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

Abstract

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.