article · Applied Sciences
Proton exchange membrane fuel cells convert chemical energy into electricity and heat, offering low-temperature operation using noble metal catalysts. Accurate mathematical modelling of these fuel cells is important for enhancing their performance in smart microgrid deployments. A swarm-based method called the coyote optimisation algorithm has been applied to identify optimal model parameters for individual cells and complete stacks. The technique works by minimising the sum of square deviations between experimental voltage measurements and model-estimated voltages. Testing on two physical systems, a 250 W stack and a NedStack PS6 unit, evaluated the approach across varying operational conditions. Compared against four alternative optimisation algorithms under identical criteria, the coyote optimisation method demonstrated superior parameter estimation accuracy and robust statistical consistency.
Fuel cells are increasingly integrated into local electricity networks and smart microgrids. To deploy them effectively, engineers require precise mathematical representations of their operational behaviour. Improving parameter estimation ensures that digital models reflect real-world fuel cells accurately, supporting better control, performance monitoring, and integration of hydrogen-based power generation into modern energy distribution systems.
This tool is relevant to microgrid developers, energy management engineers, and fuel cell system integrators needing accurate simulation models for operational planning. The technology is applied and tested on practical hardware, specifically a 250 W stack and NedStack PS6, but remains an algorithmic modelling method. Real-world commercial use would require embedding the algorithm into commercial engineering software or microgrid control platforms.
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In recent years, the penetration of fuel cells in distribution systems is significantly increased worldwide. The fuel cell is considered an electrochemical energy conversion component. It has the ability to convert chemical to electrical energies as well as heat. The proton exchange membrane (PEM) fuel cell uses hydrogen and oxygen as fuel. It is a low-temperature type that uses a noble metal catalyst, such as platinum, at reaction sites. The optimal modeling of PEM fuel cells improves the cell performance in different applications of the smart microgrid. Extracting the optimal parameters of the model can be achieved using an efficient optimization technique. In this line, this paper proposes a novel swarm-based algorithm called coyote optimization algorithm (COA) for finding the optimal parameter of PEM fuel cell as well as PEM stack. The sum of square deviation between measured voltages and the optimal estimated voltages obtained from the COA algorithm is minimized. Two practical PEM fuel cells including 250 W stack and Ned Stack PS6 are modeled to validate the capability of the proposed algorithm under different operating conditions. The effectiveness of the proposed COA is demonstrated through the comparison with four optimizers considering the same conditions. The final estimated results and statistical analysis show a significant accuracy of the proposed method. These results emphasize the ability of COA to estimate the parameters of the PEM fuel cell model more precisely.
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DOI: 10.3390/app11052052
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