Nonlinear Modelling and Control based on Adaptive Fuzzy Technique for PEMFC

W. Dong, G.-Y. Cao, and X.-J. Zhu


Proton exchange membrane fuel cell (PEMFC), adaptive neuralnetworks fuzzy infer system (ANFIS), adaptive neuralnetworks learning algorithm (ANA), adaptive neuralnetworks fuzzy controller


The operating temperature of proton exchange membrane fuel cells (PEMFC) stack is a very important control variable, which affects the electrochemical reactions and humidity of the proton exchange membrane. The variation of the operating temperature also has a signi´Čücant influence on the performance and lifespan of the fuel cells. Most of the existing PEMFC models are principle models that may not be suitable for designing control systems. In this article, an adaptive neural-network fuzzy identification model of PEMFC stack is developed based on the sampled data and operating experience. A novel adaptive fuzzy control procedure for the operating temperature is also developed. Finally, the simulation and experiment results of the control algorithm are presented. The results show that the proposed modelling and design procedure has better results than traditional PID and fuzzy control algorithms.

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