.png){kind=icon}
.png){kind=icon}
Return
Seven-parameter PEMFC model optimization using an battlefield optimization algorithm
.png){kind=icon}
OA
AI
DOI：10.1016/j.elecom.2025.108033.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• Application to PEMFCs: The BfOA algorithm is applied to optimize design variables for six PEMFC stacks: BCS 500 W [41] [42], SR-12500 W [41,42], STD 250 W [41] [42], Nedstack 600 W PS6 [43], Horizon H-12 [44], and Ballard Mark V [44]. • Comparative Analysis: The performance of BfOA is benchmarked against nine state-of-the-art algorithms, including Thermal Exchange Optimization (TEO) [45], Grey Wolf optimization (GWO) [46], Rime Optimization (RIME) [47], Equilibrium Optimizer (EO) [48], Marine Predators Algorithm (MPA) [49], Komodo Mlipir Algorithm (KMA) [50], Self-Adaptive Differential Evolution (SaDE) [51], White Shark Optimizer (WSO) [52], and Genetic Algorithm (GA) [53], across a range of optimization scenarios. • Environmental Impact Assessment: The impact of varying temperature and pressure conditions on PEMFC performance is evaluated, demonstrating the adaptability and reliability of the optimized models. • Validation Against Experimental Data: Simulation results are validated against experimental data for each PEMFC stack, confirming the robustness and accuracy of the BfOA optimized models.
Keywords:
Machine learning
Optimization
Fuel cell
Parameter estimation
Run time
Friedman ranking test
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
1.6K
Citations:
1.6W
View more organizations