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Minimization Hydrogen Management Strategy Using the Red‐Tailed Hawk Algorithm for Hybrid Storage Electric Vehicles

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Publicado en:International Journal of Energy Research vol. 2025, no. 1 (2025)
Autor principal: Rezk, Hegazy
Otros Autores: Aly, Mokhtar
Publicado:
John Wiley & Sons, Inc.
Materias:
Parameters
Distributed generation
Control systems
Wavelet transforms
Hydrogen
Algorithms
Parameter identification
Power control
Optimization techniques
Chemical reactions
Electric vehicles
State machines
Equivalence
Unmanned aerial vehicles
Batteries
Environmental impact
Energy storage
Marine fishes
Fuel cells
Hybrid electric vehicles
Heuristic methods
Energy consumption
Dynamic loads
Electric charge
Supercapacitors
Energy management
Optimization
State of charge
Energy
Energy management systems
Optimization algorithms
Vehicles
Economic
Acceso en línea:Citation/Abstract
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Resumen:Hybrid electric vehicles (HEVs) utilizing fuel cells (FCs), batteries, and supercapacitors (SCs) necessitate sophisticated energy management systems (EMSs) to optimize hydrogen utilization and improve efficiency. Conventional techniques, including proportional–integral (PI) control, state machine control strategy (SMCS), and the equivalent consumption minimization strategy (ECMS), have difficulties in sustaining optimal performance under dynamic loads because of their fixed or slowly adjusting parameters. This work introduces an improved energy consumption control system (ECMS) coupled with the red‐tailed hawk (RTH) optimization method for real‐time and adaptive power control. The RTH algorithm dynamically modifies the ECMS equivalency factor to enhance the equilibrium between the hydrogen economy and the battery state of charge (SOC). Simulation outcomes under the FTP‐75 driving cycle indicate that the proposed ECMS‐RTH decreases hydrogen consumption by 61.6% and enhances total system efficiency by 21.47% relative to traditional ECMS, while ensuring the battery SOC remains within safe parameters. The method surpasses contemporary metaheuristic techniques, including bald eagle search (BES), white shark optimizer (WSO), manta ray foraging optimization (MRFO), and cuckoo search (CS). The findings validate the efficacy of the ECMS‐RTH technique as an adaptive real‐time energy management framework for HEV applications. Future endeavors will encompass hardware‐in‐the‐loop validation and scalability studies of many microgrids.
ISSN:0363-907X
1099-114X
DOI:10.1155/er/6623935
Fuente:Advanced Technologies & Aerospace Database