Modeling, parameterization, and state of charge estimation of Li-Ion cells using a circuit model

Abstract

This paper presents a general procedure applied for modeling, parameter identification, and state of charge (SOC) estimation of a Li-Ion battery cell. The paper explains a battery tester with a number of experiments conducted to investigate the cell physical properties. Dynamics of the Li-Ion cell are modeled using an equivalent circuit model, whereas parameters of the model are calculated using particle swarm optimization. This method minimizes the output error that is the difference between the simulated output from the model and the measured terminal voltage. The provided equivalent circuit model with optimized parameters was used for SOC estimation. Two different state estimation methods have been applied to estimate the cell SOC based on real-time measurements. The estimation methods include the extended Kalman filter (EKF), and the novel smooth variable structure filter (SVSF). The SVSF method was used as it can produce more accurate state estimates for dynamic systems with modeling and parametric uncertainties. This paper compares the performance of these two estimators for real-time SOC estimation using tester data.