International Journal for Innovative Engineering and Management Research

The electrification of transportation, particularly city buses, presents significant opportunities for sustainability. Understanding real-world driving data driving data is crucial for efficient vehicle design and fleet management, as energy demand uncertainties often lead to inefficient and costly conservative design. Both industry and academia face challenges in addressing this issue due to the complexity and interdependence of various parameters.Accurate energy demand prediction is crucial for overcoming these obstacles can greatly reduce costs through optimized operations.This paper enhances the understanding of battery electric buses’(BEB) energy efficiency through machine learning models.