# A Sea Horse Optimization-Based Approach for PEM Fuel Cell Model Parameter Estimation
## 6 Conclusions  
In the literature, there is no study in which this algorithm is directly used in determining the equivalent circuit model parameters for proton exchange membrane fuel cells (PEMFC). In this paper, the SHO algorithm is applied to PEMFC model parameter optimization for the first time, and this gap in the literature is filled by evaluating the potential of the algorithm. In the modeling process, the experimental polarization data of the Ballard Mark V type cell are taken as reference, and the seven basic (\(\epsilon_{1}\), \(\epsilon_{2}\), \(\epsilon_{3}\), \(\epsilon_{4}\), \(b\), \(\lambda\), \(R_{c}\)) parameters of the equivalent circuit model are optimized with the SHO algorithm. The SHO algorithm stands out with its high accuracy (MAE = 0.001079), strong convergence performance, and low convergence time (1.74 s); it offers a solution method that is resilient to the problems of premature convergence and getting stuck in local minimum. The high accuracy level and low convergence time offered by SHO make it a remarkable alternative in terms of PEMFC parameter estimation. Recent studies [38; 39] have proposed improved versions of the original seahorse optimization algorithm by adding chaotic arrays, adaptive update rules, and improved search direction mechanisms to further improve its performance on global optimization tasks. In particular, [PERSON] et al. [38] introduced the Modified Seahorse Optimizer (MSHO) and demonstrated its superiority in benchmarking and engineering problems through numerous enhancements. These studies demonstrate the original SHO framework's modular and flexible structure, which can be extended. As a result, it can be seen that the SHO-based optimization approach presented in this study is not only limited to PEMFC modeling but has also been effectively used in various applications in the energy systems literature. The SHO algorithm provides successful results in a wide range of applications, such as real-time power management optimization and range estimation in electric vehicles [44], economical load allocation and power ripple reduction in photovoltaic microgrids [45], increasing energy efficiency in hybrid systems based on solid oxide fuel cells [46], improving system stability by providing faster settlement and lower overshoot values in load-frequency control [47], and improving transient performance in PSS settings by combining it with chaotic maps [48]. In particular, thanks to its hybrid direction updates based on Brownian motion and Levy flight, SHO offers more balanced exploration-exploitation properties and more stable convergence behavior compared to traditional metaheuristic algorithms in both nonlinear and time-sensitive systems. In this context, the SHO algorithm is considered to be an effective and flexible tool not only for fuel cell modeling but also for the optimization of larger-scale energy systems. For future research, it is recommended to explore hybrid configurations to enhance its performance in more complex systems. Moreover, extending the application of the SHO algorithm to multi-objective optimization problems and other fuel cell technologies--such as Solid Oxide Fuel Cells (SOFC) and Direct Methanol Fuel Cells (DMFC)--may further broaden its utility.  
Conceptualization, G.H.; methodology, A.E. and E.B.A.; software, G.H.; investigation, G.H.; writing--original draft preparation, G.H.; writing--review and editing, G.H.; supervision, A.E. and E.B.A.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.