Emissions and fuel consumption of a hybrid electric vehicle in real-world metropolitan traffic conditions

This study tested exhaust emissions and fuel consumption for a hybrid electric vehicle (HEV) in real-world conditions using a portable emissions measurement system (PEMS). A gradient boosting model was developed to predict the electric motor's operation and emissions using only vehicle kinematic data. The model was applied to estimate the potential emission reductions that would be achieved with HEVs compared to conventional vehicles, using two large real-driving activity datasets collected in Greater Toronto and Metropolitan Beijing. The emission reductions estimated for Toronto were 21.6%, 31.3%, and 53.0% for CO₂, CO, and NO_x, and 41.0%, 28.9%, and 68.5% for Beijing. We observed higher emission reductions for CO₂ and NO_x under low power demand vehicle operations, which occur more frequently in Beijing, while more aggressive driving was noted in Toronto, leading to smaller estimated benefits of HEVs. Compared to previous studies, our explainable gradient boosting model improved prediction accuracy and robustness substantially by achieving an average Pearson correlation of 0.741 from cross-validation. This study goes beyond an analysis of HEV emissions from engine and motor operations by applying real-world driving data from two large metropolitan areas to the model. By doing so, a novel investigation of the traffic situations, roads, and driving behaviours that yield the highest emission benefits for HEVs was conducted.