Minimizing Total Travel Time for Intelligent EVs with Real-Time Traffic and Charging Constraints

The rise of electric vehicles (EVs), driven by advances in battery technology and environmental concerns, introduces new challenges in energy-efficient routing. Intelligent electric vehicles (IEVs) hold great promise for smart mobility, but effective routing and charging remain key obstacles. This paper proposes a method to minimize total journey time by accounting for travel, charging, and waiting while integrating real-time traffic data and battery constraints. A Mixed-Integer Linear Programming (MILP) model is developed for optimal routing in small networks, and a scalable heuristic, MT2T (Minimizing Total Travel Time), is introduced for larger scenarios. The approach considers dynamic traffic, charging station booking and cancellation, and battery thresholds. Experimental results show that both methods reduce travel time and support efficient routing for next-generation IEVs.