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Energy Uncertainty Analysis of Electric Buses

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  • Jari Vepsäläinen

    (Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland)

  • Antti Ritari

    (Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland)

  • Antti Lajunen

    (Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland
    Department of Agricultural Sciences, University of Helsinki, Koetilantie 5, 00014 Helsinki, Finland)

  • Klaus Kivekäs

    (Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland)

  • Kari Tammi

    (Department of Mechanical Engineering, School of Engineering, Aalto University, Puumiehenkuja 5, 02150 Espoo, Finland)

Abstract

Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.

Suggested Citation

  • Jari Vepsäläinen & Antti Ritari & Antti Lajunen & Klaus Kivekäs & Kari Tammi, 2018. "Energy Uncertainty Analysis of Electric Buses," Energies, MDPI, vol. 11(12), pages 1-29, November.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:12:p:3267-:d:185094
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    References listed on IDEAS

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    8. Heidary Dahooie, Jalil & Mohammadian, Ayoub & Qorbani, Ali Reza & Daim, Tugrul, 2023. "A portfolio selection of internet of things (IoTs) applications for the sustainable urban transportation: A novel hybrid multi criteria decision making approach," Technology in Society, Elsevier, vol. 75(C).
    9. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Hussein M. K. Al-Masri & Thanikanti Sudhakar Babu & Yap Hoon & Khaled Alzaareer & N. V. Phanendra Babu, 2021. "Review of the Estimation Methods of Energy Consumption for Battery Electric Buses," Energies, MDPI, vol. 14(22), pages 1-28, November.
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    11. Raka Jovanovic & Islam Safak Bayram & Sertac Bayhan & Stefan Voß, 2021. "A GRASP Approach for Solving Large-Scale Electric Bus Scheduling Problems," Energies, MDPI, vol. 14(20), pages 1-23, October.
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