Modeling And Control Based On Proton Exchange Membrane Fuel Cell System Neural Network Identification Model

Proton exchange membrane fuel cell is considered as one of the best energy and a method of hydrogen use. So, research and development of fuel cell power technology has become a study hot.Proton exchange membrane fuel cell (PEMFC) is a new fuel cell that develops after the alkaline fuel cell, the phosphoric acid fuel cell, the molten carbonate fuel cell and the solid oxide fuel cell.As the PEMFC technology become more and more mature and commercialize, PEMFC system generate electricity performance must be strongly assurance. Output voltage is the most important parameter of PEMFC and operation temperature has vital influence on PEMFC performance. Based on PEMFC experimental and published data, we developed a PEMFC stack dynamic parameter model. Based on said parameter model, we obtain voltage identification model and temperature identification model. Based on identification models, PEMFC output voltage and temperature control problems are studied. The main achievements and contributions are summarized as follows:(1) Based on opened literatures and research results, using PEMFC stack major parameters as variables, combining mechanism model and experimental model, we develop a blocking parameter model of PEMFC which includes charge double layer capacitance dynamics submodel, cathode channel dynamics submodel, and stack temperature dynamics submodel. A great deal simulation results show that the model is enough to reflect the PEMFC stack performance.(2) We develop a PEMFC output voltage identification model based on RBF neural-network. Using an improved self-adaptive simplest structure algorithm train RBFNN, we get a high accuracy network with rather short time. With current density as disturber, the control systems control the output voltage at an expected operating point by adjust anode gas value and cathode gas value. We design an adaptive fuzzy neural-network controller and a fuzzy PID controller to have a control study on the PEMFC output voltage. Simulation results show that the two controllers both can stabilize the PEMFC's output voltage in an expectant value. Compare the two controllers, the first one has better performance.(3) PEMFC temperature control problem is studied. We develop a PEMFC temperature identification model based on fuzzy-neural network. Based on said temperature identification model, we design an adaptive fuzzy neural-network controller and a neural-network PID controller to have a control study on PEMFC temperature. The objective of the controller is to ensure the PEMFC system work in a proper temperature extent, and reduce fluctuate range as large as possible. The simulation results of the identification models show that the models can reflect PEMFC's characteristics correctly and the first controller has higher performance.