Intelligent Electric Vehicle Route Planning System with ML-Based Energy Consumption Prediction
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As electric vehicles gain traction across the globe, one persistent worry among drivers is whether their battery will last long enough to reach the next charging point, a concern commonly referred to as range anxiety. In this paper, we describe a practical route planning tool that tackles this problem head-on. At its core sits a Gradient Boosting Regressor trained on 20,000 synthetically generated trip records whose parameters are rooted in real-world physics. The model takes in the vehicle type, how much cargo is on board, the trip distance, driving speed, terrain changes, and outside temperature, and outputs an energy consumption estimate. On the server side, a FastAPI application pulls together driving directions from OSRM, live weather readings from OpenWeatherMap, elevation data from Open-Elevation, and nearby charger locations from OpenChargeMap. A step-by-step greedy algorithm then figures out where the driver should stop to recharge, while also factoring in how much the battery may have degraded over time. The accompanying mobile app, built with Flutter, shows the planned route on an interactive map and even works offline thanks to local caching. In our tests, the prediction model achieved an R² above 0.95 on unseen data.
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