Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

Ilyes Tegani, Okba Kraa, Haitham S. Ramadan () and Mohamed Yacine Ayad
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Ilyes Tegani: National Higher School of Technology and Engineering—Annaba, Annaba 23000, Algeria
Okba Kraa: Laboratory of Energy System Modeling Electrical Engineering (LMSE), University de Biskra, Biskra 07000, Algeria
Haitham S. Ramadan: Electrical Power and Machines Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Mohamed Yacine Ayad: CISE—Electromechatronic Systems Research Centre, Universidade da Beira Interior, Calçada Fonte do Lameiro, P-6201-001 Covilhã, Portugal

Energies, 2023, vol. 16, issue 13, 1-23

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.

Keywords: practical implementation; fuel cell hybrid electric vehicle; energy management approach; flatness theory; NARMA-L2 neuro-controller (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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