|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|
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.
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.
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
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.
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
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
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
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.
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.
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