Abstract
Transients in load and consequently in stack current have a significant impact on the performance and durability of fuel cells. The delays in auxiliary equipments in fuel cell systems (such as pumps and heaters) and back pressures degrade system performance and lead to problems in controlling tuning parameters including temperature, pressure, and flow rate. To overcome this problem, fast and delay-free systems are necessary for predicting control signals. In this paper, we propose a neural network model to control the stack terminal voltage as a proper constant and improve system performance. This is done through an input air pressure control signal. The proposed artificial neural network was constructed based on a back propagation network. A fuel cell nonlinear model, with and without feed forward control, was investigated and compared under random current variations. Simulation results showed that applying neural network feed forward control can successfully improve system performance in tracking output voltage. Also, less energy consumption and simpler control systems are the other advantages of the proposed control algorithm.
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Rakhtala, S.M., Ghaderi, R. & Ranjbar Noei, A. Proton exchange membrane fuel cell voltage-tracking using artificial neural networks. J. Zhejiang Univ. - Sci. C 12, 338–344 (2011). https://doi.org/10.1631/jzus.C0910683
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DOI: https://doi.org/10.1631/jzus.C0910683