Faraji Niri, Mona, Liu, Kailong, Apachitei, Geanina, Lain, Michael J., Widanage, Widanalage Dhammika and Marco, James (2022) Quantifying key factors for optimised manufacturing of Li-ion battery anode and cathode via artificial intelligence. Energy and AI, 100 . 100129. doi:10.1016/j.egyai.2021.100129

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Abstract

Li-ion battery is one of the key players in energy storage technology empowering electrified and clean transportation systems. However, it is still associated with high costs due to the expensive material as well as high fluctuations of the manufacturing process. Complicated production processes involving mechanical, chemical, and electrical operations makes the predictability of the manufacturing process a challenge, hence the process is optimised through trial and error rather systematic simulation. To establish an in-depth understanding of the interconnected processes and manufacturing parameters, this paper combines data-mining techniques and real production to offer a method for the systematic analysis, understanding and improving the Li-ion battery electrode manufacturing chain. The novelty of this research is that unlike most of the existing research that are focused on cathode manufacturing only, it covers both of the cathode and anode case studies. Furthermore, it is based on real manufacturing data, proposes a systematic design of experiment method for generating high quality and representative data, and leverages the artificial intelligence techniques to identify the dependencies in between the manufacturing parameters and the key quality factors of the electrode. Through this study, machine learning models are developed to quantify the predictability of electrode and cell properties given the coating process control parameters. Moreover, the manufacturing parameters are ranked and their contribution to the electrode and cell characteristics are quantified by models. The systematic data acquisition approach as well as the quantified interdependencies are expected to assist the manufacturer when moving towards an improved battery production chain.

Item Type:	Journal Article
Subjects:	Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH):	Lithium ion batteries , Lithium ion batteries -- Design and construction, Electric batteries -- Electrodes , Electric batteries -- Electrodes -- Materials, Anodes, Cathodes, Machine learning , Artificial intelligence
Journal or Publication Title:	Energy and AI
Publisher:	Elsevier
Official Date:	1 January 2022
Dates:	Date Event 1 January 2022 Published 1 December 2021 Available 29 November 2021 Accepted
Volume:	100
Article Number:	100129
DOI:	10.1016/j.egyai.2021.100129
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID FIRG015 Faraday Technology http://dx.doi.org/10.13039/100006587

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