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-...
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sg-ntu-dr.10356-1756052024-05-03T15:36:48Z CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics Hu, Fengshuo Dong, Chaoyu Tian, Luyu Mu, Yunfei Yu, Xiaodan Jia, Hongjie School of Computer Science and Engineering Engineering Lithium-ion batteries Generative adversarial network 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. Published version This research was supported by the project of National Natural Science Foundation of China (U23B6006, 52277116). 2024-04-30T05:51:06Z 2024-04-30T05:51:06Z 2024 Journal Article Hu, F., Dong, C., Tian, L., Mu, Y., Yu, X. & Jia, H. (2024). CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics. Energy and AI, 16, 100321-. https://dx.doi.org/10.1016/j.egyai.2023.100321 2666-5468 https://hdl.handle.net/10356/175605 10.1016/j.egyai.2023.100321 2-s2.0-85182394977 16 100321 en Energy and AI © 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf |
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Engineering Lithium-ion batteries Generative adversarial network Hu, Fengshuo Dong, Chaoyu Tian, Luyu Mu, Yunfei Yu, Xiaodan Jia, Hongjie CWGAN-GP with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics |
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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. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Hu, Fengshuo Dong, Chaoyu Tian, Luyu Mu, Yunfei Yu, Xiaodan Jia, Hongjie |
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Article |
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Hu, Fengshuo Dong, Chaoyu Tian, Luyu Mu, Yunfei Yu, Xiaodan Jia, Hongjie |
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Hu, Fengshuo |
title |
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_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 |
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cwgan-gp with residual network model for lithium-ion battery thermal image data expansion with quantitative metrics |
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2024 |
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https://hdl.handle.net/10356/175605 |
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