State of Health Estimation of Lithium-ion Batteries Using Convolutional Neural Network with Impedance Nyquist Plots

Abstract

: In order to maintain the Li-ion batteries in a safe operating state and to optimize their performance, a precise estimation of the state of health (SOH), which indicates the degradation level of the Li-ion batteries, has to be taken into consideration urgently. In this paper, we present a regression machine learning framework that combines a convolutional neural network (CNN) with the Nyquist plot of Electrochemical Impedance Spectroscopy (EIS) as features to estimate the SOH of Li-ion batteries with a considerable improvement in the accuracy of SOH estimation. The results indicate that the Nyquist plot based on EIS features provides more detailed information regarding battery aging than simple impedance values due to its ability to reflect impedance change over time. Furthermore, convolutional layers in the CNN model were more effective in extracting different levels of features and characterizing the degradation patterns of Li-ion batteries from EIS measurement data than using simple impedance values with a DNN model, as well as other traditional machine learning methods, such as Gaussian process regression (GPR) and support vector machine (SVM).