State-of-Charge Estimation of the Lithium-Ion Battery Using Neural Network Based on an Improved Thevenin Circuit Model

This paper focuses on real-time estimation of State of Charge (SOC) in Lithium-Ion battery. Because of the highly complex electrochemical reaction inside the battery the conventional first order battery model is not accurate and cannot respond to the battery's conditions correctly because of the simplicity of the model. So, the neural network (NN) is selected to estimate the SOC dynamically due to its strong nonlinear fitting ability. The NN strategy also was used to implement the parameter identification for the battery model.