Reliable Evolutionary Field Optimization-Based Energy Management Strategy for Mitigating Hydrogen Consumption of Hybrid Generation System

This paper proposes a new strategy incorporating the evolutionary field optimizer (EFO) to manage the production of a hybrid generating system supplying highly variable demand such as electric vehicles, automobiles, trains, and airplanes. The generating system includes fuel cell, battery, and supercapacitor. The main goal of the suggested strategy is to mitigate overall hydrogen consumption to the lowest possible level under severe fluctuations of loads and delay the deterioration of fuel cell performance. The EFO is used with a previously published external energy maximization method (EEMS) to reduce the fuel cell hydrogen usage by optimizing battery and supercapacitor generations. Two design variables are considered: the battery power and supercapacitor discharge/charge voltage. The proposed strategy is contrasted with other metaheuristic approaches such as Kepler optimization algorithm, walrus optimization algorithm, and golf optimization algorithm in addition to traditional strategies like classical proportional-integral, hybrid frequency decoupling state machine, equivalent consumption minimization strategy, and EEMS. The suggested approach reduced the amount of hydrogen consumed by 39.4% with an efficiency of 88.15% when compared to the published EEMS. The obtained results revealed that, the proposed methodology outperformed alternative approaches in terms of system overall efficiency, battery state of charge, and rate of hydrogen consumption.