Machine learning for optimised and clean Li-ion battery manufacturing: Revealing the dependency between electrode and cell characteristics

Journal article


Niri, M.F., Liu, K., Apachitei, G., Roman Ramirez, L., Lain, M., Widanage, D. and Marco, J. (2021). Machine learning for optimised and clean Li-ion battery manufacturing: Revealing the dependency between electrode and cell characteristics. Journal of Cleaner Production. 324, p. 129272. https://doi.org/10.1016/j.jclepro.2021.129272
AuthorsNiri, M.F., Liu, K., Apachitei, G., Roman Ramirez, L., Lain, M., Widanage, D. and Marco, J.
Abstract

The large number of parameters involved in each step of Li-ion electrode manufacturing process as well as the complex electrochemical interactions in those affect the properties of the final product. Optimization of the manufacturing process, although very challenging, is critical for reducing the production time, cost, and carbon footprint. Data-driven models offer a solution for manufacturing optimization problems and underpin future aspirations for manufacturing volumes. This study combines machine-learning approaches with the experimental data to build data-driven models for predicting final battery performance. The models capture the interdependencies between the key parameters of electrode manufacturing, its structural features, and the electrical performance characteristics of the associated Li-ion cells. The methodology here is based on a set of designed experiments conducted in a controlled environment, altering electrode coating control parameters of comma bar gap, line speed and coating ratio, obtaining the electrode structural properties of active material mass loading, thickness, and porosity, extracting the manufactured half-cell characteristics at various cycling conditions, and finally building models for interconnectivity studies and predictions. Investigating and quantifying performance predictability through a systems' view of the manufacturing process is the main novelty of this paper. Comparisons between different machine-learning models, analysis of models’ performance with a limited number of inputs, analysis of robustness to measurement noise and data-size are other contributions of this study. The results suggest that, given manufacturing parameters, the coated electrode properties and cell characteristics can be predicted with about 5% and 3% errors respectively. The presented concepts are believed to link the manufacturing at lab-scale to the pilot-line scale and support smart, optimised, and clean production of electrodes for high-quality Li-ion batteries.

Year2021
JournalJournal of Cleaner Production
Journal citation324, p. 129272
PublisherElsevier
ISSN1879-1786
Digital Object Identifier (DOI)https://doi.org/10.1016/j.jclepro.2021.129272
Web address (URL)https://www.sciencedirect.com/science/article/abs/pii/S0959652621034582?via%3Dihub
Publication dates
Print06 Oct 2021
Publication process dates
Accepted03 Oct 2021
Deposited21 Mar 2024
Accepted author manuscript
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File Access Level
Open
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