Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression

Roman Michael Sennefelder (), Rubén Martín-Clemente and Ramón González-Carvajal
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Roman Michael Sennefelder: EVO Engineering GmbH, 80807 Munich, Germany
Rubén Martín-Clemente: Signal Processing and Communications Department, University of Seville, 41004 Seville, Spain
Ramón González-Carvajal: Signal Processing and Communications Department, University of Seville, 41004 Seville, Spain

Energies, 2023, vol. 16, issue 11, 1-14

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

Keywords: battery electric buses; energy demand prediction; feature extraction; multiple linear regression; statistics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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