Liu, K, Li, K orcid.org/0000-0001-6657-0522 and Chen, T (2022) Interpretable Sensitivity Analysis and Electrode Porosity Classification for Li-ion Battery Smart Manufacturing. In: Proceedings: 2021 IEEE Sustainable Power and Energy Conference (iSPEC). 2021 IEEE Sustainable Power and Energy Conference (iSPEC), 23-25 Dec 2021, Nanjing, China. IEEE , pp. 3653-3658. ISBN 978-1-6654-1439-5
Abstract
Lithium-ion batteries have become one of the most promising sources for accelerating the development of sustainable energy, where effective cell manufacturing plays a direct role in determining battery qualities. Due to the highly complicated process and strongly coupled interdependencies of battery manufacturing, a data-driven approach that can evaluate the sensitivity of manufacturing parameters and provide the effective classification is urgently required. This paper proposes a boosting tree-based ensemble machine learning framework to analyze and predict how the battery electrode porosity varies with respect to the key parameters of both mixing and coating stages for the first time. Three boosting models including the AdaBoost, LPBoost, and TotalBoost are established and compared. Illustrative results demonstrate that the proposed ensemble machine learning framework is able to not only give effective quantification of both importance and correlations of parameters of interest but also provide satisfactory early-stage prediction. These kinds of information could benefit the monitoring and analysis of battery manufacturing chain, further help to produce high quality batteries for wider sustainable energy applications.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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. |
Keywords: | Sustainable energy, Li-ion battery, Battery manufacturing, Data analysis, Ensemble machine learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Oct 2021 10:59 |
Last Modified: | 17 Oct 2023 13:47 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/iSPEC53008.2021.9735647 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179567 |