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 9
Issue 8
IEEE/CAA Journal of Automatica Sinica
| Citation: | Y. J. Wang, K. Q. Li, and Z. H. Chen, “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, Aug. 2022. doi: 10.1109/JAS.2022.105779
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| [1] |
Y. Wang, J. Tian, Z. Sun, L. Wang, R. Xu, M. Li, and Z. Chen. “A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems,” Renewable & Sustainable Energy Reviews, vol. 131, p. 110015, Oct. 2020.
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| [2] |
K. Liu, Z. Wei, C. Zhang, Y. Shang, R, Teodorescu, and Q. Han, “Towards long lifetime battery: AI-based manufacturing and management,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1139–1165, Jul. 2022.
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| [3] |
T. Meng, Z. Lin, and Y. A. Shamash, “Distributed cooperative control of battery energy storage systems in DC microgrids,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 606–616, Mar. 2021. doi: 10.1109/JAS.2021.1003874
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| [4] |
K. Liu, Y. Gao, C. Zhu, K. Li, M. Fei, C. Peng, X. Zhang, and Q. Han, “Electrochemical modeling and parameterization towards control-oriented management of lithium-ion batteries,” Control Engineering Practice, vol. 124, p. 105176, Jul. 2022.
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| [5] |
Y. Wang, R. Xu, C. Zhou, X. Kang, and Z. Chen, “Digital twin and cloud-side-end collaboration for intelligent battery management system,” J. Manufacturing Systems, vol. 62, pp. 124–134, Jan. 2022. doi: 10.1016/j.jmsy.2021.11.006
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| [6] |
D. Ren, X. Feng, L. Liu, H. Hsu, L. Lu, L. Wang, X. He, and M. Ouyang, “Investigating the relationship between internal short circuit and thermal runaway of lithium-ion batteries under thermal abuse condition,” Energy Storage Materials, vol. 34, pp. 563–573, Jan. 2021. doi: 10.1016/j.ensm.2020.10.020
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| [7] |
K. Liu, X. Hu, Z. Wei, Y. Li, and Y. Jiang, “Modified Gaussian process regression models for cyclic capacity prediction of lithium-ion batteries,” IEEE Trans. Transport. Electrification, vol. 5, no. 4, pp. 1225–1236, Dec. 2019. doi: 10.1109/TTE.2019.2944802
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| [8] |
S. Zhang, B. Zhai, X. Guo, K. Wang, N. Peng, and X. Zhang, “Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks,” J. Energy Storage, vol. 26, p. 100951, 2019.
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| [9] |
J. Li, R. G. Landers, and J. Park, “A Comprehensive single-particle-degradation model for battery state-of-health prediction,” J. Power Sources, vol. 456, pp. 227950, Apr. 2020.
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| [10] |
X. Wang, R. Li, H. Dai, N. Zhang, Q. Chen, and X. Wei, “A novel dual time scale life prediction method for lithium-ion batteries considering effects of temperature and state of charge,” Int. J. Energy Research, vol. 45, pp. 14692–14709, 2021. doi: 10.1002/er.6746
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