Data-Driven Voltage Prognostic for Solid Oxide Fuel Cell System Based on Deep Learning
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- Hou, Qinlong & Zhao, Hongbin & Yang, Xiaoyu, 2019. "Economic performance study of the integrated MR-SOFC-CCHP system," Energy, Elsevier, vol. 166(C), pages 236-245.
- Zio, Enrico, 2022. "Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
- Damo, U.M. & Ferrari, M.L. & Turan, A. & Massardo, A.F., 2019. "Solid oxide fuel cell hybrid system: A detailed review of an environmentally clean and efficient source of energy," Energy, Elsevier, vol. 168(C), pages 235-246.
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- Jingxuan Peng & Dongqi Zhao & Yuanwu Xu & Xiaolong Wu & Xi Li, 2023. "Comprehensive Analysis of Solid Oxide Fuel Cell Performance Degradation Mechanism, Prediction, and Optimization Studies," Energies, MDPI, vol. 16(2), pages 1-23, January.
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Keywords
solid oxide fuel cell; recurrent neural network; long short-term memory; gated recurrent unit; encoder–decoder; state prediction;All these keywords.
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