Projects per year
Abstract
Lithium-ion batteries are key energy storage technologies to promote the global clean energy process, particularly in power grids and electrified transportation. However, complex usage conditions and lack of precise measurement make it difficult for battery health estimation under field applications, especially for aging mode diagnosis. In a recent issue of Nature Communications, Dubarry et al. shed light on this issue by investigating the solution based on machine learning and battery digital twins. They achieved aging modes diagnosis of photovoltaics-connected batteries working for 2 years with more than 10,000 degradation paths under different seasons and cloud shading conditions.
Original language | English |
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Journal | Joule |
Volume | 7 |
Issue number | 7 |
Pages (from-to) | 1405-1407 |
Number of pages | 3 |
ISSN | 2542-4785 |
DOIs |
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Publication status | Published - 19 Jul 2023 |
Bibliographical note
FPublisher Copyright:© 2023
Fingerprint
Dive into the research topics of 'Opportunities for battery aging mode diagnosis of renewable energy storage'. Together they form a unique fingerprint.-
CROSBAT: SMART BATTERY
Teodorescu, R. (PI), Stroe, D.-I. (CoPI), Sui, X. (Project Participant), Weinreich, N. A. (Project Participant), Che, Y. (Project Participant), Kulkarni, A. (Project Participant), Zheng, Y. (Project Participant), Vilsen, S. B. (Project Participant), Bharadwaj, P. (Project Participant), Christensen, M. D. (Project Coordinator) & Steffensen, B. (Project Coordinator)
01/09/2021 → 31/08/2027
Project: Research
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State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning
Che, Y. (PI), Teodorescu, R. (Supervisor) & Sui, X. (Supervisor)
01/12/2021 → 31/12/2023
Project: PhD Project
Research output
- 4 Citations
- 1 PhD thesis
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State of Health Estimation and Prediction for Lithium-ion Batteries Based on Transfer Learning
Che, Y., 2023, Aalborg Universitetsforlag.Research output: PhD thesis
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