A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 10
Issue 9
IEEE/CAA Journal of Automatica Sinica
| Citation: | D. X. Ji, Z. B. Wei, C. Y. Tian, H. R. Cai, and J. H. Zhao, “Deep transfer ensemble learning-based diagnostic of lithium-ion battery,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1899–1901, Sept. 2023. doi: 10.1109/JAS.2022.106001
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| [1] |
K. Liu, K. Liu, Z. Wei, C. Zhang, Y. Shang, R. Teodorescu, and Q.-L. Han, “Towards long lifetime battery: AI-based manufacturing and management,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1139–1165, Jul. 2022. doi: 10.1109/JAS.2022.105599
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| [2] |
S. Shen, M. Sadoughi, M. Li, Z. Wang, and C. Hu, “Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries,” Applied Energy, vol. 260, p. 114296, 2020.
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| [3] |
Y. Tan and G. Zhao, “Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries,” IEEE Industrial Electronics, vol. 67, no. 10, pp. 8723–8731, 2019. doi: 10.1109/TIE.2019.2946551
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| [4] |
Y. Che, Z. Deng, X. Lin, L. Hu, and X. Hu, “Predictive battery health management with transfer learning and online model correction,” IEEE Trans. Vehicular Technology, vol. 70, no. 2, pp. 1269–1277, 2021.
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| [5] |
B. Saha, and K. Goebel, “Battery data set,” in NASA AMES Prognostics Data Repository, [Online], Available: https://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-datarepository/#battery, 2007.
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| [6] |
C. Birkl, Oxford Battery Degradation Dataset 1, Oxford University, UK, 2017.
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| [7] |
C. Zhang, and Y. Ma, Ensemble Machine Learning: Methods and Applications, Berlin, Heidelberg, Germany: Springer, 2012.
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