University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 16 4 2025 12 22 Maximum Power Tracking in Proton Exchange Membrane-Based Fuel Cell Using Neural Network Trained with Metaheuristic Optimization Algorithm Maximum Power Tracking in Proton Exchange Membrane-Based Fuel Cell Using Neural Network Trained with Metaheuristic Optimization Algorithm 1 16 30204 10.22108/isee.2025.145950.1750 FA Mohamad Abedini Associate Professor, Department of Electrical Engineering, Ayatollah Boroujerdi University, Boroujerd, Iran Journal Article 2025 07 13 Considering the limited ability of fuel cells to produce energy, it is necessary to provide solutions in which the efficient power generated by fuel cells can be achieved. Therefore, there is a need for maximum-power-tracking methods that adjust the duty cycle of the boost converter in fuel cells. Thus, in this paper, a new power tracking method has been used that is based on the combination of neural networks and a meta-heuristic algorithm called frog jump optimization to overcome the problems of conventional methods caused by rapid changes in the operating point and power fluctuations. The proposed method has been proposed in order to perform quickly and increase the efficiency achievable from proton exchange membrane-based fuel cells. The modeling results have been presented in the MATLAB environment and compared with several different power tracking methods. The results show that the proposed method shows less than one percent error in the three evaluated temperatures for tracking the maximum power compared to the actual power of the fuel cell, and is also robust to input changes in the fuel cell. Considering the limited ability of fuel cells to produce energy, it is necessary to provide solutions in which the efficient power generated by fuel cells can be achieved. Therefore, there is a need for maximum-power-tracking methods that adjust the duty cycle of the boost converter in fuel cells. Thus, in this paper, a new power tracking method has been used that is based on the combination of neural networks and a meta-heuristic algorithm called frog jump optimization to overcome the problems of conventional methods caused by rapid changes in the operating point and power fluctuations. The proposed method has been proposed in order to perform quickly and increase the efficiency achievable from proton exchange membrane-based fuel cells. The modeling results have been presented in the MATLAB environment and compared with several different power tracking methods. The results show that the proposed method shows less than one percent error in the three evaluated temperatures for tracking the maximum power compared to the actual power of the fuel cell, and is also robust to input changes in the fuel cell. https://isee.ui.ac.ir/article_30204_d215c4b33b8c7015784714f6709e810c.pdf