Proton Exchange Membrane Fuel Cell Dynamic Modeling And Control Research

Proton exchange membrane fuel cell a kind of green energy in 21 century.Working temperature is an important measure of PEMFC performance indexes. Increasesing it is good for chemical reaction rate also reduces the ohms polarization but higher temperature will accelerate water loss. Low temperature will cause electrical pile affected and electrochemical reaction rate reduced.Therefore appropriate working temperature can increase fuel cell performance.This paper have done research on PEMFC modeling and temperature control,and design the corresponding temperature control system, the main research contents below:Introduce the mathematical model of the fuel cells and analyzes various influence factors of fuel cell performance.With single battery for research object, combined with heat and mass established PEMFC model.Estabish model with the components in Simulink, based on the results of simulation, the parameter model is effective for PEMFC, and further control methods research.Combine the neural network with the fuzzy control and advance a kind of modeling.This method will adjust the fuzzy rules and parameter by the fuzzy C-means method.Compared with traditional fuzzy control,this method improve the control system speed and accuracy,also will enhance the system robustness.Compared to the pure neural network, the FCP system can make full use of expert language information and experience knowledge so that the choice of initial parameters will be more easier.Take the FCP neural network in to the application of PEMFC temperature control system design. Through the learning ability of neural network to optimize the fuzzy logic rules of the experience, so as to achieve the effective control of temperature electric. The results show that the temperature control system has good robustness and good control quality, can satisfy the parameter time-variance PEMFC electricity pile of temperature control system requirements.