Modeling, Simulation, and Energy Efficiency of Hybrid Electric Vehicles under Urban Driving Conditions

Abstract

Hybrid Electric Vehicles (HEVs) have emerged as a critical technological solution for improving fuel savings and reducing environmental impacts in congested urban environments. Urban driving conditions, characterized by frequent stop-and-go movement, variable speeds, and high idling time, impose
unique energy demands on hybrid powertrains. This study presents comprehensive modeling and simulation of HEV performance under realistic city-traffic conditions. Using a validated multi-domain simulation environment, the research investigates component-level behavior, control-strategy efficiency, and overall energy consumption patterns. The study further evaluates regenerative braking potential, engine on/off scheduling, and battery State-of-Charge (SOC) stability. Results demonstrate that optimized energy- management algorithms can reduce fuel consumption by up to 28% while maintaining stable battery operation. Sensitivity analyses reveal that drive-cycle variability and traffic congestion levels significantly
influence vehicle efficiency. The findings contribute to better prediction models and improved energy- management frameworks for next-generation HEVs.