Abstract
Operating conditions such as temperature and pressure affect the fuel cell performance. Proton Exchange Membrane (PEM) fuel cells work at its maximum efficiency when it is operated within temperature limits. To improve the heat transfer properties of PEM fuel cells, nano coolants are added. Identification of a suitable combination of thermal conductivity, relative humidity, and parametric coefficients for performance assessment of PEM fuel cells in real time is mandatory. Conventional methods follow complex computations and trial and error procedure to model fuel cell without thermal management parameters. Hence, in this paper, the objective of parameter set identification is framed as an optimization problem and solved using Genetic Algorithm method. The performance of the proposed approach is evaluated for different test conditions and validated through simulation findings.
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