This article proposes a surrogate-driven multiobjective predictive control (SMPC) strategy to address the dynamics uncertainty and multiobjective optimization issues of electric vehicular platoon (EVP). A surrogate-driven model is established with subspace identification to alleviate the adverse effects of uncertain dynamics for EVP. Then, a subspace predictor-based distributed surrogate-driven model predictive controller is developed for EVP. To mitigate conflicts among multiple optimization objectives involving driving safety, driving comfort, and energy economy, a multiobjective cost function with the predictive sequence is designed. To this end, a gray wolf optimizer is suggested to guide the search toward diverse solutions, aiming to achieve globally optimal tradeoffs among conflicting multiple objectives. In this way, the SMPC strategy is constructed, and its stability is theoretically proven. Finally, several experiments are carried out on a cosimulation vehicular platoon platform with the IPG-CarMaker software. The experimental results validate the effectiveness of the proposed SMPC strategy.


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    Title :

    Surrogate-Driven Multiobjective Predictive Control for Electric Vehicular Platoon


    Contributors:
    Wu, Yanhong (author) / Zuo, Zhiqiang (author) / Wang, Yijing (author) / Han, Qiaoni (author) / Li, Ji (author) / Zhou, Quan (author) / Xu, Hongming (author)

    Published in:

    Publication date :

    2024-12-01


    Size :

    14547058 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English