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Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility

Author

Listed:
  • Simin Hesami

    (The Mobility, Logistics, and Automotive Technology Research Center, Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, Belgium)

  • Majid Vafaeipour

    (The Mobility, Logistics, and Automotive Technology Research Center, Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, Belgium)

  • Cedric De Cauwer

    (The Mobility, Logistics, and Automotive Technology Research Center, Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, Belgium)

  • Evy Rombaut

    (The Mobility, Logistics, and Automotive Technology Research Center, Department of Business Technology and Operations, Vrije Universiteit Brussel, 1050 Brussels, Belgium)

  • Lieselot Vanhaverbeke

    (The Mobility, Logistics, and Automotive Technology Research Center, Department of Business Technology and Operations, Vrije Universiteit Brussel, 1050 Brussels, Belgium)

  • Thierry Coosemans

    (The Mobility, Logistics, and Automotive Technology Research Center, Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, Belgium)

Abstract

As autonomous vehicle technology advances, the development of energy-efficient control methodologies emerges as a critical area in the literature. This includes the behavior control of vehicles near signalized intersections, which still needs comprehensive exploration. Through connectivity, the adoption of promising eco-driving approaches can manage a vehicle’s speed profile to improve energy consumption. This study focuses on controlling the speed of an autonomous electric vehicle (AEV) both up and downstream of a signalized intersection in the presence of preceding vehicles. In order to achieve this, a dynamic pro-active predictive cruise control eco-driving (eco-PPCC) framework is developed that, instead of merely reacting to the preceding vehicle’s speed changes, uses the preceding vehicle’s upcoming data to actively adjust and optimize the speed profile of the AEV. The proposed algorithm is compared to the conventional Gipps and eco-PCC models for benchmarking and performance analysis through numerous scenarios. Additionally, real-world measurements are performed and taken to consider practical use cases. The results demonstrate that when compared to the two baseline methods, the proposed framework can add significant value to reducing energy consumption, preventing unnecessary stops at intersections, and improving travel time.

Suggested Citation

  • Simin Hesami & Majid Vafaeipour & Cedric De Cauwer & Evy Rombaut & Lieselot Vanhaverbeke & Thierry Coosemans, 2023. "Dynamic Pro-Active Eco-Driving Control Framework for Energy-Efficient Autonomous Electric Mobility," Energies, MDPI, vol. 16(18), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6495-:d:1235993
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    References listed on IDEAS

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    1. Zifei Nie & Hooman Farzaneh, 2021. "Role of Model Predictive Control for Enhancing Eco-Driving of Electric Vehicles in Urban Transport System of Japan," Sustainability, MDPI, vol. 13(16), pages 1-37, August.
    2. Tang, Tie-Qiao & Yi, Zhi-Yan & Zhang, Jian & Wang, Tao & Leng, Jun-Qiang, 2018. "A speed guidance strategy for multiple signalized intersections based on car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 399-409.
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