Impact of topographic data on electric vehicle energy prediction
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- Category: Content №5 2025
- Last Updated on 25 October 2025
- Published on 30 November -0001
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Authors:
O. S. Beshta, orcid.org/0000-0002-4648-0260, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
D. O. Beshta, orcid.org/0000-0003-2848-2737, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
T. O. Khalaimov*, orcid.org/0000-0002-0171-8503, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
V. A. Burlaka, orcid.org/0009-0007-7230-3077, Dnipro University of Technology, Dnipro, Ukraine, e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
* Corresponding author e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.
Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu. 2025, (5): 078 - 085
https://doi.org/10.33271/nvngu/2025-5/078
Abstract:
Purpose. To assess the impact of sampling frequency and topographic data sources on the accuracy of electric vehicle (EV) driving energy consumption calculations using standardized test protocols.
Methodology. The study calculates the driving energy consumption of an EV based on a theoretical model that incorporates the topographic profile of the route and the driving mode defined by the selected test protocol. The analysis was conducted for several alternative paths of the same route along European roads under varying discretization steps and topographic data sources.
Findings. It was established that the sampling frequency and accuracy of topographic data significantly affect the results of energy consumption calculations. A range of discretization steps ensuring acceptable accuracy while reducing computational load was identified. Errors for different data sources were quantified depending on the characteristics of the route’s topographic profile.
Originality. The study establishes the relationship between the accuracy of electric vehicle driving energy calcula-tions and both the sampling frequency and the selected topographic data source, taking into account topographic profile. Threshold conditions were identified under which increasing the discretization step or using less accurate datasets does not lead to significant errors.
Practical value. The obtained results enable a well-grounded selection of the optimal discretization step and topographic data source to improve the accuracy of energy consumption forecasting for passenger EVs during route planning.
Keywords: electric vehicles, route planning, topological profile, WLTP, discretization, topological bases
References.
1. Guzek, M., Jackowski, J., Jurecki, R. S., Szumska, E. M., Zdanowicz, P., & Żmuda, M. (2024). Electric Vehicles—An Overview of Current Issues – Part 2 – Infrastructure and Road Safety. Energies, 17(2), Article 495. https://doi.org/10.3390/en17020495
2. Eurostat. (2025). Trade and production of hybrid and electric cars. Retrieved July 24, 2025, from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Trade_and_production_of_hybrid_and_electric_cars
3. Ritchie, H., & Roser, M. (2024, March). Tracking global data on electric vehicles. Our World in Data. Retrieved December 16, 2024, from https://ourworldindata.org/electric-car-sales
4. European Alternative Fuels Observatory. (2024). Incentives and Legislation | European Alternative Fuels Observatory. Retrieved December 16, 2024, from https://alternative-fuels-observatory.ec.europa.eu/transport-mode/road/norway/incentives-legislations
5. Kang, J., Kan, C., & Lin, Z. (2021). Are electric vehicles reshaping the city? An investigation of the clustering of electric vehicle owners’ dwellings and their interaction with urban spaces. ISPRS International Journal of Geo-Information, 10(5), 320. https://doi.org/10.3390/ijgi10050320
6. Bazhinov, O., Saukhanov, N., Kravtsov, M., Taran, I., & Bazhynova, T. (2025). Optimization of electromagnetic radiation by hybrid and electric vehicles. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (3), 107-118. https://doi.org/10.33271/nvngu/2025-3/107
7. Alanazi, F. (2023). Electric Vehicles: Benefits, Challenges, and Potential Solutions for Widespread Adaptation. Applied Sciences, 13(10), Article 6016. https://doi.org/10.3390/app13106016
8. Piatkowski, P., & Puszkiewicz, W. (2018). Electric vehicles: Problems or solutions. Journal of Mechanical and Energy Engineering, 2(1), Article 59. https://doi.org/10.30464/jmee.2018.2.1.59
9. Szumska, E. M. (2025). Regenerative Braking Systems in Electric Vehicles: A Comprehensive Review of Design, Control Strategies, and Efficiency Challenges. Energies, 18(10), Article 2422. https://doi.org/10.3390/en18102422
10. Vasiljević, S., Aleksandrović, B., Glišović, J., & Maslać, M. (2022). Regenerative braking on electric vehicles: Working principles and benefits of application. IOP Conference Series: Materials Science and Engineering, 1271(1), 012025. https://doi.org/10.1088/1757-899X/1271/1/012025
11. Skuza, A., & Jurecki, R. S. (2022). Analysis of factors affecting the energy consumption of an EV vehicle – A literature study. IOP Conference Series: Materials Science and Engineering, 1247(1), 012001. https://doi.org/10.1088/1757-899X/1247/1/012001
12. Lee, G., Song, J., Lim, Y., & Park, S. (2024). Energy consumption evaluation of passenger electric vehicle based on ambient temperature under real-world driving conditions. Energy Conversion and Management, 306, 118289. https://doi.org/10.1016/j.enconman.2024.118289
13. Google. (n.d.). Google Maps [Web application]. Retrieved July 1, 2025, from https://www.google.com/maps
14. Jonas, T., Wilde, T., Hunter, C. D., & Macht, G. A. (2022). The impact of road types on the energy consumption of electric vehicles. Journal of Advanced Transportation, 2022, 1436385. https://doi.org/10.1155/2022/1436385
15. Donkers, A., Yang, D., & Viktorović, M. (2020). Influence of driving style, infrastructure, weather and traffic on electric vehicle performance. Transportation Research Part D: Transport and Environment, 88, 102569. https://doi.org/10.1016/j.trd.2020.102569
16. Liu, Y., Ma, K., Yu, H., Li, J., & An, X. (2021). Influence of test cycles on energy consumption test of electric vehicles. E3S Web of Conferences, 241, 02004. https://doi.org/10.1051/e3sconf/202124102004
17. Hu, Z., Peng, J., Hou, Y., & Shan, J. (2017). Evaluation of recently released open global digital elevation models of Hubei, China. Remote Sensing, 9(3), Article 262. https://doi.org/10.3390/rs9030262
18. Rabah, M., El-Hattab, A., & Abdallah, M. (2017). Assessment of the most recent satellite-based digital elevation models of Egypt. NRIAG Journal of Astronomy and Geophysics, 6(2), 326–335. https://doi.org/10.1016/j.nrjag.2017.10.006
19. USGS. (2025). EarthExplorer. Retrieved February 4, 2025, from https://earthexplorer.usgs.gov/
20. Sonny. (2025). LiDAR Digital Terrain Models of Europe. Retrieved February 4, 2025, from https://sonny.4lima.de/
21. Beshta, O.S., Beshta, O.O., Khudolii, S.S., Khalaimov, T.O., & Fedoreiko, V.S. (2024). Electric vehicle energy consumption taking into account the route topology. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, (2), 104-112. https://doi.org/10.33271/nvngu/2024-2/104
22. Schneider, A. (2025). GPS Visualizer: Assign DEM elevation data to coordinates. Retrieved May 6, 2025, from https://www.gpsvisualizer.com/elevation
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