The performance of a fuel cell system is determined by the amount of current density the stack is able to produce; at given chemical conditions and geometry, the leading parameters in a Proton Exchange Membrane fuel cell behavior are the exchange current density, both at anode and cathode, as well as the temperature and relative humidity at the anode. Starting from these considerations, a surrogate model is defined and validated. A Multidisciplinary Design Optimization process based on such surrogate model is presented. Some approximation methods are tested and compared to reduce the computational time. The paper focuses on two unconstrained single-objective optimization processes to find the best solution in terms of maximum current density produced at a given voltage. A final validation of the optimized outputs is performed.

Proton Exchange Membrane Fuel Cell Performance Estimate through a Multidisciplinary Design Optimization approach / Testa, Enrico; Maggiore, Paolo; Pace, Lorenzo; DALLA VEDOVA, MATTEO DAVIDE LORENZO. - 47:(2015), pp. 181-186. ((Intervento presentato al convegno 6th International Conference on Theoretical and Applied Mechanics (TAM '15) tenutosi a Salerno nel June, 27-29. 2015.

Proton Exchange Membrane Fuel Cell Performance Estimate through a Multidisciplinary Design Optimization approach

TESTA, ENRICO;MAGGIORE, Paolo;PACE, LORENZO;DALLA VEDOVA, MATTEO DAVIDE LORENZO
2015

Abstract

The performance of a fuel cell system is determined by the amount of current density the stack is able to produce; at given chemical conditions and geometry, the leading parameters in a Proton Exchange Membrane fuel cell behavior are the exchange current density, both at anode and cathode, as well as the temperature and relative humidity at the anode. Starting from these considerations, a surrogate model is defined and validated. A Multidisciplinary Design Optimization process based on such surrogate model is presented. Some approximation methods are tested and compared to reduce the computational time. The paper focuses on two unconstrained single-objective optimization processes to find the best solution in terms of maximum current density produced at a given voltage. A final validation of the optimized outputs is performed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2615210
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