State-of-charge estimation of lithium-ion batteries based on fractional-order modeling and adaptive square-root cubature Kalman filter
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DOI: 10.1016/j.energy.2023.127007
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Cited by:
- Sulaiman, Mohd Herwan & Mustaffa, Zuriani & Zakaria, Nor Farizan & Saari, Mohd Mawardi, 2023. "Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle," Energy, Elsevier, vol. 279(C).
- Xinyue Liu & Yang Gao & Kyamra Marma & Yu Miao & Lin Liu, 2024. "Advances in the Study of Techniques to Determine the Lithium-Ion Battery’s State of Charge," Energies, MDPI, vol. 17(7), pages 1-16, March.
- Qian, Wei & Li, Wan & Guo, Xiangwei & Wang, Haoyu, 2024. "A switching gain adaptive sliding mode observer for SoC estimation of lithium-ion battery," Energy, Elsevier, vol. 292(C).
- Lian, Gaoqi & Ye, Min & Wang, Qiao & Li, Yan & Xia, Baozhou & Zhang, Jiale & Xu, Xinxin, 2024. "Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments," Energy, Elsevier, vol. 293(C).
- Lai, Rucong & Wang, Jie & Tian, Yong & Tian, Jindong, 2024. "FedCBE: A federated-learning-based collaborative battery estimation system with non-IID data," Applied Energy, Elsevier, vol. 368(C).
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Keywords
Lithium-ion battery; SOC estimation; Fractional-order model; Adaptive square-root cubature Kalman filter;All these keywords.
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