Model-based state of charge estimation algorithms under various current patterns

Numerous model-based techniques have been proposed to estimate the state of charge (SOC) of lithium-ion batteries. In automotive applications, the algorithms are subjected to changing load profiles, requiring investigations into their general performance under various working conditions. In this study, three different load patterns derived from a customized dynamic driving profile, a standard driving cycle, and a constant discharge are used for the experimental verification. Four selected algorithms including the Ampere-hour counting, the extended Kalman filter, the particle filter, and the recursive least square filter are implemented. Their performance in terms of accuracy and robustness are compared. In addition, the load profile is analyzed in the frequency domain. The results show that the filter performance is dependent on the current patterns and can be correlated to the frequency spectrum of the load profile