Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion Battery Using Dual Neural Network Fusion Battery Model

Abstract

ABSTRACT The OCV (open circuit voltage)-based method for SOC (state of charge) estimation by using the dual neural network fusion battery model is proposed in this paper. The weights of the constructed dual neural network fusion battery model can be used to describe the characteristics of the corresponding parameters of electrochemical model for the battery. The constructed dual neural network fusion battery model consists of two neural network models connected in series. The first part is a linear neural network battery model which can be used to identify parameters of the first-order electrochemical model or second-order electrochemical model for the battery, the second part is a BP (Back of Prorogation) neural network used for capturing the relationship between OCV and SOC. The DST (Dynamic Stress Test) data is adopted for training the dual neural network fusion battery model, by which the relationship between OCV and SOC is offline obtained. Under FUDS (Federal Urban Driving Schedule) condition, the experimental results show that the dual neural network fusion battery model can effectively estimate SOC based on the first-order electrochemical model or second-order electrochemical model.