Intelligent Electric Vehicle Route Planning System with ML-Based Energy Consumption Prediction

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.

Electric Vehicle, Route Planning, Machine Learning, Gradient Boosting, Range Anxiety, Flutter, Fast API

1. IEA, “Global EV Outlook 2024,” Paris, 2024. [Google Scholar] [Crossref]

2. A. Artmeier et al., “Optimal routing for EVs,” KI 2010, pp. 309–316. [Google Scholar] [Crossref]

3. M. Baum et al., “Energy-optimal EV routes,” 21st SIGSPATIAL, pp. 54–63, 2013. [Google Scholar] [Crossref]

4. T. Chen, C. Guestrin, “XGBoost,” 22nd KDD, pp. 785–794, 2016. [Google Scholar] [Crossref]

5. J. Friedman, “Gradient boosting,” Ann. Stat., vol. 29, pp. 1189–1232, 2001. [Google Scholar] [Crossref]

6. S. De Cauwer et al., “Energy prediction,” Energies, vol. 10, p. 1013, 2017. [Google Scholar] [Crossref]

7. F. Pedregosa et al., “Scikit-learn,” JMLR, vol. 12, pp. 2825–2830, 2011. [Google Scholar] [Crossref]

8. S. Ramírez, “FastAPI,” 2024. [Google Scholar] [Crossref]

9. OSM, “Nominatim,” 2024. [Google Scholar] [Crossref]

10. D. Luxen, C. Vetter, “OSRM,” 19th SIGSPATIAL, pp. 513–516, 2011. [Google Scholar] [Crossref]

11. OpenChargeMap, “API docs,” 2024. [Google Scholar] [Crossref]

12. OpenWeatherMap, “Weather API,” 2024. [Google Scholar] [Crossref]

13. Flutter Team, “Flutter,” Google, 2024. [Google Scholar] [Crossref]

14. J. Vepsäläinen et al., “Electric bus energy,” Energy, vol. 169, pp. 433–443, 2019. [Google Scholar] [Crossref]

15. R. Galvin, “Speed effects on EVs,” Trans. Res. D, vol. 53, pp. 234–248, 2017. [Google Scholar] [Crossref]

16. A. Fetene et al., “Big data EV energy,” Trans. Res. D, vol. 54, pp. 1–11, 2017. [Google Scholar] [Crossref]

17. M. Steinstraeter et al., “Low temp EVs,” World EV J., vol. 12, p. 115, 2021. [Google Scholar] [Crossref]

18. J. Betz et al., “Autonomous driving,” IEEE Access, vol. 10, pp. 99131–99168, 2022. [Google Scholar] [Crossref]

19. P. Keil, A. Jossen, “Li-ion aging,” World EV J., vol. 7, pp. 41–51, 2015. [Google Scholar] [Crossref]

20. D. Wang et al., “EV battery degradation,” J. Power Sources, vol. 332, pp. 193–203, 2016. [Google Scholar] [Crossref]

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