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Number of items: 4.

Zhao, Kai, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Ming, Wenlong ORCID: https://orcid.org/0000-0003-1780-7292 and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2025. Digital twin-supported battery state estimation based on TCN-LSTM neural networks and transfer learning. CSEE Journal of Power and Energy Systems 11 (2) , pp. 567-579. 10.17775/CSEEJPES.2024.00900
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Zhao, Kai, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714, Ming, Wenlong ORCID: https://orcid.org/0000-0003-1780-7292 and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2025. A Hierarchical and Self-Evolving Digital Twin (HSE-DT) method for multi-faceted battery situation awareness realisation. Machines 13 (3) , 175. 10.3390/machines13030175
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Zhao, Kai 2024. Digital twin battery storage in energy storage system. PhD Thesis, Cardiff University.
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Zhao, Kai, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Ming, Wenlong ORCID: https://orcid.org/0000-0003-1780-7292, Zhou, Yue ORCID: https://orcid.org/0000-0002-6698-4714 and Wu, Jianzhong ORCID: https://orcid.org/0000-0001-7928-3602 2022. Digital twin-driven estimation of state of charge for Li-ion battery. Presented at: IEEE International Energy Conference (ENERGYCON 2022), Riga, Latvia, 09-12 May 2022. Proceedings of 2022 IEEE 7th International Energy Conference (ENERGYCON). IEEE, 10.1109/ENERGYCON53164.2022.9830324
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