期刊
APPLIED ENERGY
卷 303, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.117630
关键词
Solid oxide fuel cell; Parameter extraction; identification; Extreme learning machine; Meta-heuristic algorithm; Artificial neural network; Optimization methods
资金
- National Natural Science Foundation of China [61963020, 51907112]
- Key Program of National Natural Science Foundation of China [52037003]
- Natural Science Foundation of China-Smart Grid Joint Fund of State Grid Cor-poration of China [U2066212]
- Major Special Project of Yunnan Province of China [202002AF080001]
A novel method based on extreme learning machine is proposed for extracting unknown parameters of solid oxide fuel cell models, with thorough investigation and significant improvement in accuracy under two typical operation conditions.
A precise, fast, and robust parameter extraction technique of solid oxide fuel cell models is extremely crucial for optimal control and behavior analysis. In this paper, a novel extreme learning machine based method is proposed to extract unknown parameters of solid oxide fuel cell models including electrochemical model and simple electrochemical model. At first, extreme learning machine is applied to overcome two thorny obstacles (e.g., data shortage and noised data) via predicting additional data and updating noised data. Then, both original data collected from a 5 kW solid oxide fuel cell stack and processed data are transferred to effectively guide eight prominent meta-heuristic algorithms for effective parameter extraction. The performance of extreme learning machine is thoroughly investigated in two typical operation conditions through a comprehensive comparison based on various training data. Simulation results validate that the proposed approach can effectively contribute to searching efficient model parameters along with high accuracy, prominent stability, high speed, and great robustness. Particularly, the accuracy of parameter extraction for electrochemical model and simple electrochemical model can be improved by 49.3% and 65.6% at most, respectively.
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