Conceptual Framework for Predictive Navigation in Electric Vehicles: Shaping the Future of Energy Efficiency in Petrosani, Romania

As the global transition towards sustainable mobility advances, electric vehicles (EVs) face increasing challenges related to efficiency, range optimization, and adaptability to diverse environments. This paper proposes a conceptual framework for predictive navigation tailored to the unique geographical and urban characteristics of Petrosani, Romania. The objective is to improve EV energy efficiency by integrating real-time data analysis, advanced predictive algorithms, and route optimization strategies. The proposed system employs GPS data, IoT sensors, and historical driving patterns to predict energy needs and suggest optimized routes based on road profiles, including elevation changes, traffic conditions, and weather impact. Additionally, the framework incorporates an energy recovery forecasting mechanism, enabling EVs to estimate and maximize energy regeneration during downhill segments. The methodology includes a simulated environment designed to test the effectiveness of predictive navigation in Petrosani’s hilly terrain, where frequent elevation variations and dynamic traffic patterns influence energy consumption. The study aligns with Industry 5.0 principles, emphasizing the integration of smart mobility solutions within human-centric ecosystems. The results demonstrate the feasibility of such an approach in optimizing EV navigation and contributing to the broader objective of sustainable urban mobility.