Predicting Energy Consumption of Battery-Operated Electric Vehicles: A Comparative Performance Assessment

Predicting the real-time energy consumption of battery-operated electric vehicles (BEVs) remains criticalin identifying energy-efficient routes and charging stations. However, accurately predicting energy consumption depends on various environmental factors, such as wind speed, wind direction, temperature, humidity, and precipi-tation. Although the existing time series (TS) models offer valuable insights into energy consumption prediction and data trends, they need to generate more accurate outcomes, especially with real-time data. Moreover, obtaining comprehensive and accurate data pertaining to BEV energy consumption poses significant challenges. The scarcity of data can hinder the progress of research in this area. Therefore, this study aims to identify an efficient prediction model by comparing it with existing models, which will con-tribute to developing more accurate and efficient prediction models for BEV power consumption. We selected particular environmental factors that were expected to impact BEV energy consumption. These findings also involved acquiring BEV energy consumption data and integrating it with various environmental parameters.