In response to the pressing need to reduce greenhouse gas emissions from the
transportation sector, hydrogen-fueled internal combustion engines
(H2ICEs) have emerged as a promising alternative to conventional
fossil-fueled powertrains. However, optimizing H2ICEs presents
challenges in balancing performance with emissions, particularly in nitrogen
oxide (NOx) formation This study proposes a data-driven methodology
using an artificial neural network (ANN) to predict key emission and performance
metrics: NOx emissions, brake mean effective pressure (BMEP), brake
specific fuel consumption (BSFC), brake power, and brake thermal efficiency,
based solely on engine operational parameters. Experimental data were collected
from a three-cylinder Ford EcoBoost engine under varying conditions of intake
pressure, spark timing, air-fuel ratio, engine speed, and valve timing. Feature
selection was performed using the Spearman correlation coefficient, identifying
engine speed, start of injection angle (SOI), air-fuel ratio (λ), and intake
pressure as the most important input variables. Bayesian optimization was
employed to tune the ANN’s hyperparameters, resulting in a network architecture
with a single hidden layer consisting of 10 neurons using the tanh activation
function, optimized with the Adam optimizer at a learning rate of 0.01. The
final ANN model exhibited satisfactory predictive performance, achieving
correlation coefficients greater than 0.97 for most outputs and exceeding 0.95
across all predicted variables. These results demonstrate that the proposed ANN
effectively captures the nonlinear behavior of hydrogen-fueled engines and
offers a valuable tool for reducing the experimental burden in engine
calibration and development, thereby supporting the advancement of
hydrogen-powered mobility solutions.