Design of a Non-Linear Observer for SOC of Lithium-Ion Battery Based on Neural Network

Abstract

This paper presents a method for use in estimating the state of charge (SOC) of lithium-ion batteries which is based on an electrochemical impedance equivalent circuit model with a controlled source. Considering that the open-circuit voltage of a battery varies with the SOC, an equivalent circuit model with a controlled source is proposed which the voltage source and current source interact with each other. On this basis, the radial basis function (RBF) neural network is adopted to estimate the uncertainty in the battery model online, and a non-linear observer based on the radial basis function of the RBF neural network is designed to estimate the SOC of batteries. It is proved that the SOC estimation error is ultimately bounded by Lyapunov stability analysis, and the error bound can be arbitrarily small. The high accuracy and validity of the non-linear observer based on the RBF neural network in SOC estimation are verified with experimental simulation results. The SOC estimation results of the extended Kalman filter (EKF) are compared with the proposed method. It improves convergence speed and accuracy.

SOC Estimation Methods for Lithium-Ion Batteries without Current Monitoring

“…In addition, the adaptive filtering method has a complex algorithm and long calculation cycle, which includes nonlinear Kalman filter, particle filter, specifically including extended Kalman filter, traceless Kalman filter, and other methods [8][9][10][11][12]. In neural network methods [13][14][15] and support vector machine methods [16][17][18][19], the SOC estimation of a battery is viewed as a regression problem, using multiple inputs (e.g., voltage, current, and environmental variables) to predict the SOC. These methods usually require a large quantity of experimental data to train the neural network and use various optimization techniques to improve precision and robustness.…”