A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
IEEE/CAA Journal of Automatica Sinica
| Citation: | K. Liu, Y. Liu, Q. Peng, N. Cui, and C. Zhang, “Interpretable data-driven learning with fast ultrasonic detection for battery health estimation,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–3, Jan. 2025.
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| [1] |
Y. Wang, K. Li, et al., “Battery full life cycle management and health prognosis based on cloud service and broad learning,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1540–1542, 2022.
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| [2] |
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, 2022.
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| [3] |
M. Chen, et al., “An overview of data-driven battery health estimation technology for battery management system,” Neurocomputing, vol. 532, no. 1, pp. 152–169, 2023.
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| [4] |
G. Ma, Z. Wang, W. Liu, et al., “Estimating the state of health for lithium-ion batteries: A particle swarm optimization-assisted deep domain adaptation approach,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1530–1543, 2023.
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| [5] |
Q. Xu, M. Wu, et al., “A hybrid ensemble deep learning approach for early prediction of battery remaining useful life,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 177–187, 2023.
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| [6] |
K. Liu et al., “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, 2023.
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| [7] |
K. Liu et al., “Explainable neural network for sensitivity analysis of lithium-ion battery smart production,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 9, pp. 1–10, 2024.
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