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 R², 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.

Light electric vehicles contribute to sustainable urban logistics. However, range anxiety is a significant problem in the use of electric vehicles. State of Charge and range estimation are of critical importance to reduce range anxiety. Although there are studies and datasets on the State of Charge and Range Estimation of passenger electric vehicles, the literature on L5 type electric vehicles is not yet mature enough. In this study, measurement data are obtained from an L5 class electric vehicle for urban cargo transportation under different driving dynamics and load conditions. The raw sensor data obtained via the Controller Area Network Bus in the vehicle are stored in 35 separate comma-seperated value files and recording frequencies of up to 800 per second are captured. Then, the cleaning process converts the raw data into one-second intervals. The data is recorded during test drives performed on a loop route of approximately two kilometres, under different slopes and environmental conditions, at speeds of 15, 25 and 35 km/h, both loaded and unloaded. The presented ESOGU-ML5EV dataset provides a reusable and organized infrastructure for those who want to analyse the energy consumption of electric vehicles used in urban logistics, examine the factors affecting consumption, conduct range estimation studies, or investigate electric vehicle routing problems.