Study on CO₂ Emission Assessment of Heavy-Duty and Ultra-Heavy-Duty Vehicles Using Machine Learning

EU is actively moving towards the implementation of Euro-7 regulations, thus placing a strong emphasis on real-road emissions. Euro-7 introduced OBM (on-board monitoring), which is an enhancement of regulations that closely replicates real-world road conditions. Furthermore, there is a need to devise an effective application strategy for utilizing the driving monitoring data prior to the enforcement of OBM. This study addresses these challenges by conducting RDE (real-driving emission) tests on both 3.5-ton and 25-ton commercial vehicles to gather CO₂ emissions and engine control unit data accessible through an OBD (on-board diagnostics) port. To process the RDE data, an appropriate machine learning model, XGBoost, was selected and trained. The outcome of our CO₂ emission prediction for the two vehicles demonstrated that employing monitoring data yielded reliable estimates of actual road CO₂ emissions. Finally, a comparative analysis was conducted between the proposed monitoring approach and the fuel-based CO₂ monitoring method using the emission factor from EMEP/EEA air pollutant emission inventory guidebook 2019 utilizing fuel consumption data achieved through the OBFCM (on-board fuel and energy consumption monitoring) rule. Our method, which is based on predictive CO₂ emissions monitoring, exhibited significantly greater accuracy. This outcome underscores the necessity to adopt the proposed approach.