The demand for fuel-efficient transport solutions are steadily increasing with the goal of reducing environmental impact and increasing efficiency. Heavy-Duty Vehicle (HDV) platooning is a promising concept where multiple HDVs drive together in a convoy with small intervehicular spacing. By doing this, the aerodynamic drag is reduced which in turn lowers fuel consumption. We propose a novel Model Predictive Control (MPC) framework for longitudinal control of the follower vehicle in a platoon consisting of two HDVs when no vehicle-to-vehicle communication is available. In the framework, the preceding vehicle's velocity profile is predicted using artificial neural networks which uses a topographic map of the road as input and is trained offline using synthetic data. The gear shifting and mass of consumed fuel for the controlled follower vehicle is modeled and used within the MPC controller. The efficiency of the proposed framework is verified in simulation examples and is benchmarked with a currently available control solution.


    Access

    Download


    Export, share and cite



    Title :

    Fuel-efficient Model Predictive Control for Heavy Duty Vehicle Platooning using Neural Networks



    Publication date :

    2018-01-01



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Fuel-Efficient Heavy-Duty Vehicle Platooning

    Alam, Assad | BASE | 2014

    Free access


    Fuel-efficient heavy-duty vehicle platooning by look-ahead control

    Turri, Valerio / Besselink, Bart / Mårtensson, Jonas et al. | BASE | 2014

    Free access

    Fuel-Efficient Control of Merging Maneuvers for Heavy-Duty Vehicle Platooning

    Koller, Julian P. J. / Colin, Alex Grossmann / Besselink, Bart et al. | IEEE | 2015


    Disturbance observer approach for fuel-efficient heavy-duty vehicle platooning

    Na, Gyujin / Park, Gyunghoon / Turri, Valerio et al. | Taylor & Francis Verlag | 2020