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 13
Issue 10
IEEE/CAA Journal of Automatica Sinica
| Citation: | Q. Yu, C. Wang, J. Li, J. Tian, and K. Liu, “StateTracer-VoltPredictor framework for micro voltage anomaly detection in lithium-ion batteries,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 10, pp. 2408–2410, Oct. 2026. doi: 10.1109/JAS.2025.125738
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| [1] |
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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| [2] |
Y. Ma, B. Li, G. Li, J. Zhang, and H. Chen, “A nonlinear observer approach of SOC estimation based on hysteresis model for lithium-ion battery,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 2, pp. 195−204, 2017.
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| [3] |
X. Hu, K. Zhang, K. Liu, X. Lin, S. Dey, and S. Onori, “Advanced fault diagnosis for lithium-ion battery systems: A review of fault mechanisms, fault features, and diagnosis procedures,” IEEE Industrial Electronics Magazine, vol. 14, no. 3, pp. 65−91, 2020.
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| [4] |
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, 2023.
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| [5] |
D. Ji, Z. Wei, C. Tian, H. Cai, and J. Zhao, “Deep transfer ensemble learning-based diagnostic of lithium-ion battery,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 9, pp. 1899−1901, 2023.
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| [6] |
X. Li, X. Gao, Z. Zhang, Q. Chen, and Z. Wang, “Fault diagnosis and detection for battery system in real-world electric vehicles based on long-term feature outlier analysis,” IEEE Trans. Transportation Electrification, vol. 10, no. 1, pp. 1668−1679, 2024.
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| [7] |
J. Hu, X. Bian, Z. Wei, J. Li, and H. He, “Residual statistics-based current sensor fault diagnosis for smart battery management,” IEEE J. Emerging and Selected Topics in Power Electronics, vol. 10, no. 2, pp. 2435−2444, 2022.
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| [8] |
M. Zhao, S. Zhong, X. Fu, B. Tang, and M. Pecht, “Deep residual shrinkage networks for fault diagnosis,” IEEE Trans. Industrial Inform., vol. 16, no. 7, pp. 4681−4690, 2020.
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| [9] |
P. Kollmeyer, “Panasonic 18650PF Li-ion battery data,” Jun. 2018. [Online], Available: https://data.mendeley.com/datasets/wykht8y7tg/1, Accessed on: Jul. 10, 2025.
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