Evaluation of the model-based state-of-charge estimation methods for lithium-ion batteries

Abstract

To achieve accurate battery SoC, the Gaussian is applied to construct battery model. It is able to simulate the time-variable, nonlinear characteristics of battery. To adaptively adjust the Gaussian battery model parameter set and order, a novel online four-step model parameter identification and order selection method is proposed. To further evaluate the Gaussian battery model estimation accuracy, another two kinds of representative battery models including the combined model and Thevenin model are built as comparisons. Results based on three kinds of Kalman filters show that the maximum SoC estimation error of each case is within 2% and the Gaussian model has the best accuracy for voltage prediction as well as SoC estimation.