Mathematic and optimum design of direct methanol fuel cell model based on intelligent optimization algorithms

A one dimensional mathematic direct methanol fuel cell model (DMFC) is established based on the basal principle of the DMFC transport phenomena and electrochemistry kinetics, the nonlinear ordinary differential equations of which is solved numerically. The result implies that the power density of DMFC changes along with the anode methanol concentration and the current density. Three intelligent optimization algorithms which are direct Monte Carlo algorithm, simulated annealing algorithm and genetic algorithm, is used to optimize the DMFC design. The result implies that the maximal power density of the DMFC is 0.042933 W/cm² when the anode methanol concentration is 0.663 mol/L and the current density is 0.229 A/cm². Compared with the result of these three intelligent optimization algorithms, the efficiency of genetic algorithm is better than direct Monte Carlo algorithm and simulated annealing algorithm.