Liu, K, Yang, Z, Wang, H et al. (1 more author) (2021) Classifications of Lithium-Ion Battery Electrode Property Based on Support Vector Machine with Various Kernels. In: Recent Advances in Sustainable Energy and Intelligent Systems: 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Han. 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, 30 Oct - 01 Nov 2021, Hangzhou, China. Springer, Singapore , pp. 23-34. ISBN 978-981-16-7209-5
Abstract
Manufacturing chain of lithium-ion batteries belongs to a significantly complex process with many coupled product parameters and intermediate products. To well monitor and optimize battery manufacturing process, it is vital to design a data-driven approach for effectively modelling and classifying the product properties within this complicated production chain. In this paper, a support vector machine (SVM)-based framework, through using four various and powerful kernels including linear kernel, quadratic kernel, cubic kernel and Gaussian kernel, is proposed to well classify the electrode mass loading property of battery. The effects of four crucial variables including three product features from mixing step and one product parameter from coating step on the electrode property classification are also investigated. Comparative results illustrate that electrode mass loading can be effectively classified by the designed SVM framework while Gaussian kernel-based SVM achieves the best classification for all labelled classes. This is the first time to systematically evaluate and compare the performance of different kernel-based SVMs on the battery electrode property classification. Due to data-driven nature, the proposed SVM-based framework can be easily extended to classify other product properties and analyze other variables in battery production domain.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Singapore Pte Ltd. 2021. This is an author produced version of a conference paper published in Recent Advances in Sustainable Energy and Intelligent Systems: 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Hangzhou, China, October 22–24, 2021, Proceedings, Part II. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Lithium-ion battery; Battery production chain; Support vector machine; Kernel functions; Electrode property classifications |
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 10:22 |
Last Modified: | 19 Oct 2022 00:16 |
Status: | Published |
Publisher: | Springer, Singapore |
Identification Number: | 10.1007/978-981-16-7210-1_3 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179566 |