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Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells

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  • Yang, Bo
  • Guo, Zhengxun
  • Yang, Yi
  • Chen, Yijun
  • Zhang, Rui
  • Su, Keyi
  • Shu, Hongchun
  • Yu, Tao
  • Zhang, Xiaoshun

Abstract

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.

Suggested Citation

  • Yang, Bo & Guo, Zhengxun & Yang, Yi & Chen, Yijun & Zhang, Rui & Su, Keyi & Shu, Hongchun & Yu, Tao & Zhang, Xiaoshun, 2021. "Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells," Applied Energy, Elsevier, vol. 303(C).
    • Handle: RePEc:eee:appene:v:303:y:2021:i:c:s0306261921009983
    DOI: 10.1016/j.apenergy.2021.117630
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    Cited by:

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    2. Dokur, Emrah & Erdogan, Nuh & Salari, Mahdi Ebrahimi & Karakuzu, Cihan & Murphy, Jimmy, 2022. "Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine," Energy, Elsevier, vol. 248(C).
    3. Aihua Tang & Yuanhang Yang & Quanqing Yu & Zhigang Zhang & Lin Yang, 2022. "A Review of Life Prediction Methods for PEMFCs in Electric Vehicles," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    4. Wang, Erlei & Xia, Jiangying & Li, Jia & Sun, Xianke & Li, Hao, 2022. "Parameters exploration of SOFC for dynamic simulation using adaptive chaotic grey wolf optimization algorithm," Energy, Elsevier, vol. 261(PA).
    5. Yaping Wu & Xiaolong Wu & Yuanwu Xu & Yongjun Cheng & Xi Li, 2023. "A Novel Adaptive Neural Network-Based Thermoelectric Parameter Prediction Method for Enhancing Solid Oxide Fuel Cell System Efficiency," Sustainability, MDPI, vol. 15(19), pages 1-17, September.
    6. Zhimin Guo & Zhiyuan Ye & Pengcheng Ni & Can Cao & Xiaozhao Wei & Jian Zhao & Xing He, 2023. "Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System," Energies, MDPI, vol. 16(6), pages 1-21, March.

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