Optimum Conditions for Maximum Power of a Direct Methanol Fuel Cell

It is well known that anode and cathode pressures and cell temperature are the effective parameters in performance of Direct Methanol Fuel Cell (DMFC). In the present study, the genetic algorithm as one of the most powerful optimization tools is applied to determine operating conditions which result in the maximum power density of a DMFC. A quasi-two-dimensional, isothermal model is presented to determine maximum power of a DMFC. For validation of this model, the results of the model are compared to experimental results and shown to be in good agreement with them. 1. Introduction Direct methanol fuel cell is one of the most promising transportable power sources which can be used in mobiles, laptops, and small power generation [1]. DMFC is well known to be influenced by large numbers of parameters such as flow rate, methanol concentration, operating temperatures, and anode and cathode pressures. In order to improve the performance of the DMFC, it is necessary to determine the effects of various parameters on the performance of the fuel cell. Other parameters include type and thickness of membrane, catalyst type, geometrical parameters of the flow field, and the type of the gas diffusion layer. However, while these parameters are very important, they are not operating variables. Therefore, they are not variable during fuel cell use. The fuel cell performance has been the subject of several research papers. Xu et al. [2] used numerical simulation to establish a relationship between the feed concentration of fuel and power density at a certain current density, and they also identified the optimal feed concentration. Moreover, Wang et al. [3] focused on the effect of cell temperature and oxygen flow rate on cell response using the experimental setup. The main important parameters which affect the performance of the DMFC are operating parameters such as operating temperature and pressure in both sides of the cathode and anode. More recent research has focused on the values of these parameters as derived by experimental approaches, but, in current research, these parameters are optimized using genetic algorithm (GA) and a DMFC analytical model for creating the maximum power as the fitness function of the GA [4]. 2. Genetic Algorithm Genetic algorithm is an artificial system and it is similar to human genetic system. GA is a parallel mathematical algorithm that transforms a set of population (namely, chains of chromosomes using genetic operations) into a new population (namely, a next generation) based on the fitness of each chromosome [4, 5]. The basic