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Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation

Author

Listed:
  • Bianca Caiazzo

    (Department of Information Technology and Electrical Engineering (DIETI), University of Naples Federico II, 80125 Naples, Italy
    Authors are in alphabetic order.)

  • Angelo Coppola

    (Department of Information Technology and Electrical Engineering (DIETI), University of Naples Federico II, 80125 Naples, Italy
    Authors are in alphabetic order.)

  • Alberto Petrillo

    (Department of Information Technology and Electrical Engineering (DIETI), University of Naples Federico II, 80125 Naples, Italy
    Authors are in alphabetic order.)

  • Stefania Santini

    (Department of Information Technology and Electrical Engineering (DIETI), University of Naples Federico II, 80125 Naples, Italy
    Authors are in alphabetic order.)

Abstract

This paper addresses the leader tracking problem for a platoon of heterogeneous autonomous connected fully electric vehicles where the selection of the inter-vehicle distance between adjacent vehicles plays a crucial role in energy consumption reduction. In this framework, we focused on the design of a cooperative driving control strategy able to let electric vehicles move as a convoy while keeping a variable energy-oriented inter-vehicle distance between adjacent vehicles which, depending on the driving situation, was reduced as much as possible to guarantee air-drag reduction, energy saving and collision avoidance. To this aim, by exploiting a distance-dependent air drag coefficient formulation, we propose a novel distributed nonlinear model predictive control (DNMPC) where the cost function was designed to ensure leader tracking performances, as well as to optimise the inter-vehicle distance with the aim of reducing energy consumption. Extensive simulation analyses, involving a comparative analysis with respect to the classical constant time headway (CTH) spacing policy, were performed to confirm the capability of the DNMPC in guaranteeing energy saving.

Suggested Citation

  • Bianca Caiazzo & Angelo Coppola & Alberto Petrillo & Stefania Santini, 2021. "Distributed Nonlinear Model Predictive Control for Connected Autonomous Electric Vehicles Platoon with Distance-Dependent Air Drag Formulation," Energies, MDPI, vol. 14(16), pages 1-17, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5122-:d:617648
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    Citations

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    Cited by:

    1. Yaqian Wang & Xiaohong Jiao, 2022. "Dual Heuristic Dynamic Programming Based Energy Management Control for Hybrid Electric Vehicles," Energies, MDPI, vol. 15(9), pages 1-19, April.
    2. Nicola Albarella & Dario Giuseppe Lui & Alberto Petrillo & Stefania Santini, 2023. "A Hybrid Deep Reinforcement Learning and Optimal Control Architecture for Autonomous Highway Driving," Energies, MDPI, vol. 16(8), pages 1-19, April.

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