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A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems

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  • Alessia Musa

    (Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10125 Torino, Italy
    Center for Automotive Research and Sustainable Mobility (CARS), Politecnico di Torino, 10125 Torino, Italy)

  • Michele Pipicelli

    (Department of Industrial Engineering, University of Naples Federico II, 80126 Napoli, Italy
    Istituto di Scienze e Tecnologie per l’Energia e la Mobilità Sostenibili (STEMS), 80125 Napoli, Italy
    Teoresi S.P.A., 10152 Torino, Italy)

  • Matteo Spano

    (Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10125 Torino, Italy
    Center for Automotive Research and Sustainable Mobility (CARS), Politecnico di Torino, 10125 Torino, Italy)

  • Francesco Tufano

    (Department of Industrial Engineering, University of Naples Federico II, 80126 Napoli, Italy)

  • Francesco De Nola

    (Teoresi S.P.A., 10152 Torino, Italy)

  • Gabriele Di Blasio

    (Istituto di Scienze e Tecnologie per l’Energia e la Mobilità Sostenibili (STEMS), 80125 Napoli, Italy)

  • Alfredo Gimelli

    (Department of Industrial Engineering, University of Naples Federico II, 80126 Napoli, Italy)

  • Daniela Anna Misul

    (Department of Mechanical and Aerospace Engineering (DIMEAS), Politecnico di Torino, 10125 Torino, Italy
    Center for Automotive Research and Sustainable Mobility (CARS), Politecnico di Torino, 10125 Torino, Italy)

  • Gianluca Toscano

    (Teoresi S.P.A., 10152 Torino, Italy)

Abstract

Advanced Driver-Assistance Systems (ADASs) are currently gaining particular attention in the automotive field, as enablers for vehicle energy consumption, safety, and comfort enhancement. Compelling evidence is in fact provided by the variety of related studies that are to be found in the literature. Moreover, considering the actual technology readiness, larger opportunities might stem from the combination of ADASs and vehicle connectivity. Nevertheless, the definition of a suitable control system is not often trivial, especially when dealing with multiple-objective problems and dynamics complexity. In this scenario, even though diverse strategies are possible (e.g., Equivalent Consumption Minimization Strategy, Rule-based strategy, etc.), the Model Predictive Control (MPC) turned out to be among the most effective ones in fulfilling the aforementioned tasks. Hence, the proposed study is meant to produce a comprehensive review of MPCs applied to scenarios where ADASs are exploited and aims at providing the guidelines to select the appropriate strategy. More precisely, particular attention is paid to the prediction phase, the objective function formulation and the constraints. Subsequently, the interest is shifted to the combination of ADASs and vehicle connectivity to assess for how such information is handled by the MPC. The main results from the literature are presented and discussed, along with the integration of MPC in the optimal management of higher level connection and automation. Current gaps and challenges are addressed to, so as to possibly provide hints on future developments.

Suggested Citation

  • Alessia Musa & Michele Pipicelli & Matteo Spano & Francesco Tufano & Francesco De Nola & Gabriele Di Blasio & Alfredo Gimelli & Daniela Anna Misul & Gianluca Toscano, 2021. "A Review of Model Predictive Controls Applied to Advanced Driver-Assistance Systems," Energies, MDPI, vol. 14(23), pages 1-24, November.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7974-:d:690760
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    References listed on IDEAS

    as
    1. Antonio Capuano & Matteo Spano & Alessia Musa & Gianluca Toscano & Daniela Anna Misul, 2021. "Development of an Adaptive Model Predictive Control for Platooning Safety in Battery Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-14, August.
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    3. He, Hongwen & Wang, Yunlong & Han, Ruoyan & Han, Mo & Bai, Yunfei & Liu, Qingwu, 2021. "An improved MPC-based energy management strategy for hybrid vehicles using V2V and V2I communications," Energy, Elsevier, vol. 225(C).
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    5. Ma, Fangwu & Yang, Yu & Wang, Jiawei & Liu, Zhenze & Li, Jinhang & Nie, Jiahong & Shen, Yucheng & Wu, Liang, 2019. "Predictive energy-saving optimization based on nonlinear model predictive control for cooperative connected vehicles platoon with V2V communication," Energy, Elsevier, vol. 189(C).
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    Cited by:

    1. David Sotelo & Antonio Favela-Contreras & Alfonso Avila & Arturo Pinto & Francisco Beltran-Carbajal & Carlos Sotelo, 2022. "A New Software-Based Optimization Technique for Embedded Latency Improvement of a Constrained MIMO MPC," Mathematics, MDPI, vol. 10(15), pages 1-19, July.
    2. Pier Giuseppe Anselma, 2022. "Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health," Energies, MDPI, vol. 15(5), pages 1-25, February.
    3. Yesid Bello & Juan Sebastian Roncancio & Toufik Azib & Diego Patino & Cherif Larouci & Moussa Boukhnifer & Nassim Rizoug & Fredy Ruiz, 2023. "Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption," Energies, MDPI, vol. 16(4), pages 1-26, February.
    4. Maciej Ławryńczuk & Piotr M. Marusak & Patryk Chaber & Dawid Seredyński, 2022. "Initialisation of Optimisation Solvers for Nonlinear Model Predictive Control: Classical vs. Hybrid Methods," Energies, MDPI, vol. 15(7), pages 1-21, March.
    5. Pedro Bautista-Camino & Alejandro I. Barranco-Gutiérrez & Ilse Cervantes & Martin Rodríguez-Licea & Juan Prado-Olivarez & Francisco J. Pérez-Pinal, 2022. "Local Path Planning for Autonomous Vehicles Based on the Natural Behavior of the Biological Action-Perception Motion," Energies, MDPI, vol. 15(5), pages 1-23, February.

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