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Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression

Author

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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)

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.

Suggested Citation

  • Roman Michael Sennefelder & Rubén Martín-Clemente & Ramón González-Carvajal, 2023. "Energy Consumption Prediction of Electric City Buses Using Multiple Linear Regression," Energies, MDPI, vol. 16(11), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4365-:d:1157450
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    References listed on IDEAS

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    Cited by:

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    3. Magdalena Rykała & Małgorzata Grzelak & Łukasz Rykała & Daniela Voicu & Ramona-Monica Stoica, 2023. "Modeling Vehicle Fuel Consumption Using a Low-Cost OBD-II Interface," Energies, MDPI, vol. 16(21), pages 1-23, October.

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