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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    Title :

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


    Contributors:
    Gao, Yuan (author) / Sun, Kai (author) / Hong, Jinlong (author) / Na, Xiaoxiang (author) / Gao, Bingzhao (author) / Chen, Hong (author)


    Publication date :

    2024-09-24


    Size :

    5472417 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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