Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models

Great challenges face many researchers in constructing an equivalent circuit for solid oxide fuel cell as the constructed model should converge to the actual one. There is lack of some parameters in the datasheet provided by the manufacturer. This paper proposes a new methodology based on a recent political optimizer to solve the problem of identifying the unknown parameters of fuel cell equivalent circuit. Six parameters are considered as design variables which are E, A, J_o, R_Ω, B, and J_max, the sum of mean squared error between the measured and estimated stack voltages is considered as the fitness function to be minimized. Two scenarios of the fuel cell operation are implemented, the first one is steady-state model and the second one is the transient/dynamic-state based model, both scenarios are analyzed at different operating conditions. In a dynamic-state based model, two load disturbances are applied and the performance of the constructed model is investigated. Moreover, comparison with other reported approaches and programmed algorithms of grey wolf optimizer, Harris hawks optimizer, multi-verse optimizer, antlion optimizer, and marine predators algorithm is presented. Furthermore, the statistical parameters of best, worst, mean, median, variance and standard deviation for each optimizer are calculated. In the steady-state based model, the minimum fitness function is 1.571e-06 obtained via the proposed approach for operation at 1173 K. In dynamic-based model, the best obtained error via the proposed PO is 1.8697. The results confirmed the preference, robustness, and competence of the proposed methodology in estimating the parameters of SOFC equivalent circuit.