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Feature analyses and modelling of lithium-ion batteries manufacturing based on random forest classification
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Liu, Kailong, Hu, Xiaosong, Zhou, Huiyu, Tong, Lei, Widanage, Widanalage Dhammika and Marco, James (2021) Feature analyses and modelling of lithium-ion batteries manufacturing based on random forest classification. IEEE/ASME Transactions on Mechatronics, 26 (6). pp. 2944-2955. doi:10.1109/TMECH.2020.3049046 ISSN 1083-4435.
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WRAP-Feature-analyses-modelling-lithium-ion-batteries-random-forest-2021.pdf - Accepted Version - Requires a PDF viewer. Download (7Mb) | Preview |
Official URL: https://doi.org/10.1109/TMECH.2020.3049046
Abstract
Lithium-ion battery manufacturing is a highly complicated process with strongly coupled feature interdependencies, a feasible solution that can analyse feature variables within manufacturing chain and achieve reliable classification is thus urgently needed. This article proposes a random forest (RF)-based classification framework, through using the out of bag (OOB) predictions, Gini changes as well as predictive measure of association (PMOA), for effectively quantifying the importance and correlations of battery manufacturing features and their effects on the classification of electrode properties. Battery manufacturing data containing three intermediate product features from the mixing stage and one product parameter from the coating stage are analysed by the designed RF framework to investigate their effects on both the battery electrode active material mass load and porosity. Illustrative results demonstrate that the proposed RF framework not only achieves the reliable classification of electrode properties but also leads to the effective quantification of both manufacturing feature importance and correlations. This is the first time to design a systematic RF framework for simultaneously quantifying battery production feature importance and correlations by three various quantitative indicators including the unbiased feature importance (FI), gain improvement FI and PMOA, paving a promising solution to reduce model dimension and conduct efficient sensitivity analysis of battery manufacturing.
Item Type: | Journal Article | ||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||
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, Lithium ion batteries -- Materials, Lithium ion batteries -- Design and construction -- Classification | ||||||||
Journal or Publication Title: | IEEE/ASME Transactions on Mechatronics | ||||||||
Publisher: | Institute of Electrical and Electronics Engineers | ||||||||
ISSN: | 1083-4435 | ||||||||
Official Date: | December 2021 | ||||||||
Dates: |
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Volume: | 26 | ||||||||
Number: | 6 | ||||||||
Page Range: | pp. 2944-2955 | ||||||||
DOI: | 10.1109/TMECH.2020.3049046 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 28 January 2021 | ||||||||
Date of first compliant Open Access: | 29 January 2021 | ||||||||
RIOXX Funder/Project Grant: |
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