Grant, P.S., Greenwood, D., Pardikar, K. orcid.org/0000-0001-5720-6518 et al. (37 more authors) (2022) Roadmap on Li-ion battery manufacturing research. Journal of Physics: Energy, 4 (4). 042006. ISSN 2515-7655
Abstract
Growth in the Li-ion battery market continues to accelerate, driven by increasing need for economic energy storage in the electric vehicle market. Electrode manufacture is the first main step in production and in an industry dominated by slurry casting, much of the manufacturing process 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 value to the electrode manufacturing value chain. Overcome the current barriers in the electrode manufacturing requires advances in material innovation, manufacturing technology, in-line process metrology and data analytics to improve cell performance, quality, safety and process sustainability. In this roadmap we present where fundamental research can impact advances in each stage of the electrode manufacturing process from materials synthesis to electrode calendering. We also highlight the role of new process technology such as dry processing and advanced electrode design supported through electrode level, physics-based modelling. To compliment this, the progresses in data driven models of full manufacturing processes is reviewed. For all the processes we describe, there is a growing need process metrology, not only to aid fundamental understanding but also to enable true feedback control of the manufacturing process. It is our hope this roadmap will contribute to this rapidly growing space and provide guidance and inspiration to academia and industry.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Author(s). Published by IOP Publishing Ltd. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. (http://creativecommons.org/licenses/by/4.0/) Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield) The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Materials Science and Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/L016818/1 UK RESEARCH AND INNOVATION EP/T012412/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 26 Sep 2022 13:49 |
Last Modified: | 14 Nov 2022 15:01 |
Status: | Published |
Publisher: | IOP Publishing |
Refereed: | Yes |
Identification Number: | 10.1088/2515-7655/ac8e30 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190810 |