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 12
Issue 3
IEEE/CAA Journal of Automatica Sinica
| Citation: | W. Zhang, H. Hao, and Y. Zhang, “State of charge prediction of lithium-ion batteries for electric aircraft with swin transformer,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 3, pp. 645–647, Mar. 2025. doi: 10.1109/JAS.2023.124020
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Z. Fei, F. Yang, K.-L. Tsui, L. Li, and Z. Zhang, “Early prediction of battery lifetime via a machine learning based framework,” Energy, vol. 225, p. 120205, Jun. 2021.
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K. S. R. Mawonou, A. Eddahech, D. Dumur, D. Beauvois, and E. Godoy, “State-of-health estimators coupled to a random forest approach for lithium-ion battery aging factor ranking,” J Power Sources, vol. 484, p. 229154, Feb. 2021.
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| [3] |
W. Zhang, X. Li, and X. Li, “Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation,” Measurement, vol. 164, p. 108052, Nov. 2020.
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| [4] |
Y. Zhu, X. Li, Y. Zhang, and W. Zhang, “Cross-domain prognostic method of lithium-ion battery in new energy electric aircraft with domain adaptation,” IEEE Sensors J., vol. 23, no. 13, pp. 14487–14498, Jul. 2023. doi: 10.1109/JSEN.2023.3277131
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| [5] |
M. Peter, H. A. Ghali, S. Memon, and F. Duan, “Electric vehicles lithium-polymer ion battery dynamic behaviour charging identification and modelling scheme,” J. Dynamics, vol. 2, no. 3, pp. 170–176, Aug. 2023.
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| [6] |
K. Liu, Q. Peng, R. Teodorescu, and A. M. Foley, “Knowledge-guided data-driven model with transfer concept for battery calendar ageing trajectory prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 272–274, Jan. 2023. doi: 10.1109/JAS.2023.123036
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| [7] |
B. Liu, H. Wang, M.-L. Tseng, and Z. Li, “State of charge estimation for lithium-ion batteries based on improved barnacle mating optimizer and support vector machine,” J. Energy Storage, vol. 55, p. 105830, 2022.
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| [8] |
X. Li, S. Yu, Y. Lei, N. Li, and B. Yang, “Intelligent machinery fault diagnosis with event-based camera,” IEEE Trans. Ind. Informatics, vol. 20, no. 1, pp. 380–389, 2024.
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| [9] |
W. Zhang, and X. Li, “Data privacy preserving federated transfer learning in machinery fault diagnostics using prior distributions,” Structural Health Monitoring, vol. 21, no. 4, pp. 1329–1344, Jul. 2023.
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| [10] |
X. Li, C. Zhang, X. Li, and W, Zhang, “Federated transfer learning in fault diagnosis under data privacy with target self-adaptation,” J. Manufacturing Systems, vol. 68, pp. 523–535, Jun. 2023. doi: 10.1016/j.jmsy.2023.05.006
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| [11] |
B. Peter Adedeji, and G. Kabir, “A feedforward deep neural network for predicting the state-of-charge of lithium-ion battery in electric vehicles,” Decision Analytics J., vol. 8, p. 100255, Sept. 2023.
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| [12] |
C. Vidal, P. Malysz, M. Naguib, A. Emadi, and P. J. Kollmeyer, “Estimating battery state of charge using recurrent and non-recurrent neural networks,” J. Energy Storage, vol. 47, p. 103660, Mar. 2022.
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