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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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    1. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    2. Keller, Victor & Lyseng, Benjamin & Wade, Cameron & Scholtysik, Sven & Fowler, McKenzie & Donald, James & Palmer-Wilson, Kevin & Robertson, Bryson & Wild, Peter & Rowe, Andrew, 2019. "Electricity system and emission impact of direct and indirect electrification of heavy-duty transportation," Energy, Elsevier, vol. 172(C), pages 740-751.
    3. Vepsäläinen, Jari & Otto, Kevin & Lajunen, Antti & Tammi, Kari, 2019. "Computationally efficient model for energy demand prediction of electric city bus in varying operating conditions," Energy, Elsevier, vol. 169(C), pages 433-443.
    4. Lajunen, Antti & Lipman, Timothy, 2016. "Lifecycle cost assessment and carbon dioxide emissions of diesel, natural gas, hybrid electric, fuel cell hybrid and electric transit buses," Energy, Elsevier, vol. 106(C), pages 329-342.
    5. Hatem Abdelaty & Moataz Mohamed, 2021. "A Prediction Model for Battery Electric Bus Energy Consumption in Transit," Energies, MDPI, vol. 14(10), pages 1-26, May.
    6. Basso, Rafael & Kulcsár, Balázs & Sanchez-Diaz, Ivan, 2021. "Electric vehicle routing problem with machine learning for energy prediction," Transportation Research Part B: Methodological, Elsevier, vol. 145(C), pages 24-55.
    7. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    8. Gallet, Marc & Massier, Tobias & Hamacher, Thomas, 2018. "Estimation of the energy demand of electric buses based on real-world data for large-scale public transport networks," Applied Energy, Elsevier, vol. 230(C), pages 344-356.
    9. Li, Pengshun & Zhang, Yuhang & Zhang, Yi & Zhang, Yi & Zhang, Kai, 2021. "Prediction of electric bus energy consumption with stochastic speed profile generation modelling and data driven method based on real-world big data," Applied Energy, Elsevier, vol. 298(C).
    10. Kalghatgi, Gautam, 2018. "Is it really the end of internal combustion engines and petroleum in transport?," Applied Energy, Elsevier, vol. 225(C), pages 965-974.
    11. Gao, Zhiming & Lin, Zhenhong & LaClair, Tim J. & Liu, Changzheng & Li, Jan-Mou & Birky, Alicia K. & Ward, Jacob, 2017. "Battery capacity and recharging needs for electric buses in city transit service," Energy, Elsevier, vol. 122(C), pages 588-600.
    12. Klaus Kivekäs & Antti Lajunen & Jari Vepsäläinen & Kari Tammi, 2018. "City Bus Powertrain Comparison: Driving Cycle Variation and Passenger Load Sensitivity Analysis," Energies, MDPI, vol. 11(7), pages 1-26, July.
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