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Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-network-based energy consumption model

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  • Nie, Zifei
  • Farzaneh, Hooman

Abstract

This paper proposes a real-time dynamic predictive cruise control (PCC) system to minimize the energy consumption for electric vehicles (EVs) under integrated traffic situations with synthetic driving scenarios, considering both constraints from the preceding vehicle and the influence of traffic signal lights. The proposed PCC system is working based on the bi-level model predictive control (MPC) algorithm. The Signal Phase and Timing (SPaT)-oriented MPC calculates a desired acceleration command as the optimal control signal at each sampling step based on the forthcoming SPaT information with the purpose of passing the nearest signalized intersection during the green light interval without stop. The car-following-oriented MPC executes preceding vehicle tracking task through maintaining a safe inter-distance using a customized variable time headway (VTH) strategy. The instantaneous energy consumption for EV in different traffic scenarios was quantified by a data-driven model. The developed system was validated through comparison with IDM and human driver's maneuver in both suburban and urban areas road in the city of Fukuoka, Japan, during off-peak and peak hours, using the real traffic system and SPaT data. To further evaluate the performance of the proposed PCC system in high speed driving situation, another case study with transitions from highway to urban road was conducted. The simulative results showed that the proposed PCC system can realize the energy-saving rates by 8.5%–15.6%. And it was working well and robustly under high speed driving situation.

Suggested Citation

  • Nie, Zifei & Farzaneh, Hooman, 2022. "Real-time dynamic predictive cruise control for enhancing eco-driving of electric vehicles, considering traffic constraints and signal phase and timing (SPaT) information, using artificial-neural-netw," Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:energy:v:241:y:2022:i:c:s0360544221031376
    DOI: 10.1016/j.energy.2021.122888
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    References listed on IDEAS

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    Cited by:

    1. Luca Pulvirenti & Luigi Tresca & Luciano Rolando & Federico Millo, 2023. "Eco-Driving Optimization Based on Variable Grid Dynamic Programming and Vehicle Connectivity in a Real-World Scenario," Energies, MDPI, vol. 16(10), pages 1-19, May.

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