Model parameter estimation of SOFCs using a modified cat optimization algorithm

The energy crisis is one of the topics that is always considered by human societies today. On the other hand, the use of new energy and energy conversion systems with high efficiency and low pollution, such as fuel cells, has received much attention. Due to the cost and time-consuming construction of fuel cells, their design and simulation are very important. The purpose of this study is to provide a methodology for optimal estimation of the unknown petameters in solid oxide fuel cells (SOFCs). The purpose is to minimize the error value between the experimental output voltage data of a practical 96 cell SOFC and the output voltage data of the designed model by considering seven main uncertain parameters as decision variables. Here, a modified version of the Cat Optimization Algorithm is utilized for minimization and to deliver higher accuracy and convergence speed for SOFC model parameter estimation. The model is finally applied to a case study and its results are compared with some state of the art methods, including Converged Grass Fibrous Root Optimization Algorithm (CGFROA), Modified African Vulture Optimization algorithm (MAVO), Chaotic Binary Shark Smell Optimization (BSSO), and Coyote Optimization Algorithm (COA).