Modeling and state of charge estimation of Lithium-ion Battery using the autoregressive exogenous model

Abstract

State of charge (SOC) estimation of lithium-ion batteries is the key technology of battery management system. In this paper, a method for estimating SOC of lithium-ion batteries based on ARX-AKF algorithm is proposed. The lithium-ion battery model adopts the autoregressive exogenous (ARX) model. The model order is determined by the genetic algorithm based on the Akaike's information criterion (AIC) and the model parameters are obtained by the recursive least squares, thereby solving the problem which is difficult to obtain the parameters of the equivalent circuit model accurately. Secondly, the adaptive Kalman filter (AKF) algorithm is used to estimate the SOC of the lithium-ion batteries based on the established ARX model. Finally, the performance of the algorithm is verified by the hybrid pulse power characteristic (HPPC) experiment. The experimental results show that the algorithm proposed in this paper has advantages of high precision, fast convergence, low computation cost and good practical value.