A bio-inspired metaheuristic approach for optimizing polymer electrolyte membrane fuel cell models

As hydrogen gains prominence as an energy source, polymer electrolyte membrane fuel cells (PEMFCs) are emerging as a leading clean technology. However, optimizing PEMFC design and performance hinges on accurate modeling. Addressing this challenge, researchers have introduced the enhanced salp swarm algorithm (ESSA). This bio-inspired metaheuristic identifies unknown PEMFC model parameters through several key innovations: opposition-based learning, a new class of "exploration salps," crossover and mutation techniques from genetic algorithms, and a "survival of the fittest" selection process. These additions effectively overcome the limitations of the standard salp swarm algorithm, leading to improved population diversity, faster convergence, better exploration, and higher accuracy. Rigorous experimental validation on BCS 500 W commercial PEMFC stacks demonstrated ESSA's consistent ability to deliver more accurate and stable parameter estimations with quicker convergence compared to numerous other state-of-the-art algorithms, making it a significant advancement in PEMFC parameter estimation and optimization.