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Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory
Faculty
Engineering
Year:
2023
Type of Publication:
ZU Hosted
Pages:
Authors:
Hytham Saad Mohamed Ramadan
Staff Zu Site
Abstract In Staff Site
Journal:
Energies MDPI
Volume:
Keywords :
Practical Energy Management Control , Fuel Cell
Abstract:
This paper proposes a practical solution to address the energy management issue in fuel cell hybrid electric vehicles (FCHEVs). This solution revolves around a powertrain system that contains a fuel cell (FC) as the main supply, a photovoltaic cell (PC) as the secondary energy source, and a battery bank (Batt) as backup storage to compensate for the FC’s low response rate. The energy in this hybrid powertrain system alternated between the designated elements and the load via a DC bus, and to maintain a stable output voltage, the DC link was adjusted using a nonlinear approach that is based on the flatness theory and the nonlinear autoregressive moving average (NARMA-L2) neuro-controller. As for the current regulation loops, the sliding mode technique was employed to attain the high dynamic of the reference signals produced by the energy manager loop. To validate the accuracy of the proposed energy management approach (EMA), a test bench was equipped with digital, electronic circuits and a dSPACE DS-1104 unit. This experimental bench contained a fuel cell emulator FC of 1200 W and 46 A, lithium-ion batteries of 24 V, and a solar source capable of 400 W. The obtained results, indeed, attested to the validity of the approach used, yielding a notable performance during multiple charge variations. This ultimately demonstrated that the management approach enhanced the efficiency of the hybrid powertrain.
Author Related Publications
Hytham Saad Mohamed Ramadan, "Efficient and Sustainable Reconfiguration of Distribution Networks via Metaheuristic Optimization", IEEE, 2022
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Hytham Saad Mohamed Ramadan, "Efficient experimental energy management operating for FC/battery/SC vehicles via hybrid Artificial Neural Networks-Passivity Based Control", ELSEVIER, 2021
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Hytham Saad Mohamed Ramadan, "Hydrogen storage technologies for stationary and mobile applications: Review, analysis and perspectives", ELSEVIER, 2021
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Hytham Saad Mohamed Ramadan, "Efficient metaheuristic utopia-based multi-objective solutions of optimal battery-mix storage for microgrids", ELSEVIER, 2021
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Hytham Saad Mohamed Ramadan, "Optimal reconfiguration for vulnerable radial smart grids under uncertain operating conditions", ELSEVIER, 2021
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