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Energy Management Improvement Based on Fleet Digitalization Data Exploitation for Hybrid Electric Buses

In: Computational Intelligence and Optimization Methods for Control Engineering

Author

Listed:
  • Jon Ander López

    (IKERLAN Technology Research Centre, Energy Storage and Management Area)

  • Victor Isaac Herrera

    (Escuela Superior Politécnica de Chimborazo)

  • Haritza Camblong

    (University of the Basque Country
    ESTIA Research)

  • Aitor Milo

    (IKERLAN Technology Research Centre, Energy Storage and Management Area)

  • Haizea Gaztañaga

    (IKERLAN Technology Research Centre, Energy Storage and Management Area)

Abstract

The chapter focuses on a fleet energy management approach with the aim of reducing operation and maintenance costs. A state-of-the-art is presented for the different proposed fleet management approaches. In order to tackle the digitalization challenge of exploiting the large data volume of a fleet of vehicles, a methodology for improving electrified buses energy efficiency at fleet level is proposed. In addition, an energetic analysis of a fleet based on this methodology has been performed. The analyzed fleet is composed of buses with parallel and series configurations and include energy storage systems based on batteries and ultracapacitors. In the first stage, a dynamic programming approach has been applied to determine the initial optimal operation performance for each bus route. Then, several disruptions (e.g., traffic jams, auxiliary consumption, and passenger variations) have been added to the routes to simulate “real” road and daily operation conditions. This data is used for monitoring the energetic key performance factors by learning from the buses with the best energetic behavior. Finally, a decision-making process is applied to improve the local energy management of the less-efficient bus.

Suggested Citation

  • Jon Ander López & Victor Isaac Herrera & Haritza Camblong & Aitor Milo & Haizea Gaztañaga, 2019. "Energy Management Improvement Based on Fleet Digitalization Data Exploitation for Hybrid Electric Buses," Springer Optimization and Its Applications, in: Maude Josée Blondin & Panos M. Pardalos & Javier Sanchis Sáez (ed.), Computational Intelligence and Optimization Methods for Control Engineering, chapter 0, pages 321-355, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-25446-9_14
    DOI: 10.1007/978-3-030-25446-9_14
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    Cited by:

    1. Afaq Ahmad & Muhammad Khalid & Zahid Ullah & Naveed Ahmad & Mohammad Aljaidi & Faheem Ahmed Malik & Umar Manzoor, 2022. "Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging," Energies, MDPI, vol. 15(24), pages 1-32, December.
    2. López-Ibarra, Jon Ander & Gaztañaga, Haizea & Saez-de-Ibarra, Andoni & Camblong, Haritza, 2020. "Plug-in hybrid electric buses total cost of ownership optimization at fleet level based on battery aging," Applied Energy, Elsevier, vol. 280(C).

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