The Library
Machine learning in lithium‐ion battery cell production : a comprehensive mapping study
Tools
Haghi, Sajedeh, Hidalgo, Marc, Niri, Mona Faraji, Daub, Rüdiger and Marco, James (2023) Machine learning in lithium‐ion battery cell production : a comprehensive mapping study. Batteries & Supercaps, 6 (7). e202300046. doi:10.1002/batt.202300046 ISSN 2566-6223.
|
PDF
Batteries Supercaps - 2023 - Haghi - Machine Learning in Lithium‐Ion Battery Cell Production A Comprehensive Mapping.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (13Mb) | Preview |
Official URL: https://doi.org/10.1002/batt.202300046
Abstract
With the global quest for improved sustainability, partially realized through the electrification of the transport and energy sectors, battery cell production has gained ever-increasing attention. An in-depth understanding of battery production processes and their interdependence is crucial for accelerating the commercialization of material developments, for example, at the volume predicted to underpin future electric vehicle production. Over the last five years, machine learning approaches have shown significant promise in understanding and optimizing the battery production processes. Based on a systematic mapping study, this comprehensive review details the state-of-the-art applications of machine learning within the domain of lithium-ion battery cell production and highlights the fundamental aspects, such as product and process parameters and adopted algorithms. The compiled findings derived from multi-perspective comparisons demonstrate the current capabilities and reveal future research opportunities in this field to further accelerate sustainable battery production.
Item Type: | Journal Article | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > Q Science (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
|||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
SWORD Depositor: | Library Publications Router | |||||||||
Library of Congress Subject Headings (LCSH): | Lithium ion batteries -- Design and construction, Machine learning | |||||||||
Journal or Publication Title: | Batteries & Supercaps | |||||||||
Publisher: | Wiley | |||||||||
ISSN: | 2566-6223 | |||||||||
Official Date: | July 2023 | |||||||||
Dates: |
|
|||||||||
Volume: | 6 | |||||||||
Number: | 7 | |||||||||
Article Number: | e202300046 | |||||||||
DOI: | 10.1002/batt.202300046 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Re-use Statement: | ||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 19 May 2023 | |||||||||
Date of first compliant Open Access: | 19 May 2023 | |||||||||
RIOXX Funder/Project Grant: |
|
|||||||||
Related URLs: |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year