Analysis of Li-Ion Cells Ageing Process Trough ECM Characterization, Statistics and Machine-Learning Algorithms

The work describes the use of Electrochemical Impedance Spectroscopy (EIS) and Equivalent Circuit Models (ECMs) for predicting the aging of lithium-ion (Li-ion) batteries, in order to accelerate the assessment of battery cells used in space applications. The aging of Li-ion cells can lead to capacity fade, power fade, and safety issues, making it essential to develop techniques for predicting their performance under different operating conditions. The use of machine learning (ML) techniques is also considered crucial for battery research and development due to the high number of variables involved in the problem and the massive amount of data generated daily. The methodology involved in analysing EIS data, selecting suitable datasets and exploiting them to develop representative mathematical models for the cell status at each studied age stage is described here. The text also discusses the use of statistical and machine learning methods for understanding and predicting the state of health of Li-ion batteries based on specific parameters. The results of the study showed that the Cambridge dataset was the most suitable for the analysis of the aging process. On the other hand, the Support Vector Machine for Regression (SVR) and the K-Nearest Neighbors Regression (KNN-Re) methods were selected for training a contrasted correlation model on the study dataset. The feature importance calculation using the permutation importance method revealed that the test temperature was the most important feature for predicting the state of health, followed by Warburg impedance.