Solid oxide fuel cell simulation model tuning using operating conditions dependent optimization techniques.

Area of Science:

• Electrochemistry
• Energy Systems Engineering
• Computational Modeling

Background:

• Solid Oxide Fuel Cells (SOFCs) are advanced energy conversion devices.
• Understanding SOFC dynamic behavior and optimizing performance is crucial for grid integration.
• Existing models often lack precision in dynamic response and optimization.

Purpose of the Study:

• To develop and validate a mathematical model for conventional and reversible SOFCs.
• To investigate SOFC transient voltage stability after current changes.
• To enhance SOFC model accuracy using advanced optimization algorithms.

Main Methods:

• Mathematical modeling and simulation of SOFC performance.
• Analysis of steady-state V-I polarization curves and transient voltage responses.
• Application of optimization algorithms: Walrus Optimization Algorithm (WaOA), Secretary Bird Optimization Algorithm (SBOA), Chaos Game Optimization (CGO), and Teaching-Learning Based Optimization (TLBO).

Main Results:

• WaOA demonstrated the lowest modeling error, achieving superior model calibration compared to other algorithms.
• WaOA-based tuning reduced modeling error by over five orders of magnitude versus traditional curve-fitting.
• Optimization of reactant flow rates using WaOA led to an approximate 6% increase in output power.

Conclusions:

• The proposed WaOA-based SOFC modeling approach significantly enhances accuracy and predictive capability.
• Optimized SOFC operation through WaOA leads to improved efficiency and power output.
• This research facilitates better integration of SOFCs into electrical networks through precise dynamic modeling.