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,. doi: 10.1109/JAS.2022.106001|
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, 2022.
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.
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
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.
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.
C. Birkl, Oxford Battery Degradation Dataset 1, Oxford University, UK, 2017.
C. Zhang, and Y. Ma, Ensemble Machine Learning: Methods and Applications, Springer, 2012.