AUTHOR=Ai Xueyi , Yue Yi , Xu Haoxuan 

TITLE=Grasshopper Optimization Algorithms for Parameter Extraction of Solid Oxide Fuel Cells

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.853991

DOI=10.3389/fenrg.2022.853991

ISSN=2296-598X

ABSTRACT=The accuracy and reliability of solid oxide fuel cell (SOFC) modeling mainly depend on the precise extraction and optimization of some unknown parameters. However, the SOFC model is a multi-peak, non-linear, multi-variable and strongly combined system. In the previous decisive optimization methods, it is difficult to achieve satisfactory parameter extraction. Therefore, this work proposes a SOFC parameter extraction method based on the superhuman algorithm, and extracts several important parameters of the SOFC model. In addition, the electrochemical model (ECM), which is a typical SOFC model, has also been studied to testify the extraction performance of grasshopper optimization algorithm(GOA) under various working conditions. Simulation results based on MATLAB show that GOA can greatly improve the accuracy, speed and stability of inferring these unknown parameters’ confirmation through a comprehensive comparison with the ant lion optimizer (ALO).