Roadmap on Li-ion battery manufacturing research

Patrick S. Grant* (Corresponding Author), David Greenwood, Kunal Pardikar, Rachel Smith, Thomas Entwistle, Laurence A. Middlemiss, Glen Murray, Serena A. Cussen, M. J. Lain, M. J. Capener, M. Copley, Carl D. Reynolds, Sam D. Hare, Mark J.H. Simmons, Emma Kendrick, Stanislaw P. Zankowski, Samuel Wheeler, Pengcheng Zhu, Peter R. Slater, Ye Shui ZhangAndrew R.T. Morrison, Will Dawson, Juntao Li, Paul R. Shearing, Dan J.L. Brett, Guillaume Matthews, Ruihuan Ge, Ross Drummond, Eloise C. Tredenick, Chuan Cheng, Stephen R. Duncan, Adam M. Boyce, Mona Faraji-Niri, James Marco, Luis A. Roman-Ramirez, Charlotte Harper, Paul Blackmore, Tim Shelley, Ahmad Mohsseni, Denis J. Cumming* (Corresponding Author)

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
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Abstract

Growth in the Li-ion battery market continues to accelerate, driven primarily by the increasing need for economic energy storage for electric vehicles. Electrode manufacture by slurry casting is the first main step in cell production but much of the manufacturing optimisation is based on trial and error, know-how and individual expertise. Advancing manufacturing science that underpins Li-ion battery electrode production is critical to adding to the electrode manufacturing value chain. Overcoming the current barriers in electrode manufacturing requires advances in materials, manufacturing technology, in-line process metrology and data analytics, and can enable improvements in cell performance, quality, safety and process sustainability. In this roadmap we explore the research opportunities to improve each stage of the electrode manufacturing process, from materials synthesis through to electrode calendering. We highlight the role of new process technology, such as dry processing, and advanced electrode design supported through electrode level, physics-based modelling. Progress in data driven models of electrode manufacturing processes is also considered. We conclude there is a growing need for innovations in process metrology to aid fundamental understanding and to enable feedback control, an opportunity for electrode design to reduce trial and error, and an urgent imperative to improve the sustainability of manufacture.

Original languageEnglish
Article number042006
Number of pages58
JournalJPhys Energy
Volume4
Issue number4
Early online date7 Nov 2022
DOIs
Publication statusPublished - 7 Nov 2022

Bibliographical note

Funding Information:
This work was supported by the Faraday Institution [Grant Nos. FIRG015 and EP/S003053/1]. P R S would like to acknowledge the Royal Academy of Engineering [CiET1718\59] for financial support.

Keywords

  • battery
  • coating
  • electrode
  • Li-ion
  • machine learning
  • manufacturing
  • metrology

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