Clustering and disjoint principal component analysis of emissions and driving volatility data collected from a hybrid electric vehicle in real drive conditions

Abstract

Despite the fuel use and emission benefits of Hybrid Electric Vehicles (HEVs), few studies have characterized in detail emission patterns and driving volatility profiles from HEVs in different road types under Real Driving Emission (RDE) conditions. This paper characterized second-by-second tailpipe emissions, vehicle engine, and dynamics from a 2020 Toyota HEV sub-compact on a 44 km driving route over rural, urban, and highway roads in the Aveiro region (Portugal). Driving volatility was represented by six driving styles based on combinations of acceleration/deceleration and vehicular jerk (the rate at which an object’s acceleration changes with respect to the time). Clustering and Disjoint Principal Component Analysis (CDPCA) was applied to examine the relationships between emissions, engine, internal combustion engine (ICE) status, roadway characteristics, and vehicular jerk types. Although the urban route yielded lower carbon dioxide and nitrogen oxides emissions than rural and highway routes did, it resulted in highly volatile driving behaviors at low speeds (< 45 km.h-1). Both route type and HEV ICE operating behavior showed to have an impact on the distribution of vehicular jerk types. CDPCA constrained to road sector exhibited different shapes in the clusters of the jerk types between ICE operation status. This paper can provide insights into RDE analysis of the new generation of HEVs about the characterization of volatile driving behaviors. Such information can be integrated into vehicle electronic car units and navigation systems to provide feedback for drivers about their driving behavior in terms of high emission rates and jerkings to the vehicle.