## 1 Introduction
Optimization methodologies in the latest studies include some approaches such as neural network (NN) [21], particle swarm algorithm (PSO) [27], genetic algorithm (GA) [28], dynamic programming (DP) [18], sunflower optimization (SFO) [22], and Bald Eagle Search Algorithm (BESA) [29]. The BESA, which is one of the nature-inspired meta-heuristic optimization algorithms [30], is chosen for its superior balance between exploration and exploitation, making it highly effective in navigating complex search spaces. When compared to commonly used algorithms, the BESA offers significant advantages in computation time and convergence speed. Its structured phases allow for the faster identification of optimal solutions with fewer iterations, making it particularly suitable for problems requiring both high precision and computational efficiency [29; 31; 32; 33]. Previous studies in the literature include topics such as vehicle component optimization, energy management systems, and parameter optimization. [PERSON] et al. [34] benefit from the BESA for a battery state of health estimation. In a methodology comparison, the BESA showed the highest accuracy. [PERSON] et al. [35] conducted a study on battery parameter estimation using the BESA. They compared the performance of several methods and found that the BESA showed more reliable results. [PERSON] et al. [36] studied charging distribution in India. They used the BESA to find the best location for charging stations. The chosen methodology resulted in more accuracy. [PERSON] et al. [37] provide a hybrid storage system (battery and supercapacitor) management strategy. The BESA showed superiority in the battery state of charge preserves. [PERSON] [38] developed an optimization methodology that includes some subsystems, such as photovoltaics, wind turbines, fuel cells, micro turbines, and batteries. During the stages of the optimization process, the developed method structured with the BESA presented the best result values between the specified components. [PERSON] et al. [29] highlighted the lifetime of fuel cells and researched parameter optimization. The BESA and five different metaheuristic algorithms were used on two different fuel cell systems. Among others, the BESA showed the best results in the optimization of fuel cell systems. [PERSON] et al. [31] provide an optimization strategy for fuel cells with five different algorithms. Three different fuel cell systems were used for the optimization process, and the BESA showed the best result. [PERSON] et al. [32] researched the parameter identification of fuel cells. The BESA showed better performance than the genetic algorithm (GA) to optimize the fuel cell. [PERSON] et al. [39] utilized the BESA in an energy management system at home. The approach aims to reduce energy consumption while keeping human comfort stable and minimizing the electricity bills and average peak ratio. [PERSON] et al. [40] researched the best fuel cell characteristic definitions. Among the five different approaches, the BESA made two fuel cell systems with the best parameters. [PERSON] et al. [41] researched the optimal parameters of a fuel cell system. While the optimization process includes several algorithms, the BESA is the superior algorithm among them. [PERSON] et al. [42] compared several optimization methodologies for fuel cell performance optimization. The BESA minimized the parameters more effectively. [PERSON] et al. [33] conducted research on energy management optimization of a fuel cell hybrid electric vehicle that has a fuel cell, battery, and supercapacitor. A significant hydrogen consumption reduction was achieved in the New European Driving Cycle (NEDC) and Extra Urban Driving Cycle (EUDC) driving cycles using the BESA. Studies in the literature make strong contributions to this subject. There are still gaps in the literature on this rapidly developing topic. Energy management systems designed in previous studies are mostly about only hydrogen consumption minimization.