Data Driven Energy Economy Prediction for Electric City Buses using Machine Learning

Author(s)	Aasritha Reddy Dwarampudi, Anirudh Beldona, Kruthika Kemmasaram, Janardhana Rao S
Country	India
Abstract	Electric buses are gaining popularity in city buses in attempt to have cleaner transportation. To make proper design of these buses and have them running effectively, it is necessary to learn to know how they will be utilized in practice. Nevertheless, it may be difficult to predict their energy requirements, and thus they are overly designed. This paper relies on actual driving experiences and machine learning to better estimate the amount of energy that electric buses will consume. The authors tested five machine learning models with the help of features carefully selected on speed profiles. One of the models had a higher accuracy of more than 94%. The findings can assist businesses and cities in reducing expenses and operating electric buses in a more effective way.
Keywords	Electric buses, energy prediction, machine learning, spectral entropy, fleet management, vehicle routing, public transport electrification, speed profiles
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-01-22
DOI	https://doi.org/10.36948/ijfmr.2026.v08i01.65349

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