de Oliveira Junior, José Genario (TU Wien), Aras, Ayse Cisel (AVL Research and Engineering Turkey), Sivaraman, Thyagesh (AVL List GmbH), Hametner, Christoph (Vienna University of Technology)

Lithium-Ion Cell Ageing Prediction with Automated Feature Extraction

Scheduled for presentation during the Regular Session "Energy Storage and Charging Systems" (TuAT3), Tuesday, August 30, 2022, 11:00−11:20, Pfahl Hall 140

10th IFAC International Symposium on Advances in Automotive Control, August 28-31, 2022, Columbus, Ohio, USA

This information is tentative and subject to change. Compiled on May 1, 2024

Abstract

This paper aims to investigate how some features commonly associated with more generic time-series analysis are associated with capacity fade in lithium-ion cells and how they can be used to create simple but effective machine-learning models. This is done by processing the current, voltage, and temperature measurements, which span around two hundred cells for roughly two years, with a popular automated time-series analysis routine that extracts a significant number of different characteristics from the dataset for each signal. The most promising factors associated with the capacity fade are obtained by using a feature selection technique that is simple, quick and does not depend on a specific model structure. An analysis of the most relevant results is done, together with a standard hyperparameter search strategy using bayesian optimization for different classical regression models. With this step-by-step approach, the most promising features were investigated and an average error smaller than 5% was obtained on previously unseen validation data.