A novel VSP-based CO2 emission model for ICEs and HEVs based on internally observable variables: Engine operating speeds.

The driving characteristics and engine operating characteristics on vehicle carbon dioxide (CO 2) emissions of different types of vehicles are explored in this study. For Internal Combustion Engine Vehicles (ICEVs), vehicle specific power (VSP) is the parameter with the highest correlation coefficient with CO 2 emission rate, while for Hybrid Electric Vehicles (HEVs), it becomes engine speed. Due to the compound drive of fossil-fueled internal combustion engines and electric motors, the CO 2 emission rates of HEVs is no longer positive correlated with velocity-related vehicle dynamics presented by traditional VSP binning method. Therefore, a novel binary VSP binning model coupled with engine speed maps (VSP + M) is proposed to link the tailpipe emissions to vehicle activities and engine operating parameters. After well-designed configurations on the number of map divisions m and the number of elements into a tile z , the VSP + M model is able to achieve higher prediction accuracy along with better data usage. For HEVs, the prediction accuracy represented by R2 is observed over three-fold increase beyond 0.9, which embodies great value of binary model integrated with both externally observable variable (EOV) and internally observable variable (IOV) parameters in essence of the actual road traffic scenarios undergoing large-scale electrification. • The driving and engine operating characteristics on vehicle emissions are explored through RDE tests of HEVs and ICEVs. • According to the correlation analysis, the IVOs and EVOs that have strong correlations with CO 2 emissions are extracted. • A novel binary VSP model (VSP + M) is proposed to link the emissions to vehicle activities and engine operating parameters. • The VSP + M model is optimized by the values of m and z to improve the prediction accuracy of vehicle carbon emissions. [ABSTRACT FROM AUTHOR]