The Modeling And Simulation For Solid Oxide Fuel Cell

Because Fuel Cell doesn't influenced by Garnot cycle, has high energy conversion efficiency, its main emission is water (depends on reactants) and produces slight pollution, Fuel Cell has been one of the most important energy installation in 21st century. In recent years, a growing interest in the Fuel Cell technology has emerged; Fuel Cell has good application prospect in a number of market areas. Besides possess the advantages of high efficiency, cleanness, low noisiness, strong load capacity, SOFC also has the merit of no leakage, no electrolyte erosion, high comprehensive efficiency and long utilization span. FC not only can use H2 as fuel, but also can use natural gas, CO and other fuels as fuel, so it will hopefully be developed into large-scaled electricity generation equipment to substitute the firepower electricity generation equipment which is used now.At the present time, researchs which have been carried on SOFC are mainly on chemistry, material and structure, there's little on the control of SOFC, but it is a necessary procedure before the practicality and industrialization of SOFC. In order to improve the design and performance of SOFC system, it is necessary to carry out a deep investigation on the electric characteristic of SOFC. Nevertheless, SOFC work at high temperature, closed and complicated condition, its inner states are difficult to investigate, the cost for experiment is very high and sometimes it is impossible for experiment. Presently, only a few parameters and variables have been investigated and analyzed in a small way. Comparatively, the cost for numerical analysis is lower. Using numerical analysis method to establish mathematic model of SOFC, then carry on simulation, lay the theory foundation for the control of SOFC.According to FC mechanism and experiment data, the electric characteristic model of SOFC is built, the relationship between output performance and related factors is analyzed. A Radial Basis Function Neural Network (RBFNN) is used to set up the electric output identification model. For improved genetic algorithm has quicker convergence rate and higher precision, an improved genetic algorithm—AGA based RBFNN, a corresponding identification model of SOFC voltage at different temperatures is developed, to solve the problem of precision reduction caused by model simplification and input parameter inaccuracy. The main achievements of this thesis are given below:*Based on the FC mechanism, the relationship among voltage, fuel utilization and current density is theoretically analyzed, diversified polarizations are analyzed in detail as well. The relationship and influence between output performance and related factors are investigated deeply. The factors of temperature and current density are taken into consideration in this thesis.*The electric output identification model is established, using the identification method of RBFNN, the model should dynamic simulate the voltage/current density relationship curve, to realize the nonlinear dynamic mapping of input to output.*Based on the study of advantages and disadvantages of standard genetic algorithms, an improved genetic algorithm—AGA, to optimize input parameters(the centers of concealed layer of RBFNN, the widths of concealed layer of RBFNN, the output values of RBFNN). The final simulation proved that the model is practical, which can provide theory foundation for the future control of SOFC.