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 1
Jan.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Contents
K. L. 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
Citation: K. L. 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

Knowledge-Guided Data-Driven Model With Transfer Concept for Battery Calendar Ageing Trajectory Prediction

doi: 10.1109/JAS.2023.123036
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    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.
    [2]
    K. Liu, et al., “Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries,” Control Eng. Pract, vol. 124, p. 105176, Apr. 2022. doi: 10.1016/j.conengprac.2022.105176
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    Y. Li, et al., “Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review,” Renew. Sust. Energ. Rev, vol. 113, p. 109254, Jul. 2019.
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    T. Hu, H. Ma, H. Sun, and K. Liu, “Electrochemical-theory-guided modelling of the conditional generative adversarial network for battery calendar ageing forecast,” IEEE J. Emerg. Sel. Top. Power Electron, Feb. 2022.
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    B. Jiang, H. Dai, X. Wei, and Z. Jiang, “Multi-kernel relevance vector machine with parameter optimization for cycling aging prediction of lithium-ion batteries,” IEEE J. Emerg. Sel. Top. Power Electron, Dec. 2021.
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    K. Liu, Y. Shang, Q. Ouyang, and W. D. Widanage, “A data-driven approach with uncertainty quantification for predicting future capacities and remaining useful life of lithium-ion battery,” IEEE Trans. Ind. Electron, vol. 68, no. 4, pp. 3170–3180, Apr. 2021. doi: 10.1109/TIE.2020.2973876

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