Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure
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’ ele...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-06-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/12/4489 |
| _version_ | 1797487714102149120 |
|---|---|
| author | Mona Faraji Niri Jimiama Mafeni Mase James Marco |
| author_facet | Mona Faraji Niri Jimiama Mafeni Mase James Marco |
| author_sort | Mona Faraji Niri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:51:41Z |
| format | Article |
| id | doaj.art-83bf67bfc1b541acb6ff2b93e2a5daed |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T23:51:41Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-83bf67bfc1b541acb6ff2b93e2a5daed2023-11-23T16:32:00ZengMDPI AGEnergies1996-10732022-06-011512448910.3390/en15124489Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode MicrostructureMona Faraji Niri0Jimiama Mafeni Mase1James Marco2Warwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UKWarwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UKWarwick Manufacturing Group, University of Warwick, Coventry CV4 7AL, UKLi-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.https://www.mdpi.com/1996-1073/15/12/4489Li-ion batterydeep learningautoencoder decoderelectrode microstructureimage reconstruction |
| spellingShingle | Mona Faraji Niri Jimiama Mafeni Mase James Marco Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure Energies Li-ion battery deep learning autoencoder decoder electrode microstructure image reconstruction |
| title | Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure |
| title_full | Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure |
| title_fullStr | Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure |
| title_full_unstemmed | Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure |
| title_short | Performance Evaluation of Convolutional Auto Encoders for the Reconstruction of Li-Ion Battery Electrode Microstructure |
| title_sort | performance evaluation of convolutional auto encoders for the reconstruction of li ion battery electrode microstructure |
| topic | Li-ion battery deep learning autoencoder decoder electrode microstructure image reconstruction |
| url | https://www.mdpi.com/1996-1073/15/12/4489 |
| work_keys_str_mv | AT monafarajiniri performanceevaluationofconvolutionalautoencodersforthereconstructionofliionbatteryelectrodemicrostructure AT jimiamamafenimase performanceevaluationofconvolutionalautoencodersforthereconstructionofliionbatteryelectrodemicrostructure AT jamesmarco performanceevaluationofconvolutionalautoencodersforthereconstructionofliionbatteryelectrodemicrostructure |