The Future of Green Mobility: Optimizing Electric Vehicle Routes Through Hybrid Intelligence

The Electric Vehicle Routing Problem (EVRP) has some of its own specific characteristics such as limited vehicle range, long charging times, and very uneven charging station distribution. Conventional optimization techniques fail to deliver solutions even within a dynamic environment such as an urban one due to their rigidity and scalability issues. The present work proposes an integration of hybrid approach combining neural networks, simulated annealing, and reinforcement learning. Neural networks predict consumption and availability for charging stations, while simulated annealing optimizes routes. Reinforcement learning dynamically re-routes traffic in real time due to disruptions such as traffic or station outages. The results of the testing in dense urban areas show a reduction of 20% in logistical costs and 15% in emissions when compared to conventional methods. Some challenges include dependence on accurate data, high calculation costs, and low applicability in rural scenarios. To enhance efficiency, future work could integrate digital twins, or multi-objective optimization. In order to ensure scalable and sustainable solutions for the logistics of EVs, in smart cities, this paper aims to connect the AI and operational research. In conclusion, a SWOT analysis will be presented to assess the strengths, weaknesses, opportunities and threats of this strategy.