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Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios

Author

Listed:
  • Pietro Stabile

    (Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy)

  • Federico Ballo

    (Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy)

  • Giorgio Previati

    (Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy)

  • Giampiero Mastinu

    (Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy)

  • Massimiliano Gobbi

    (Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy)

Abstract

This paper aims to provide a quantitative assessment of the effect of driver action and road traffic conditions in the real implementation of eco-driving strategies. The study specifically refers to an ultra-efficient battery-powered electric vehicle designed for energy-efficiency competitions. The method is based on the definition of digital twins of vehicle and driving scenario. The models are used in a driving simulator to accurately evaluate the power demand. The vehicle digital twin is built in a co-simulation environment between VI-CarRealTime and Simulink. A digital twin of the Brooklands Circuit (UK) is created leveraging the software RoadRunner. After validation with actual telemetry acquisitions, the model is employed offline to find the optimal driving strategy, namely, the optimal input throttle profile, which minimizes the energy consumption over an entire lap. The obtained reference driving strategy is used during real-time driving sessions at the dynamic driving simulator installed at Politecnico di Milano (DriSMi) to include the effects of human driver and road traffic conditions. Results assess that, in a realistic driving scenario, the energy demand could increase more than 20% with respect to the theoretical value. Such a reduction in performance can be mitigated by adopting eco-driving assistance systems.

Suggested Citation

  • Pietro Stabile & Federico Ballo & Giorgio Previati & Giampiero Mastinu & Massimiliano Gobbi, 2023. "Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios," Energies, MDPI, vol. 16(3), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1394-:d:1051727
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    References listed on IDEAS

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    1. Pietro Stabile & Federico Ballo & Gianpiero Mastinu & Massimiliano Gobbi, 2021. "An Ultra-Efficient Lightweight Electric Vehicle—Power Demand Analysis to Enable Lightweight Construction," Energies, MDPI, vol. 14(3), pages 1-18, February.
    2. Wang, Siyang & Lin, Xianke, 2020. "Eco-driving control of connected and automated hybrid vehicles in mixed driving scenarios," Applied Energy, Elsevier, vol. 271(C).
    3. Barkenbus, Jack N., 2010. "Eco-driving: An overlooked climate change initiative," Energy Policy, Elsevier, vol. 38(2), pages 762-769, February.
    4. Mahmoud Ibrahim & Anton Rassõlkin & Toomas Vaimann & Ants Kallaste, 2022. "Overview on Digital Twin for Autonomous Electrical Vehicles Propulsion Drive System," Sustainability, MDPI, vol. 14(2), pages 1-16, January.
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