Examining influential factors on the energy consumption of electric and diesel buses: A data-driven analysis of large-scale public transit network in Beijing

Abstract

Switching from diesel-powered to battery-powered buses has been a global tendency. Traditional approaches rely on standard driving cycles or fuel economy data for energy consumption on a limited number of buses; thus, expanding to large bus fleets at the city level has become challenging. This study uses high-resolution GPS and smart card transaction data to generate each bus driving profile and weight dynamics in a large-scale transit network. Two vehicle activity-based energy consumption models are adopted and calibrated for diesel bus (DB) and electric bus by using the field data of 630 bus routes in Beijing. The average energy consumptions of DBs and electric buses are 43.5 and 14.1 L/100 km, respectively. A gradient boosting regression tree algorithm is presented to examine and rank distinct influential factors on the energy consumption of DBs and electric buses. Heterogenous behaviors are identified: the leading attributes affecting the energy consumption of electric buses and DBs are route characteristics and operational condition, respectively. After computing, the total energy conservation of electrifying all bus fleets is equivalent to 0.87% of daily electricity demand in Beijing. These findings set a good base for further studies on bus fleet replacement, charging infrastructure deployment, and electrified route prioritization.