Binaya Sharma

Multi-Vehicle Energy Consumption Prediction Under Real-World Driving Conditions

Abstract

This study introduces DeepTran, a novel deep learning framework designed to predict vehicle energy consumption across urban road networks. DeepTran’s hybrid architecture, which integrates a Residual Neural Network (ResNet) into Vision Transformer, captures both spatial patterns and temporal dependencies while incorporating real-time traffic conditions and static vehicle characteristics. Extensive experiments conducted on Michigan’s road network, using a diverse fleet of Internal Combustion Engine (ICE) vehicles, Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), and Electric Vehicles (EVs), demonstrate DeepTran’s superior performance over existing algorithms. This research contributes to the understanding of vehicle energy consumption patterns and has potential applications in urban planning, traffic management, and environmental policy formulation.