Paper Title
Smart EV Route Optimization with Charging Forecast Using State-Space A* and Intelligent Backend Architecture

Abstract
Electric Vehicles (EVs) are starting to play a crucial role in environmentally friendly transportation networks. How- ever, long-distance travel planning is complicated by the battery’s short range and the unpredictability of charging stations. Conventional navigation systems do not take into account EV specific limitations like battery capacity, energy consumption, and charging requirements; instead, they mainly optimize routes based on distance or travel time. In this paper, a battery-aware backend architecture is used to implement a smart EV route optimization system with charging forecasting. To calculate the best routes for electric cars, the system combines Django-based backend services, OSRM routing engine and PostgreSQL database storage. Rule-based feasibility checks, graph based routing using Dijkstra’s algorithm, heuristic search using A*, and an advanced state-space A* routing model that directly integrates battery constraints into the routing process are some of the stages through which the routing algorithm develops. The backend system works out the best routes for EVs to travel, predicts where they will need to stop to charge, simulates how much battery will be used on each segment of the route, and figures out how much time the whole trip will take, including any delays for charging. There is also a persistent caching mechanism in place to cut down on routing computations that have to be done over and over again and to make the system more scalable. Experimental evaluation shows that the suggested method makes it easier for electric vehicle users to find routes and plan their trips. Keywords – Electric Vehicles, Route Optimization, Charging Forecast, State-Space Routing, Smart Transportation Systems