An online state of charge estimation method with reduced prior battery testing information

An online State of Charge (SOC) estimation method with reduced prior battery testing information is proposed in this paper, in which no testing data obtained in laboratory is needed, including the relationship between the open circuit voltage (OCV) and the SOC. The first order RC battery model is utilized to interpret the characteristics of the lithium-ion battery. The genetic algorithm is introduced to carry out the online identification for the battery model. Parameters obtained by the identification are applied to the joint SOC estimation method to estimate the SOC of the battery. An experimental battery test workbench is established to validate the proposed method. Several drive cycle current profiles are scaled down and applied to the battery. The experiment results show that the parameters obtained by the proposed method could characterize the battery well, even for different drive cycles, and accurate SOC of the battery could be obtained online.