| Study on CO2 Emission Assessment of Heavy-Duty and Ultra-Heavy-Duty Vehicles Using Machine Learning
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| Seokho Moon 1, Jinhee Lee 2, Hyung Jun Kim 3, Jung Hwan Kim 3, Suhan Park 4 |
1Department of Mechanical Engineering , Graduate School of Konkuk University
2Advanced Powertrain R&D Center , Korea Automotive Technology Institute
3Transportation Pollution Research Center , National Institute of Environmental Research
4School of Mechanical and Aerospace Engineering , Konkuk University |
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| ABSTRACT |
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"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.
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| Key Words:
Heavy-duty vehicle · Real driving emission · Portable emission measurement system · On-board diagnostics · On-board monitoring · CO 2 emissions · Artifi cial intelligence prediction · Machine learning
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Corresponding Author.
Suhan Park , Email. 
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