Lithium-ion Cell Ageing Prediction with Automated Feature Extraction. Issue 24 (2022)

• Record Type: Journal Article
• Authors: Jr, Jose Genario de Oliveira; Aras, Cisel; Sivaraman, Thyagesh; Hametner, Christoph
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 24(2022)
• Page Start: 203
• Page End: 208
• Publication Date: 2022
• Subjects: Battery management systems -- Energy storage systems: electrochemical systems, supercapacitators, fuel cells; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.10.285
• Languages: English
• ISSNs: 2405-8963

Abstract

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.