A robust state of charge estimation for multiple models of lead acid battery using adaptive extended Kalman filter

Abstract

An accurate estimation technique of the state of charge (SOC) of batteries is an essential task of the battery management system. The adaptive Kalman filter (AEKF) has been used as an obsever to investigate the SOC estimation effectiveness. Therefore, The SOC is a reflexion of the chemistry of the cell which it is the key parameter for the battery management system. It is very complex to monitor the SOC and control the internal states of the cell. Three battery models are proposed and their state space models have been established, their parameters were identified by applying the least square method. However, the SOC estimation accuracy of the battery depends on the model and the efficiency of the algorithm. In this paper, AEKF technique is presented to estimate the SOC of Lead acid battery. The experimental data is used to identify the parameters of the three models and used to build different open circuit voltage–state of charge (OCV-SOC) functions relationship. The results shows that the SOC estimation based-model which has been built by hight order RC model can effectively limit the error, hence guaranty the accuracy and robustness.