Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development
Fast growing demands for electric vehicles require better longevity, safety and reliability for next-generation high-energy battery technologies. A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824000946 |
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| author | Yiheng Pang Yun Wang Zhiqiang Niu |
| author_facet | Yiheng Pang Yun Wang Zhiqiang Niu |
| author_sort | Yiheng Pang |
| collection | DOAJ |
| description | Fast growing demands for electric vehicles require better longevity, safety and reliability for next-generation high-energy battery technologies. A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics. Nowadays, Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance, health and safety, but is hurdled by a scarcity of data. To mitigate this issue, this study presents one of the first studies for data development through both experimental studies and three-dimensional (3-D) multi-physics modeling to underpin a deep learning framework with in-depth examination for battery performance and thermal risk prediction. Specifically, Part I focused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model accuracy by two steps: firstly, we validated the multi-physics model against two commercial Lithium-ion batteries, i.e., Panasonic NCR18650B and 18650BD; Then, the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model, such as voltage evolution and maximum local temperature (hot spot). The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II. |
| format | Article |
| id | doaj-art-0c329c2ef62a423d9d6f2bd6b8ac94e6 |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-0c329c2ef62a423d9d6f2bd6b8ac94e62024-12-18T08:53:03ZengElsevierEnergy and AI2666-54682024-12-0118100428Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data developmentYiheng Pang0Yun Wang1Zhiqiang Niu2Renewable Energy Resources Lab (RERL), Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, 92697-3975, United StatesRenewable Energy Resources Lab (RERL), Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, 92697-3975, United States; Corresponding authors.Department of Aeronautical and Automotive Engineering, Loughborough University, United Kingdom; Corresponding authors.Fast growing demands for electric vehicles require better longevity, safety and reliability for next-generation high-energy battery technologies. A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics. Nowadays, Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance, health and safety, but is hurdled by a scarcity of data. To mitigate this issue, this study presents one of the first studies for data development through both experimental studies and three-dimensional (3-D) multi-physics modeling to underpin a deep learning framework with in-depth examination for battery performance and thermal risk prediction. Specifically, Part I focused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model accuracy by two steps: firstly, we validated the multi-physics model against two commercial Lithium-ion batteries, i.e., Panasonic NCR18650B and 18650BD; Then, the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model, such as voltage evolution and maximum local temperature (hot spot). The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II.http://www.sciencedirect.com/science/article/pii/S2666546824000946Li-ion batteriesData developmentMulti-physics modelingHot spotMachine learning |
| spellingShingle | Yiheng Pang Yun Wang Zhiqiang Niu Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development Energy and AI Li-ion batteries Data development Multi-physics modeling Hot spot Machine learning |
| title | Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development |
| title_full | Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development |
| title_fullStr | Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development |
| title_full_unstemmed | Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development |
| title_short | Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development |
| title_sort | deep learning from three dimensional lithium ion battery multiphysics model part i data development |
| topic | Li-ion batteries Data development Multi-physics modeling Hot spot Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824000946 |
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