Multi-Objective Optimization-Based Health-Conscious Predictive Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles

Mehdi Sellali, Alexandre Ravey, Achour Betka, Abdellah Kouzou, Mohamed Benbouzid, Abdesslem Djerdir, Ralph Kennel and Mohamed Abdelrahem
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Mehdi Sellali: FEMTO-ST Institute (UMR CNRS 6174), University of Technology of Belfort-Montbéliard, 90010 Belfort, France
Alexandre Ravey: FEMTO-ST Institute (UMR CNRS 6174), University of Technology of Belfort-Montbéliard, 90010 Belfort, France
Achour Betka: LGEB Laboratory, University of Biskra, Biskra 07000, Algeria
Abdellah Kouzou: LAADI Laboratory, Faculty of Sciences and Technology, Ziane Achour University, Djelfa 17000, Algeria
Mohamed Benbouzid: Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
Abdesslem Djerdir: FEMTO-ST Institute (UMR CNRS 6174), University of Technology of Belfort-Montbéliard, 90010 Belfort, France
Ralph Kennel: Chair of Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany
Mohamed Abdelrahem: Chair of Electrical Drive Systems and Power Electronics, Technical University of Munich (TUM), 80333 Munich, Germany

Energies, 2022, vol. 15, issue 4, 1-17

Abstract: The Energy Management Strategy (EMS) in Fuel Cell Hybrid Electric Vehicles (FCHEVs) is the key part to enhance optimal power distribution. Indeed, the most recent works are focusing on optimizing hydrogen consumption, without taking into consideration the degradation of embedded energy sources. In order to overcome this lack of knowledge, this paper describes a new health-conscious EMS algorithm based on Model Predictive Control (MPC), which aims to minimize the battery degradation to extend its lifetime. In this proposed algorithm, the health-conscious EMS is normalized in order to address its multi-objective optimization. Then, weighting factors are assigned in the objective function to minimize the selected criteria. Compared to most EMSs based on optimization techniques, this proposed approach does not require any information about the speed profile, which allows it to be used for real-time control of FCHEV. The achieved simulation results show that the proposed approach reduces the economic cost up to 50% for some speed profile, keeping the battery pack in a safe range and significantly reducing energy sources degradation. The proposed health-conscious EMS has been validated experimentally and its online operation ability clearly highlighted on a PEMFC delivery postal vehicle.

Keywords: energy management strategy; model predictive control; health conscious; multi-objective optimization; fuel cell hybrid electric vehicles (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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