With the concept of cloud control system (CCS) for intelligent and connected vehicles proposed and developed, the vehicles and traffic safety and economy have a comprehensive improvement. However, the current research mostly stays in vehicle-to-vehicle or vehicle-to-infrastructure, which requires an on-board calculator with large computational and storage capacity, and the planning range is short. To solve these problems, a predictive adaptive cruise control (PACC) algorithm based on CCS is proposed. Hierarchical control architecture of PACC between vehicle and cloud is constructed, coordinating free cruising mode with car-following mode. For the free cruising mode, an ergodic optimization method placed in the cloud is designed. A multi-objective optimization algorithm based on model predictive control is designed for the car-following mode, which can plan the vehicle speed in advance. Finally, the simulation results show that the PACC algorithm can achieve 8.16% fuel saving with consuming less time for free cruising, and for car-following, it can achieve 3.23% fuel saving. More importantly, the results also show that the CCS has significant potential and feasibility for intelligent and connected vehicles application.


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    Title :

    Predictive Adaptive Cruise Control for Heavy-duty Vehicle based on Cloud Control System


    Contributors:
    Li, Shuyan (author) / Li, Rui (author) / Gao, Bolin (author) / Wan, Keke (author) / Li, Keqiang (author) / Wang, Xuepeng (author) / Wang, Daniel (author) / Yang, Dongbo (author)


    Publication date :

    2022-10-08


    Size :

    679388 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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