CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics
Lithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However, acquiring thermal imaging samples of lithium-...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-05-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546823000939 |
| _version_ | 1797353605428150272 |
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| author | Fengshuo Hu Chaoyu Dong Luyu Tian Yunfei Mu Xiaodan Yu Hongjie Jia |
| author_facet | Fengshuo Hu Chaoyu Dong Luyu Tian Yunfei Mu Xiaodan Yu Hongjie Jia |
| author_sort | Fengshuo Hu |
| collection | DOAJ |
| description | Lithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However, acquiring thermal imaging samples of lithium-ion battery faults is challenging due to factors such as high experimental costs and associated risks. To address this, our study proposes the utilization of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty and Residual Network (CWGAN-GP with Residual Network) to augment the dataset of thermal images depicting lithium-ion battery faults. We employ various evaluation metrics to quantitatively analyze and compare the generated thermal images of lithium-ion batteries. Subsequently, the expanded dataset, comprising four types of thermal images depicting lithium-ion battery faults, is input into a Mask Region-based Convolutional Neural Network for training. The results demonstrate that the proposed model surpasses both traditional Generative Adversarial Network and Wasserstein Generative Adversarial Network in terms of the quality of generated thermal images of lithium-ion batteries. Moreover, the augmentation of the dataset leads to an improvement in the fault diagnosis accuracy of the Mask Region-based Convolutional Neural Network. |
| first_indexed | 2024-03-08T13:33:27Z |
| format | Article |
| id | doaj.art-715e25c9e45143ffb9a288bac4649c09 |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-03-08T13:33:27Z |
| publishDate | 2024-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-715e25c9e45143ffb9a288bac4649c092024-01-17T04:17:17ZengElsevierEnergy and AI2666-54682024-05-0116100321CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metricsFengshuo Hu0Chaoyu Dong1Luyu Tian2Yunfei Mu3Xiaodan Yu4Hongjie Jia5School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaAgency for Science, Technology and Research, Nanyang Technological University, 639798, Singapore; Corresponding author.School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaLithium batteries find extensive applications in energy storage. Temperature is a crucial indicator for assessing the state of lithium-ion batteries, and numerous experiments require thermal images of lithium-ion batteries for research purposes. However, acquiring thermal imaging samples of lithium-ion battery faults is challenging due to factors such as high experimental costs and associated risks. To address this, our study proposes the utilization of a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty and Residual Network (CWGAN-GP with Residual Network) to augment the dataset of thermal images depicting lithium-ion battery faults. We employ various evaluation metrics to quantitatively analyze and compare the generated thermal images of lithium-ion batteries. Subsequently, the expanded dataset, comprising four types of thermal images depicting lithium-ion battery faults, is input into a Mask Region-based Convolutional Neural Network for training. The results demonstrate that the proposed model surpasses both traditional Generative Adversarial Network and Wasserstein Generative Adversarial Network in terms of the quality of generated thermal images of lithium-ion batteries. Moreover, the augmentation of the dataset leads to an improvement in the fault diagnosis accuracy of the Mask Region-based Convolutional Neural Network.http://www.sciencedirect.com/science/article/pii/S2666546823000939Lithium-ion batteriesGenerative adversarial networkCWGAN-GP |
| spellingShingle | Fengshuo Hu Chaoyu Dong Luyu Tian Yunfei Mu Xiaodan Yu Hongjie Jia CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics Energy and AI Lithium-ion batteries Generative adversarial network CWGAN-GP |
| title | CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics |
| title_full | CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics |
| title_fullStr | CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics |
| title_full_unstemmed | CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics |
| title_short | CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics |
| title_sort | cwgan gp with residual network model for lithium ion battery thermal image data expansion with quantitative metrics |
| topic | Lithium-ion batteries Generative adversarial network CWGAN-GP |
| url | http://www.sciencedirect.com/science/article/pii/S2666546823000939 |
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