Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application

Abdeldjalil Djouahi (), Belkhir Negrou, Boubakeur Rouabah, Abdelbasset Mahboub and Mohamed Mahmoud Samy
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Abdeldjalil Djouahi: Laboratory Promotion et Valorisation des Ressources Sahariennes (VPRS), University of Kasdi Merbah Ouargla, BP 511, Ouargla 30000, Algeria
Belkhir Negrou: Laboratory Promotion et Valorisation des Ressources Sahariennes (VPRS), University of Kasdi Merbah Ouargla, BP 511, Ouargla 30000, Algeria
Boubakeur Rouabah: Electrical Engineering Department, University of Kasdi Merbah Ouargla, BP 511, Ouargla 30000, Algeria
Abdelbasset Mahboub: Electrical Engineering Department, University of Kasdi Merbah Ouargla, BP 511, Ouargla 30000, Algeria
Mohamed Mahmoud Samy: Electrical Engineering Department, Faculty of Engineering, Beni-Suef University, Beni-Suef 2722165, Egypt

Energies, 2023, vol. 16, issue 9, 1-18

Abstract: In light of the energy and environment issues, fuel cell vehicles have many advantages, including high efficiency, low-temperature operation, and zero greenhouse gas emissions, making them an excellent choice for urban environments where air pollution is a significant problem. The dynamics of fuel cells, on the other hand, are relatively slow, owing principally to the dynamics of the air compressor and the dynamics of manifold filling. Because these dynamics can limit the overall performance of fuel cell vehicles, two key technologies that have emerged as critical components of electric vehicle powertrains are batteries and supercapacitors. However, choosing the best hybrid energy storage system that combines a battery and a supercapacitor is a critical task nowadays. An electric vehicle simulated application by MATLAB Code is modeled in this article using the multi-objective particle swarm optimization technique (MOPSO) to determine the appropriate type of batteries and supercapacitors in the SFTP-SC03 drive cycle. This application optimized both component sizing and power management at the same time. Batteries of five distinct types (Lithium, Li-ion, Li-S, Ni-Nicl2, and Ni-MH) and supercapacitors of two different types (Maxwell BCAP0003 and ESHSR-3000CO) were used. Each storage component is distinguished by its weight, capacity, and cost. As a consequence, using a Li-ion battery with the Maxwell BCAP0003 represented the optimal form of hybrid storage in our driving conditions, reducing fuel consumption by approximately 0.43% when compared to the ESHSR-3000CO.

Keywords: fuel-cell hybrid electric vehicle; particle swarm optimization algorithm; hydrogen consumption; multi-objective function problem; energy management strategy (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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