An innovation framework for investigating the performance of proton exchange membrane fuel cells: a perspective of the EANN model toward lower emission

Bekmyrza K.Z. Kuterbekov K.A. Kabyshev A.M. Kubenova M.M. Baratova A.A. Aidarbekov N. Yadav B.K.
2025 Oxford University Press

International Journal of Low-Carbon Technologies
2025 #20 2157 - 2172 pp.

An emotion-inspired artificial neural network (ANN) was developed to predict proton exchange membrane fuel cell (PEMFC) performance from operating conditions, addressing the need for accurate, low-overhead models deployable in real time (e.g. hydrogen-powered electric vehicles). Novelty lies in emotion-modulated updates coupled with diversity-preserving evolutionary tuning, enabling adaptive learning and improved generalization under coupled temperature–pressure–humidity–load variations. Using a physics-based dataset and K-fold validation with Monte Carlo sensitivity, the model outperformed ANN while reducing computation. Voltage and current-density errors were low (root mean squared error, 0.03 V, 0.15 A/cm²), with higher fit (R ²≈0.97) and ~22% lower cost; robustness was maintained across perturbations (R ²>0.95, 200 runs).

emotional artificial neural networks (EANN) , energy efficiency optimization , fuel cell performance , predictive modeling techniques , proton exchange membrane fuel cells (PEMFCs)

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Institute of Physics and Technical Science, L.N. Gumilyov Eurasian National University, Satpayev St. 2, Astana, 010008, Kazakhstan
Faculty of Transport and Energy, L.N. Gumilyov Eurasian National University, Satpayev St. 2, Astana, 010008, Kazakhstan
Department of Mechanical Engineering, Purwanchal Campus, Institute of Engineering (IOE), Tribhuvan University (TU), Dharan-08, 56700, Nepal

Institute of Physics and Technical Science
Faculty of Transport and Energy
Department of Mechanical Engineering

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