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Performance evaluation of convolutional auto encoders for the reconstruction of Li-ion battery electrode microstructure
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Faraji Niri, Mona, Mafeni Mase, Jimiama and Marco, James (2022) Performance evaluation of convolutional auto encoders for the reconstruction of Li-ion battery electrode microstructure. Energies, 15 (12). pp. 4489-4509. doi:10.3390/en15124489 ISSN 1996-1073.
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Official URL: https://doi.org/10.3390/en15124489
Abstract
Li-ion batteries play a critical role in the transition to a net-zero future. The discovery of new materials and the design of novel microstructures for battery electrodes is necessary for the acceleration of this transition. The battery electrode microstructure can potentially reveal the cells’ electrochemical characteristics in great detail. However, revealing this relation is very challenging due to the high dimensionality of the problem and the large number of microstructure features. In fact, it cannot be achieved via the traditional trial-and-error approaches, which are associated with significant cost, time, and resource waste. In search for a systematic microstructure analysis and design method, this paper aims at quantifying the Li-ion battery electrode structural characteristics via deep learning models. Deliberately, here, a methodology and framework are developed to reveal the hidden microstructure characteristics via 2D and 3D images through dimensionality reduction. The framework is based on an auto-encoder decoder for microstructure reconstruction and feature extraction. Unlike most of the existing studies that focus on a limited number of features extracted from images, this study concentrates directly on the images and has the potential to define the number of features to be extracted. The proposed methodology and model are computationally effective and have been tested on a real open-source dataset where the results show the efficiency of reconstruction and feature extraction based on the training and validation mean squared errors between 0.068 and 0.111 and from 0.071 to 0.110, respectively. This study is believed to guide Li-ion battery scientists and manufacturers in the design and production of next generation Li-ion cells in a systematic way by correlating the extracted features at the microstructure level and the cell’s electrochemical characteristics.
Item Type: | Journal Article | |||||||||
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Subjects: | Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
Library of Congress Subject Headings (LCSH): | Lithium ion batteries, Deep learning (Machine learning), Electrodes, Image reconstruction | |||||||||
Journal or Publication Title: | Energies | |||||||||
Publisher: | MDPI | |||||||||
ISSN: | 1996-1073 | |||||||||
Official Date: | 20 June 2022 | |||||||||
Dates: |
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Volume: | 15 | |||||||||
Number: | 12 | |||||||||
Page Range: | pp. 4489-4509 | |||||||||
DOI: | 10.3390/en15124489 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 22 June 2022 | |||||||||
Date of first compliant Open Access: | 22 June 2022 | |||||||||
RIOXX Funder/Project Grant: |
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