Title :
Modeling of a Fuel Cell Stack by Neural Networks Based on Particle Swarm Optimization
Author :
Hu, Peng ; Cao, Guang-yi ; Zhu, Xin-jian ; Li, Jun ; Ren, Yuan
Author_Institution :
Inst. of Fuel Cell, Shanghai Jiao Tong Univ., Shanghai
Abstract :
This paper presented a nonlinear voltage modeling procedure of a proton exchange membrane fuel cell (PEMFC) stack by neural networks based on particle swarm optimization (PSO). PEMFC stack is a complex nonlinear system which is hard to model by traditional ways. So neural networks based on particle swarm optimization (PSONN) was developed to identify a nonlinear PEMFC stack voltage model. In the paper, the PSO algorithm trained the connection weights and thresholds of neural networks, and a neural networks nonlinear autoregressive model with exogenous inputs was applied in modeling PEMFC stack voltage model. The simulation indicated that the PSONN model can efficiently approach the behavior of a PEMFC stack.
Keywords :
autoregressive processes; neural nets; particle swarm optimisation; power engineering computing; proton exchange membrane fuel cells; complex nonlinear system; fuel cell stack modeling; neural networks; nonlinear PEMFC stack voltage model; nonlinear autoregressive model; nonlinear voltage modeling; particle swarm optimization; proton exchange membrane fuel cell stack; Biomembranes; Computational modeling; Fuel cells; Gases; Mathematical model; Neural networks; Nonlinear systems; Particle swarm optimization; Protons; Voltage;
Conference_Titel :
Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-2486-3
Electronic_ISBN :
978-1-4244-2487-0
DOI :
10.1109/APPEEC.2009.4918500