Artificial intelligence-driven real-world battery diagnostics
Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volu...
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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/S2666546824000855 |
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| author | Jingyuan Zhao Xudong Qu Yuyan Wu Michael Fowler Andrew F. Burke |
| author_facet | Jingyuan Zhao Xudong Qu Yuyan Wu Michael Fowler Andrew F. Burke |
| author_sort | Jingyuan Zhao |
| collection | DOAJ |
| description | Addressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization. |
| format | Article |
| id | doaj-art-51cd2a37c708480a8fadf3ee21004cc0 |
| institution | DOAJ |
| issn | 2666-5468 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-51cd2a37c708480a8fadf3ee21004cc02025-08-20T02:49:00ZengElsevierEnergy and AI2666-54682024-12-011810041910.1016/j.egyai.2024.100419Artificial intelligence-driven real-world battery diagnosticsJingyuan Zhao0Xudong Qu1Yuyan Wu2Michael Fowler3Andrew F. Burke4Institute of Transportation Studies, University of California Davis, Davis, CA 95616, USA; Corresponding author.Hubei Longzhong Laboratory, Hubei University of Arts and Science, Xiangyang 441000, ChinaDepartment of Civil and Environmental Engineering, Stanford University, Stanford, CA 94305, USADepartment of Chemical Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, CanadaInstitute of Transportation Studies, University of California Davis, Davis, CA 95616, USAAddressing real-world challenges in battery diagnostics, particularly under incomplete or inconsistent boundary conditions, has proven difficult with traditional methodologies such as first-principles and atomistic calculations. Despite advances in data assimilation techniques, the overwhelming volume and diversity of data, coupled with the lack of universally accepted models, underscore the limitations of these traditional approaches. Recently, deep learning has emerged as a highly effective tool in overcoming persistent issues in battery diagnostics by adeptly managing expansive design spaces and discerning intricate, multidimensional correlations. This approach resolves challenges previously deemed insurmountable, especially with lost, irregular, or noisy data through the design of specialized network architectures that adhere to physical invariants. However, gaps remain between academic advancements and their practical applications, including challenges in explainability and the computational costs associated with AI-driven solutions. Emerging technologies such as explainable artificial intelligence (XAI), AI for IT operations (AIOps), lifelong machine learning to mitigate catastrophic forgetting, and cloud-based digital twins open new opportunities for intelligent battery life-cycle assessment. In this perspective, we outline these challenges and opportunities, emphasizing the potential of innovative technologies to transform battery diagnostics, as demonstrated by our recent practice and the progress made in the field. This includes promising achievements in both academic and industry field demonstrations in modeling and forecasting the dynamics of multiphysics and multiscale battery systems. These systems feature inhomogeneous cascades of scales, informed by our physical, electrochemical, observational, empirical, and/or mathematical understanding of the battery system. Through data assimilation efforts, meticulous craftsmanship, and elaborate implementations—and by considering the wealth and spatio-temporal heterogeneity of available data—such AI-based intelligent learning philosophies have great potential to achieve better accuracy, faster training, and improved generalization.http://www.sciencedirect.com/science/article/pii/S2666546824000855BatterySafetyHealthLifetimeArtificial intelligenceMachine learning |
| spellingShingle | Jingyuan Zhao Xudong Qu Yuyan Wu Michael Fowler Andrew F. Burke Artificial intelligence-driven real-world battery diagnostics Energy and AI Battery Safety Health Lifetime Artificial intelligence Machine learning |
| title | Artificial intelligence-driven real-world battery diagnostics |
| title_full | Artificial intelligence-driven real-world battery diagnostics |
| title_fullStr | Artificial intelligence-driven real-world battery diagnostics |
| title_full_unstemmed | Artificial intelligence-driven real-world battery diagnostics |
| title_short | Artificial intelligence-driven real-world battery diagnostics |
| title_sort | artificial intelligence driven real world battery diagnostics |
| topic | Battery Safety Health Lifetime Artificial intelligence Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824000855 |
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