Analysis of Factors Affecting Electric Vehicle Range Estimation: A Case Study of the Eskisehir Osmangazi University Campus

Ahmet Alperen Polat (), Sinem Bozkurt Keser, İnci Sarıçiçek and Ahmet Yazıcı
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Ahmet Alperen Polat: Center of Intelligent Systems Applications Research, Eskisehir Osmangazi University, Eskişehir 26040, Türkiye
Sinem Bozkurt Keser: Department of Computer Engineering, Eskişehir Osmangazi University, Eskişehir 26040, Türkiye
İnci Sarıçiçek: Center of Intelligent Systems Applications Research, Eskisehir Osmangazi University, Eskişehir 26040, Türkiye
Ahmet Yazıcı: Center of Intelligent Systems Applications Research, Eskisehir Osmangazi University, Eskişehir 26040, Türkiye

Sustainability, 2025, vol. 17, issue 8, 1-23

Abstract: In recent years, electric vehicles have become increasingly widespread, both in the logistics sector and in personal use. This increase, together with factors such as environmental concerns and government incentives, has brought energy consumption and range estimation issues to the forefront. In this study, the energy consumption of an electric cargo vehicle under different speed and load conditions is examined with an experimental and data-driven approach, and then used for range estimation. The raw data collected from the vehicle on the selected ~2 km route in Eskisehir Osmangazi University campus are combined into per-second samples with time synchronization and data cleaning. The route is divided into average of 150 m segments, and variables such as slope, energy consumption, and acceleration are calculated for each segment. Then, the data are used to train various machine learning models, such as Extra Trees, CatBoost, LightGBM, Voting Regressor, and XGBoost, and their performances regarding energy consumption-based range estimation are compared. The findings show that driving dynamics such as high speed and sudden acceleration, as well as road slope and load conditions, significantly shape the energy consumption and thus the remaining range. In particular, Extra Trees outperforms other machine learning models in terms of metrics such as R 2 , RMSE and, MAE, with a reasonable computational time. The results provide applicable guidance in areas such as route optimization, smart battery management, and charging infrastructure to reduce range anxiety and increase the operational efficiency of electric vehicles.

Keywords: electric vehicle; energy consumption; range estimation; machine learning; logistics; road gradient; data analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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