Parameters identification of solid oxide fuel cell for static and dynamic simulation using comprehensive learning dynamic multi-swarm marine predators algorithm

An accurate identification of the parameters of solid oxide fuel cell (SOFC) models is the first step to provide a reliable design for an energy storage system using SOFC. Therefore, in the current work, a novel developed variant for the marine predators algorithm (MPA) is proposed based on comprehensive learning and dynamic multi-swarm approaches to extract highly accurate, precise, and efficient parameters of the SOFC model that achieve the closely matching between the actual and estimated system responses. The proposed comprehensive learning dynamic multi-swarm marine predators algorithm (CLDMMPA) is examined with two scenarios that are SOFC steady-state and dynamic state-based models under variable operating conditions. The results of the proposed algorithm are validated via an intensive comparison based on statistical metrics and non-parametric tests with other recent counterparts. Furthermore, the accuracy of identified parameters in the case of the dynamic model is evaluated with two cases of sudden power load variations, and the dynamic responses of the stack voltage and current are analyzed. The comparisons and analyses have confirmed the superiority of the proposed CLDMMPA to provide highly accurate identified parameters that exhibit the minimum deviation between the measured and estimated stack current-voltage and stack current-power curves. Moreover, the consistency of the CLDMMPA results and the smooth decaying in its convergence curves are other remarkable points superior to other counterparts.

Automated summary

The proposal of CLDMMPA provides highly accurate identified parameters for the SOFC model, achieving a close matching between actual and estimated system responses. The algorithm is validated under different operating conditions and exhibits superiority in the dynamic model.