AI-Based Predictive Modeling and NSGA-II Optimization for Eco-Driving Route Planning in Electric Vehicles

As EVs reshape the automotive industry, eco-driving and route optimization become essential in reducing energy inefficiencies, especially in urban settings where frequent acceleration and deceleration degrade battery performance. This study proposes a mathematical model for EV energy consumption and applies Multi-Objective Optimization (MOO) with NSGA-II (Non-dominated Sorting Genetic Algorithm II) to minimize energy loss while extending driving range through route selection, traffic avoidance, and eco-driving strategies. The framework integrates real-time traffic and weather data from the TomTom Application Programming Interface (API) along with historical traffic records from NYC OpenData. Vehicle-specific data from the Volkswagen eGolf further trains the model under real driving conditions, producing the energy consumption dataset $E_{Total}$ . Using the $E_{Total}$ dataset, a predictive algorithm improves energy forecasting accuracy by incorporating traffic flow, road topology, and vehicle dynamics. Numerical simulation results confirm the model’s effectiveness in balancing energy use and travel time through Pareto-optimal decision-making. The AI model generated 100 predictive route options between Times Square and the Holland Tunnel. Among these, route 2 (5.17 km) was identified as the most efficient, requiring only 1.03 kWh of energy $E_{Total}$ . These findings demonstrate the potential of AI in eco-routing to extend EV range and enhance driver-assistance and autonomous vehicle systems.