter (CKF) and the particle filters (PF) have been developed. In this paper, we compare the efficiency and the complexity of different non-linear battery internal state estimation methods based on the EKF, the UKF, the CKF, and the PF. In addition to the SoC, the transient response of the battery is also estimated. The experimental results show that the PF- and the CKF-based methods perform best. Under the chosen conditions, the PF-based method achieves the root mean square error of approximately 3% of the SoC. Although, the efficiency of the PF is slightly better than the CKF, it is computationally more complex.