Big Data Challenges for E-Mobility

The rapid urbanization and demand for sustainable transportation have driven the adoption of Electric Vehicles(EVs) in modern cities. Optimizing EV routes and energy consumption is critical for improving urban mobility, reducingcarbon emissions, and enhancing transportation efficiency as part of smart city initiatives.This research proposes an advanced EV route optimization system leveraging machine learning to predict energyconsumption and analyze real-time traffic. The system integrates energy prediction models, real-time traffic data, anddynamic routing algorithms to optimize travel paths. It addresses challenges in energy management, route planning, andcharging infrastructure by estimating energy use based on traffic, road types, and vehicle load. Real-time traffic updatesenable dynamic route adjustments, ensuring optimal travel time and energy efficiency.This work contributes to smart city transportation solutions, offering a practical framework for implementing EVroute optimization systems and valuable insights for policymakers, urban planners, and transportation authorities strivingfor sustainable urban mobility.