The development of connected and automated vehicles (CAVs) enables improvements in the safety, smoothness, and energy efficiency of the road transportation systems. This paper addresses the problem of optimally controlling batteryelectric CAVs crossing an unsignalized intersection subject to a first-in-first-out crossing policy. The optimal velocity trajectory of each vehicle that minimizes the average energy consumption and travel time, is found by a decentralized model predictive control (DMPC) method via a convex modeling framework so as to ensure computational efficiency and the optimality of the solution. Numerical examples and comparisons with a centralized control counterpart demonstrate the effectiveness of the proposed decentralized coordination scheme and the trade-off between energy consumption and travel time. Further investigation into the size of the sampling interval is also provided in order to show the validity of the method in practice.


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    Titel :

    Decentralized Model Predictive Control for Automated and Connected Electric Vehicles at Signal-free Intersections


    Beteiligte:
    Pan, X (Autor:in) / Chen, B (Autor:in) / Dai, L (Autor:in) / Timotheou, S (Autor:in) / Evangelou, S (Autor:in)

    Erscheinungsdatum :

    2021-12-15


    Anmerkungen:

    In: Proceedings of the 60th IEEE Conference on Decision and Control (CDC 2021). IEEE: Austin, TX, USA. (2021) (In press).


    Medientyp :

    Paper


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    DDC:    629




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