PEM fuel cell voltage-tracking using artificial neural network

Rakhtala Rostami, Seyed Mehdi; Ghaderi, R.; Ranjbar, A.; Fadaeian, T.; Niaki, S. Ali Nabavi

Abstract

Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cell. The delay exciting in auxiliary equipments in fuel cell system such as pumps, heaters, back pressures will degrade system performance and lead to problems in controlling tuning parameters including temperature, pressure and flow rate. To overcome this problem, using fast and delay-free systems for predicting control signals is inevitable. Neural network model is proposed to control the stack terminal voltage as a proper constant and improve system performance. This is done through input air pressure control signal. The proposed artificial neural network is constructed based on back propagation network. A fuel cell nonlinear model with and without feed forward control is investigated and compared under random current variations. Simulation results have shown that, applying neural network feed forward control can successfully improve system performance in tracking output voltage. Furthermore consuming less energy and simpler control system are the other advantages of the proposed control algorithm. ©2009 IEEE.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2009 IEEE Electrical Power and Energy Conference, EPEC 2009
• Start Date: Aug 22, 2009
• End Date: Aug 23, 2023
• Online Publication Date: Mar 4, 2010
• Publication Date: Mar 4, 2010
• Deposit Date: Aug 15, 2023
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2009 IEEE Electrical Power & Energy Conference (EPEC)
• ISBN: 9781424445080
• DOI: https://doi.org/10.1109/EPEC.2009.5420935
• Public URL: https://uwe-repository.worktribe.com/output/11001594