Construction of a NOx Emission Prediction Model for Hybrid Electric Buses Based on Two-Layer Stacking Ensemble Learning

To enhance the management of NOx emissions from hybrid electric buses, this paper develops an instantaneous NOx emission prediction model for hybrid electric buses based on a two-layer stacking ensemble learning method. Seventeen parameters, including operational characteristic parameters of hybrid electric buses, engine operating parameters, and emission after-treatment device operating parameters are selected as input features for the model. The correlation analysis results indicate that the Pearson correlation coefficients of engine coolant temperature and selective catalytic reduction (SCR) after-treatment device temperature show a significant linear negative correlation with instantaneous NOx emission mass. The Mutual Information (MI) analysis reveals that engine intake air volume, SCR after-treatment device temperature and engine fuel consumption have strong nonlinear relationships with instantaneous NOx emission mass. The two-layer stacking ensemble learning model selects eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and an optimized BP neural network as base learners, with a linear regression model as the meta-learner, effectively predicting the instantaneous NOx emission mass of hybrid electric buses. The evaluation metrics of the proposed model—mean absolute error, root mean square error, and coefficient of determination—are 0.0068, 0.0283, and 0.9559, respectively, demonstrating a significant advantage compared to other benchmark models.