New optimized technique for unknown parameters selection of SOFC using Converged Grass Fibrous Root Optimization Algorithm

This paper presents an optimal technique for parameter estimation of a Solid Oxide Fuel Cell (SOFC) model. The idea is to minimize the Sum of Squared Error (SSE) between the output voltage and the experimental data. To achieve this purpose, a new metaheuristic, called the Converged Grass Fibrous Root Optimization Algorithm (CGRA) is presented and is validated by comparing it with some well-known algorithms. The method is then applied to the optimal parameter estimation of the model To analyze the method accuracy and its robustness, the proposed model is verified under different pressure and temperature operating conditions and the results have been compared with some different methods from the literature. Simulation results indicate a good confirmation between the experimental results and the model designed based on the proposed CGRA. The results for the CGRA show that the SSE results for 3 atm constant pressure and 563.85°C, 649.85°C, 699.85°C, 749.85°C, and 799.85°C are 1.67E−4, 1.84E−4, 9.42E−4, 1.87E−3, and 1.62E−3, respectively and for 799.85°C constant temperature with 1 atm, 2 atm, 3 atm, 4 atm, and 5 atm, are 1.68E−3, 1.84E−3, 9.42E−3, 1.87E−3, and 1.62E−3, respectively that are the minimum values among the other analyzed methods that indicate that the suggested technique gives better efficiency with the highest robustness and convergence speed compared with the other methods.