The Predictive Cruise Control (PCC) algorithm is currently being utilized to address the high energy consumption issue in heavy-duty trucks. The premise for achieving significant energy-saving effectiveness with the PCC algorithm lies in accurately obtaining engine fuel consumption model. In this paper, we develop a data-driven fuel consumption model, which uses Gaussian processes regression to learn the prediction error between static fuel consumption map and actual fuel consumption rate. This model possesses the following characteristics: (i) Improved accuracy in predicting fuel consumption rates; (ii) Online parameter updating; (iii) Assisting PCC algorithm in higher energy-saving rates without increasing computational complexity. Through simulation validation, the learning-based model improves fuel consumption rate prediction accuracy by 26.9%. Moreover, the PCC algorithm, which uses this advanced model, has been compared with Adaptive Cruise Control (ACC) and the original PCC, achieving energy-saving improvements of up to 9.6% and 3.1%, respectively.


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    Titel :

    Learning-Based Predictive Cruise Control for Fuel Consumption Model in Heavy-Duty Trucks


    Beteiligte:
    Gao, Yuan (Autor:in) / Sun, Kai (Autor:in) / Hong, Jinlong (Autor:in) / Na, Xiaoxiang (Autor:in) / Gao, Bingzhao (Autor:in) / Chen, Hong (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    5472417 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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