SOFC model parameter identification by means of Modified African Vulture Optimization algorithm

Abstract

A new efficient technique for the best selection of the unknown variables in the Solid Oxide Fuel Cell (SOFC) stack models is proposed in this paper. The main concept in this paper is the minimization of the sum of squared error values between the empirical voltage and current profile and the obtained voltage and current profiles from the method. The minimization process is defined by a new improved metaheuristic, which is the Modified African Vulture Optimizer (MAVO). The MAVO algorithm is designed to modify the algorithm and achieve results with better effectiveness as concerns convergence and accuracy. For determining the system consistency, two scenarios based on pressure and temperature variations are investigated. The technique has been finally compared with several other techniques to verify its prominence. The results show the minimum value of the SSE under different temperatures, equal to 1.87 e−4, and the minimum value of the MSE under different temperatures, equal to 1.24 e−3. This indicates promising results for the proposed method as a proper identification system. Final achievements indicate that the suggested approach provides outstanding effectiveness toward the compared methods.

Keywords

• Parameter identification
• System modeling
• Fuel cell
• Solid Oxide Fuel Cell
• Modified African Vulture Optimization algorithm