Heterogeneity Analysis of Operating Mode Distribution for Modeling Energy Consumption of Light-Duty Vehicles

• Published in: Transportation research record Vol. 2677; no. 1; pp. 93 - 109
• Main Authors: Zhang, Leqi, Song, Guohua, Zhang, Zeyu
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.01.2023
• Subjects: operating mode distribution; driving behavior; vehicle-specific power; heterogeneity; energy efficiency
• ISSN: 0361-1981; 2169-4052
• DOI: 10.1177/03611981221098397

The use of vehicle operating mode (OpMode) distribution is widely accepted for estimating energy consumption and emissions in the Motor Vehicle Emission Simulator (MOVES) model. However, the heterogeneity of driving behavior may lead to errors when using the default OpMode distribution. To improve the accuracy of energy consumption estimations, it is necessary to recognize the heterogeneity in OpMode distribution among different driving behaviors. With this aim, this paper designs a speed-specific indicator of energy efficiency reflecting driving behavior based on the speed-specific vehicle-specific power (VSP) distribution. The paper uses field data from 26,082 drivers recorded second by second during workdays. It also discusses the intra-heterogeneity and inter-heterogeneity of driving behavior based on unsupervised algorithm clustering. The findings of this paper are as follows. (1) The speed-specific VSP distribution clearly reflects the differences in energy efficiency of individuals’ driving behavior. (2) The energy efficiency indicator reflects the multidimensional inter-heterogeneity and intra-heterogeneity of driving behavior. (3) Drivers’ varied driving behavior causes heterogeneity in energy efficiency at different speeds, possibly causing an error of 6.34% in the emissions estimations. (4) Drivers of electric vehicles (EVs) and hybrid electric vehicles (HEVs) show more aggressive driving behaviors than drivers of conventional vehicles (CVs), which may cause an energy estimation error of over 6% for EVs and HEVs. Thus, the OpMode distribution of EVs, HEVs, and CVs should be modeled separately for on-road energy estimations.