Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression

The widespread electrification of public transportation is increasing and a powerful way to reduce greenhouse gas (GHG) emissions. Using real-world driving data crucial for vehicle design efficient fleet operation. Although electric powertrains are significantly superior conventional combustion engines in many aspects, such as efficiency, dynamics, noise or pollution maintenance, there several factors that still hinder the penetration e-mobility. One most critical points high costs—especially battery buses (BEB) due expensive energy storage systems. Uncertainty about demand target scenario leads conservative design, inefficient operation costs. This paper based on real case study city Seville presents methodology support transformation We investigate large measurement introduce analyze second-stage feature space finally predict vehicles’ using statistical algorithms. Achieving prediction accuracy more than 85%, this simple approach proper tool manufacturers operators provide tailored mobility solutions thus affordable sustainable transportation.