Dynamic And Static Analysis And Control Of SOFC System For The Goals Of High System Efficiency And Low NO_x Emission

Due to its advantages of high efficiency and low emission,solid oxide fuel cell(SOFC)stands out among many emerging energies.SOFC system’s working state determines whether it can be really used.In order to make the system work in the optimal state,several key issues need to be solved.Firstly,the optimal input operating point of the system should be found.The thermal output of the system at this operating point must meet the constraint conditions.It is the prerequisite for long-life and safe operation.Regarding the operation effect,most studies use system efficiency as the evaluation index.However,it is found that the system will inevitably emit nitrogen oxides(NO_x)when system is running.NO_x is very harmful to the environment.In order to make the system run with high efficiency and low emission,the search process for the optimal operating point should comprehensively consider the two evaluation indexes of system efficiency and NO_x emission.Secondly,the fast load tracking capability determines whether the system can be adapted to complex working conditions.Control strategy needs to be designed to achieve fast load tracking.In order to solve the above problems,this thesis takes the 5k W pure hydrogen SOFC system as the research object.Through statically and dynamically analyzing and controller designing,the SOFC system can run safely,greenly,efficiently and quickly.The system operation effect has been optimized.Firstly,this thesis builds a Simulink model of 5k W pure hydrogen SOFC system.In order to simulate the process of NO_x emission numerically,a NO_x emission model has been built based on the Zeldovich principle.The two models are integrated according to the system connection mode,and a SOFC system model considering NO_x emissions is obtained.Then according to experimental data,the validity of the model is verified.Then the static characteristics of the system have been analyzed based on the model.It is found that there is a strong coupling relationship between thermal characteristics,system efficiency and NO_x emission,especially the system efficiency and NO_x emission.The trends of the two evaluation indexes are opposite when the air flow in the operating point is changed.To solve this problem,an analysis method for finding the optimal operating point with high efficiency and low NO_x emission is obtained by designing the genetic-particle swarm algorithm and optimizing objective function in the algorithm.Furthermore,the dynamic characteristics of the system during power switching are analyzed.And an open-loop power switching strategy has been designed.The problems of"fuel shortage"and"NO_x emission increase"and so on which may occur during the power switching process can be solved by using the switching strategy.The best strategy parameters have been found through the combination of genetic-particle swarm algorithm and BP neural network.It makes foundation for controller design.Finally,a sliding mode controller has been designed.Output power of the system is the controlled object.The design of the sliding surface refers to the results of the static and dynamic analysis,so it can make the system work at the optimal state.The system can also achieve fast load tracking.