Predictive Modeling as a Tool to Improve Compliance with Euro 7 Standards: Real-Time NOₓ Emissions

The implementation of Euro 7 emission standards demands advanced real-time NOₓ monitoring systems for diesel vehicles. This study develops a novel Mixture of Experts (MoE) architecture to predict NOₓ emissions under varying thermal and kinematic conditions. Data from a Euro 6d commercial vehicle equipped with PEMS yielded 3,247 samples during real-world driving campaigns. Engine operations were classified into three phases: cold (<70°C coolant temperature), hot low-speed (<90 km/h), and hot high-speed (≥90 km/h), based on aftertreatment system thermal dynamics. The MoE framework dynamically routes predictions to optimized XGBoost specialized regressors for each operational phase, achieving high performance precision (R² = 0.918, RMSE = 1.825 mg/s) with 58% RMSE reduction compared to unified models. Phase separability analysis using t-SNE confirmed distinct emission mechanisms (silhouette coefficient = 0.73). Integration with autoencoder-based anomaly detection achieved 95.2% sensitivity, while Model Predictive Control implementation demonstrated 11-13% NOₓ reduction across driving scenarios. The framework operates within real-time constraints (<1.5 ms inference latency) suitable for embedded automotive applications and Euro 7 compliance through intelligent, context-aware emission prediction.