Capacity and State-of-Health Prediction of Lithium-Ion Batteries Using Reduced Equivalent Circuit Models

Abstract

Knowledge of battery health and its degradation has been a research focus since it enables users to use batteries optimally. The dynamic electrochemical properties within a cell can be represented by an equivalent circuit to observe the impedance over a range of frequencies, which is an indicator of the cell’s degradation buildup from an electrical framework. This process provides information on the different electrochemical processes observed at different frequency ranges, which can be used to optimally predict the capacity fade of a cell. With the increasing demand for batteries, faster and less computationally intensive means are being explored to predict the capacity degradation of batteries. The proposed method in this article introduces an effective reduced equivalent circuit model (ER-ECM) for battery prognosis studies. The ER-ECM measures the parameters of impedance spectra from the high- to mid-frequency regions for data input. These parameters are then used to accurately predict the capacity of the battery and its state of health. The results show that the overarching charge transfer resistance provides the most salient data for capacity predictions, having an average capacity error of 1.4%, which is a 40% reduction compared to using all the parameters of the ER-ECM. The ECMs used in this study also provide faster model training and testing by 6% compared to using global impedance spectra.

Topics & Concepts