The acquisition of multi-dimensional traffic information and constantly increasing computational power enable sophisticated control techniques to be applied in cruise control system. This study proposes a predictive cruise control (PCC) scheme based on model predictive control, which is formulated as a multi-objective nonlinear optimization problem. In order to facilitate the proposed PCC to deal with different driving conditions, a clustering method is used to identify the driving state of the preceding vehicle. Then, Bayesian optimization method is adopted to learn the optimal weighting parameters in the multi-objective optimization function, which can improve the control performance. Simulation results show that 2.83% fuel-saving rate can be obtained by applying Bayesian optimization method compared to fixed weighting parameters while maintaining good tracking ability and driving comfort.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predictive Cruise Control of Connected Vehicle With Online Parameters Learning


    Beteiligte:
    Wang, Yuhao (Autor:in) / Gong, Xun (Autor:in) / Lin, Jiamei (Autor:in) / Hu, Yunfeng (Autor:in) / Chen, Hong (Autor:in)


    Erscheinungsdatum :

    29.10.2021


    Format / Umfang :

    998559 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Data-driven Predictive Connected Cruise Control

    Shen, Minghao / Orosz, Gabor | IEEE | 2023



    Energy-Efficient Reactive and Predictive Connected Cruise Control

    Shen, Minghao / Dollar, Robert Austin / Molnar, Tamas G. et al. | IEEE | 2024



    Predictive adaptive cruise control

    LAHTI JOHN / BALTON CHRISTOPHER S / SCHRAMM ALEXANDER E et al. | Europäisches Patentamt | 2022

    Freier Zugriff