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

• Published in: Applied energy 2021-12, Vol.303, p.117630, Article 117630
• Main Authors: Yang, Bo, Guo, Zhengxun, Yang, Yi, Chen, Yijun, Zhang, Rui, Su, Keyi, Shu, Hongchun, Yu, Tao, Zhang, Xiaoshun
• Format: Article
• Language: English
• Subjects: Artificial neural network; Extreme learning machine; Meta-heuristic algorithm; Optimization methods; Parameter extraction/identification; Solid oxide fuel cell
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2021.117630

•Extreme learning machine is employed to predict data and filter noised data.•Data shortage and noised data obstacles for parameter extraction are solved.•Eight meta-heuristic algorithms are employed to two solid oxide fuel cell models.•Practical performance of the proposed method is validated in the 5 kW cell stack.•Two constructive recommendations are proposed for future development. 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.