A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications
AbstractThis paper presents a simple strategy for controlling an interleaved boost converter that is used to reduce the current fluctuations in proton exchange membrane fuel cells, with high impact on the fuel cell lifetime. To keep the output voltage at the desired reference value under the strong fluctuations of the fuel flow rate, fuel supply pressure, and temperature, a neural network controller is developed and implemented using Matlab-Simulink (R2012b, MathWorks limited, London, UK). The advantage of this controller resides in its simplicity, where limited number of tests are carried out using Matlab-Simulink to construct it. To investigate the robustness of the proposed converter and the neural network controller, strong variations of the fuel flow rate, fuel supply pressure, temperature and air supply pressure are applied to both the fuel cell and the neural network controller of the converter. The simulation results show the effectiveness and the robustness of the both the proposed controller and converter to control the load voltage and minimize the current and voltage ripples. As a result of that, fuel cell current oscillations are considerably reduced on the one hand, while on the other hand, the load voltage is stabilized during transient variations of the fuel cell inputs. View Full-Text
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Barhoumi, E.M.; Ben Belgacem, I.; Khiareddine, A.; Zghaibeh, M.; Tlili, I. A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications. Energies 2018, 11, 3423.
Barhoumi EM, Ben Belgacem I, Khiareddine A, Zghaibeh M, Tlili I. A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications. Energies. 2018; 11(12):3423.Chicago/Turabian Style
Barhoumi, El M.; Ben Belgacem, Ikram; Khiareddine, Abla; Zghaibeh, Manaf; Tlili, Iskander. 2018. "A Neural Network-Based Four Phases Interleaved Boost Converter for Fuel Cell System Applications." Energies 11, no. 12: 3423.
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