A dataset for state of charge and range estimation of an L5 type electric vehicle that is used for Urban Logistic

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.