A survey on parameters estimation of the proton exchange membrane fuel cells based on the swarm-inspired optimization algorithms

The main purpose of this study is to review various swarm-inspired optimization algorithms to discuss the significance of some established works in this area. Accurate parameter estimation is required to guarantee proper modeling of PEMFCs. However, because PEMFC models are complex, non-linear, and multivariate, parameter estimation is quite difficult. To estimate the linear and non-linear parameters of a PEMFC model in real time, this work investigates PEMFC model parameters estimation methods with a focus on online identification algorithms, which are thought of as the foundation of designing a global energy management strategy. Various PEMFC models with various classifications and objectives are initially addressed in this regard. The parameters of two well-known semi-empirical models in the literature, including 500 W BCS PEMFC and the 6 kW NedSstack PS6 PEMFC have then been identified using some potential swarm-inspired optimization algorithms for practical applications, such that the TSD error for the NedStack PS6 and BCS PEMFC based on the swarm-inspired optimization algorithms provide averagely 2.22 and 0.047, respectively. Finally, the obtained accomplishments and upcoming difficulties are highlighted.