ArticleScientific reports2025
Integrated neural network and metaheuristic algorithms for balancing electrical performance and thermal safety in PEMFC design.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Efficient design of proton exchange membrane fuel cells (PEMFCs) requires balancing high electrical output with thermal stability, yet the complex interactions among operating parameters make this a challenging task. Addressing this gap, this study develops an integrated predictive-optimization-decision framework that systematically models PEMFC performance, explores trade-offs, and guides application-specific design choices. The primary innovation lies in combining multi-layer perceptron neural networks (MLPNN) with metaheuristic optimization, particle swarm optimization (PSO), modified particle swarm optimization (MPSO), multi-objective Harris hawks optimization (MOHHO), and multi-objective PSO (MOPSO), followed by decision-making using the additive ratio assessment (ARAS) method. Predictive modeling results demonstrate variable-specific advantages of optimization strategies: PSO-MLPNN yielded superior accuracy for electrical power output prediction (MAPE = 0.233%), while MPSO-MLPNN achieved marginally better accuracy for cell temperature prediction (MAPE = 0.301%). Multi-objective optimization revealed the inherent trade-off between power and temperature, with MOHHO providing broader Pareto fronts and greater diversity than MOPSO. Optimal operating conditions (ST
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