Eco-Routing and Driving Pattern Optimization to Minimize EV Energy Usage

As electric vehicles (EVs) become more prevalent, reducing energy consumption through intelligent routing and driving strategies has emerged as a critical research area. This paper proposes a dual-layer framework that combines eco-routing with driving pattern optimization to minimize overall energy usage for EVs. The system integrates historical and real-time traffic, road grade, and battery data to recommend energy-efficient routes and personalized driving behavior adjustments. Machine learning techniques are applied to estimate consumption over alternative paths, while dynamic control algorithms guide driving maneuvers based on contextual energy profiles. Experimental results from simulations and real-world datasets demonstrate that the proposed method reduces energy consumption by up to 20% compared to shortest-path routing and by 12% compared to standard eco-driving. These findings highlight the potential of integrated eco-routing and behavioral adaptation for extending range and improving EV efficiency in practical deployments.