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Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication

Author

Listed:
  • Carlo Fiorillo

    (Department of Mechanical and Aerospace Engineering, Center for Automotive Research and Sustainable Mobility (CARS), Politecnico di Torino, 10129 Torino, Italy)

  • Mattia Mauro

    (Department of Mechanical and Aerospace Engineering, Center for Automotive Research and Sustainable Mobility (CARS), Politecnico di Torino, 10129 Torino, Italy)

  • Atriya Biswas

    (McMaster Automotive Resource Center, McMaster University, Hamilto, ON L8P0A6, Canada)

  • Angelo Bonfitto

    (Department of Mechanical and Aerospace Engineering, Center for Automotive Research and Sustainable Mobility (CARS), Politecnico di Torino, 10129 Torino, Italy)

  • Ali Emadi

    (McMaster Automotive Resource Center, McMaster University, Hamilto, ON L8P0A6, Canada)

Abstract

Battery electric vehicles (BEVs) face challenges like their limited all-electric range, the discrepancy between promised and actual energy efficiency, and battery health degradation, despite their environmental benefits. This article proposes an optimal adaptive cruise control (OACC) framework by leveraging ideal vehicle-to-vehicle communication to address these challenges. In a connected vehicle environment, where it is assumed that the Ego vehicle’s vehicle control unit (VCU) accurately knows the speed and position of the Leading vehicle, the VCU can optimally plan the acceleration trajectory for a short-term future time window through a model predictive control (MPC) framework tailored to BEVs. The primary objective of the OACC is to reduce the energy consumption and battery state-of-health degradation of a BEV. The Chevrolet Spark 2015 is chosen as the BEV platform used to validate the effectiveness of the proposed OACC. Simulations conducted under urban and highway driving conditions, as well as under communication delay and infused noise, resulted in up to a 3.7% reduction in energy consumption and a 9.7% reduction in battery state-of-health (SOH) degradation, demonstrating the effectiveness and robustness of the proposed OACC.

Suggested Citation

  • Carlo Fiorillo & Mattia Mauro & Atriya Biswas & Angelo Bonfitto & Ali Emadi, 2024. "Designing a Real-Time Implementable Optimal Adaptive Cruise Control for Improving Battery Health and Energy Consumption in EVs through V2V Communication," Energies, MDPI, vol. 17(9), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:1986-:d:1380720
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    References listed on IDEAS

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    1. Lee, Heeyun & Kim, Kyunghyun & Kim, Namwook & Cha, Suk Won, 2022. "Energy efficient speed planning of electric vehicles for car-following scenario using model-based reinforcement learning," Applied Energy, Elsevier, vol. 313(C).
    2. Anselma, Pier Giuseppe & Biswas, Atriya & Belingardi, Giovanni & Emadi, Ali, 2020. "Rapid assessment of the fuel economy capability of parallel and series-parallel hybrid electric vehicles," Applied Energy, Elsevier, vol. 275(C).
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