Data-driven prediction and multi-objective optimization of pemfc performance using an ANN–GA hybrid model
Issued Date
2026-03-01
Resource Type
eISSN
26662027
Scopus ID
2-s2.0-105029724466
Journal Title
International Journal of Thermofluids
Volume
32
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Thermofluids Vol.32 (2026)
Suggested Citation
Boekfah A., Seanglumlert C., Rumnum S., Rattanaphan S., Punurai W., Suvanjumrat C. Data-driven prediction and multi-objective optimization of pemfc performance using an ANN–GA hybrid model. International Journal of Thermofluids Vol.32 (2026). doi:10.1016/j.ijft.2026.101580 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115116
Title
Data-driven prediction and multi-objective optimization of pemfc performance using an ANN–GA hybrid model
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Proton exchange membrane fuel cells (PEMFCs) are regarded as a key clean energy technology for transportation, portable power devices, and stationary power generation due to their high efficiency, low operating temperature, and zero-emission characteristics. Improving PEMFC performance while reducing system cost remains a critical challenge, requiring accurate prediction tools and robust optimization strategies. This study proposes a novel, unified artificial neural network–genetic algorithm (ANN–GA) framework for simultaneous performance prediction and optimization of PEMFC systems. A multilayer perceptron ANN, with its architecture and hyperparameters optimized using a genetic algorithm, was trained using 239 experimentally obtained datasets to predict cell voltage (V) and power density (I). The model accounts for key operating and design parameters, including hydrogen flow rate (Q<inf>H<inf>2</inf></inf>), anode relative humidity (RH<inf>a</inf>), anode back pressure (P<inf>a</inf>), cell operating temperature (T<inf>PEMFC</inf>), anode stoichiometric ratio (λ<inf>a</inf>), oxygen flow rate (Q<inf>O<inf>2</inf></inf>), cathode relative humidity (RH<inf>c</inf>), cathode back pressure (P<inf>c</inf>), stack number (n), active area (A), and current density (J). Sensitivity analysis revealed that operating temperature is the most influential factor affecting PEMFC performance, followed by stack number. The optimized ANN exhibited excellent predictive accuracy, achieving a coefficient of determination of R² = 0.99868 and a mean squared error of 0.0007655, with a mean absolute prediction error of 6.27% across the independent ANN test dataset, corresponding to a coefficient of determination of R² = 0.99868. For the optimization stage, the trained ANN was coupled with a genetic algorithm to perform multi-objective optimization, in which PEMFC performance indicators and cost-related outputs were simultaneously predicted and subsequently aggregated using a weighted-sum strategy to identify an optimal trade-off operating condition. The proposed framework represents a distinct advancement over existing data-driven PEMFC models, offering a computationally efficient, experimentally validated, and practically deployable tool for the design and optimization of high-performance, cost-effective PEMFC systems for next-generation hydrogen energy applications.