Comparison among different recent metaheuristic algorithms for parameters estimation of solid oxide fuel cell: Steady-state and dynamic models

The first stage in designing an efficient solid oxide fuel cell (SOFC) model is to accurately determine its unknown parameters. The modeling and analysis of the steady-state and dynamic responses of SOFC models require precise and reliable parameter identification. As a result, this paper proposes four novel metaheuristic optimization algorithms to extract the parameters of a reliable SOFC and build an accurate model whose response matches the actual measured response. The paper considers the following optimization algorithms: Gorilla troops optimizer (GTO), Honey badger algorithm (HBA), African vultures optimization algorithm (AVOA), and Chimp optimization algorithm (ChOA). These recent algorithms have the benefits of providing a suitable balance between the global exploration and the local phases of exploitation that improve the population variety and efficiently reach a global optimal solution. These algorithms are used to determine the optimal parameters of the SOFC steady-state model using a set of a Siemens cell experimental dataset at various operating temperatures. Moreover, a Proportional-Integral-Derivative (PID) controller is proposed in the dynamic model to improve the transient response of a Siemens 100 kW stack voltage and current at different power levels. Furthermore, extensive comparisons based on statistical analysis are used to validate the results of the proposed optimizers. The proposed optimizers are then compared with other optimization techniques presented in previous literature. The results show that the GTO and HBA optimizers outperform other algorithms in determining the best SOFC parameters that produce accurate voltage-current and power-current characteristic curves.