Grasshopper Optimization Algorithms for Parameter Extraction of Solid Oxide Fuel Cells

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, nonlinear, multivariable, and strongly combined system. In the previous decisive optimization methods, it is difficult to achieve satisfactory parameter extraction. Therefore, this article 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 verify the extraction performance of the glass jump 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 through a comprehensive comparison with the particle swarm optimization (PSO) algorithm.