1 IGEC-1 Proceedings of the International Green Energy Conference 12-16 June 2005, Waterloo, Ontario, Canada Paper No. 126 OPTIMIZATION OF A FUEL CELL SYSTEM BASED ON EMPIRICAL DATA OF A PEM FUEL CELL STACK AND THE GENERALIZED ELECTROCHEMICAL MODEL J. Wishart, M. Secanell, and Z. Dong* Department of Mechanical Engineering and Institute for Integrated Energy Systems (IESVic) University of Victoria Victoria, BC, Canada V8W 3P6 ∗ Email: zdong@me.uvic.ca G. Wang School of Mechanical Science and Engineering Jilin University, Changchun, China ABSTRACT A fuel cell system model is implemented in MATLAB in order to optimize the system operating conditions. The implemented fuel cell model is a modified version of the semi-empirical model introduced by researchers at the Royal Military College of Canada. In addition, in order to model the whole fuel cell system, heat transfer and gas flow considerations and the associated Balance of Plant (BOP) components are incorporated into the model. System design optimizations are carried out using three different methods, including the sequential quadratic programming (SQP) local optimization algorithm and simulated annealing (SA) and genetic algorithm (GA) global optimization algorithms. Using the operating conditions of the fuel cell system as the design variables, the net output power of the system is optimized. The three methods are used in order to gain some insight into the nature of the objective function and the performance of the different algorithms. The optimization results show a good agreement and provide useful information on the design optimization problem. This study prepares us for more complex modeling and system optimization research. INTRODUCTION As a promising technology that may successfully supersede the combustion of fossil fuels as the dominant method of energy-generation, hydrogen fuel cells are studied worldwide with an aim to improve the power output and lower the cost for wide-spread applications. Among various types of fuel cells, the Proton Exchange Membrane Fuel Cell (PEMFC) is arguably the fastest- growing type and the fuel cell that is most likely to be widely used in the near future. The modeling and optimization of PEMFC system, carried out in this work, is aimed at achieving better fuel cell system designs. Modeling of real-world applications has been seen as a useful tool for decades. Fuel cell modeling is in its relative infancy, but already a significant amount of effort has been put forth to understand the parameters and issues affecting the performance of the fuel cell. Fuel cell modeling requires a broad skill base, as electrochemical, thermodynamic and fluid dynamic relationships must be combined with heat transfer and mass and energy balance equations to construct an appropriate model (Heraldsson and Wipke, 2004). A balance must be found between simplifying assumptions that compromise the accuracy of the model and increasing complexity that makes the model an unworkable behemoth. Given the dubious credibility of the current fuel cell models, contemporary fuel cell optimization results must be met with skepticism. Nevertheless, some laudable attempts at fuel cell optimization can be found in the literature. Xue and Dong (1998) used a semi-empirical model of the Ballard Mark IV fuel cell and models for the auxiliary systems to create a model of the fuel cell system. The optimal active stack intersection area and air stoichiometric ratio to maximize net power output is achieved, and, at the same time, minimize production costs. Grujicic and Chittajallu (2004) used a 2D computational fuel cell dynamics model to optimize the electric current per fuel-cell width at a cell voltage of 0.7V. In the optimization sequential quadratic programming was used to obtain the operational and geometric parameters for achieving the maximum electric current, including air inlet pressures and cathode thickness, cathode length for each shoulder segment of flow channel, and fraction of cathode length associated with the flow channel. In general, optimization of fuel cell systems is still a challenge not only because of the inaccuracy of the models but because the optimization is a highly non-linear problem where the objective function is obtained using a numerical model of the fuel cell and fuel cell system. Non- linear optimization involves the search for a minimum of a non-linear objective function subject to non-linear constraints. It is common for these optimization problems to have multiple optima. Due to this difficulty, two different approaches have emerged in the area of non-linear optimization: local methods and global methods. Local methods aim to obtain a local minimum, and they cannot guarantee that the minimum obtained is the