Researchers from Northern Border University developed an accurate model of PEM fuel cells that offers accurate results of modeling and simulation in a steady-state condition
Increasing concern regarding global warming and depleting fossil fuels has led to high adoption of renewable energy sources that offer clean energy. A fuel cell is a renewable energy storage apparatus that converts chemical potential energy (energy stored in molecular bonds) into electrical energy. Some of the types of fuel cells include, proton exchange membrane fuel cells (PEMFCs), phosphoric acid fuel cells, solid acid fuel cells, alkaline fuel cells, solid oxide fuel cells, molten carbonate fuel cells, electric storage fuel cells, and polymer exchange membrane (PEM) fuel cells. PEM fuel cells can find application in the transportation sector.
Developed in 2019, Atom Search Optimization (ASO) is a new optimization approach for solving optimization problems. Now, a team of researchers from Northern Border University used ASO to generate the obscure parameters of two commercial PEM fuel cells. To demonstrate the performance of the proposed ASO-based methodology, the team conducted validations and comparisons. Two models of the PEM fuel cells were tested to legalize the proposed ASO-based methodology in a steady-state condition. In test case 1, a model of SR-12 500 W modular PEM fuel cell was tested to regularize the performance of ASO, whereas in test case 2, the model of the 250 W PEM fuel cell stack was tested.
The team found that various plots and insignificant values of sum of the squared error demonstrated good fittings between the measured and computed voltages. The viability and qualification of the proposed ASO-based methodology was demonstrated through the comparisons between the obtained results by the ASO and other recent challenging optimization method-based results. The team also conducted performance measures that reprove the effective performance and robustness of the ASO-realized results. According to the researchers, ASO can be used to optimize engineering complicated problems. The research was published in the journal MDPI Energies on May 17, 2019.