Abstract In order to pursue efficiency improvement for vehicles, embedding the information of connected vehicle and infrastructure to the powertrain system control is taken as potential ways. This paper proposes an integrated control framework to deal with the energy consumption optimization problem for a hybrid electric vehicle (HEV). Taking the vehicle motion dynamics as constraint, a receding horizon control (RHC) scheme is developed to get a short-term optimal solution of power distribution for the HEV. To further improve the vehicle performance, an extreme learning machine (ELM) model is developed with the information of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) to predict the real-time demand torque variation. The performance of the control system is finally demonstrated at a traffic-in-the-loop powertrain simulation system (TILPSS) platform.


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    Title :

    Receding Horizon Optimal Control of Hybrid Electric Vehicles Using ELM-Based Driver Acceleration Rate Prediction


    Contributors:
    Zhang, Jiangyan (author) / Xu, Fuguo (author) / Zhang, Yahui (author) / Shen, Tielong (author)


    Publication date :

    2019-06-30


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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