Heavy-duty vehicles are among the major contributors to greenhouse gas emissions in addition to their high energy consumption. Thus, modeling their fuel consumption (FC) is of prime importance to the limitation of these environment-harmful emissions and energy saving. In this paper, we propose a data-driven model based on artificial neural networks (ANN) to predict the average FC in heavy-duty cloud-connected Ford trucks. In particular, we propose a driving-independent model based only on the vehicle weight and road grade. Owing to idling situations, the average FC includes some outliers; we propose to remove these outliers based on the weight-normalized average FC to take the changing vehicle weights into consideration. Initially, the model uses the percent torque, vehicle speed, vehicle weight, and road slope as predictors. In that case, our proposed model achieved an R2 of 0.96 outperforming the results in the literature by a significant margin. Next, we investigate the cases of excluding the torque and vehicle speed in order to assess the model’s effectiveness when using only those predictors which are independent of the vehicle dynamics. In these challenging cases, our proposed model still maintains an R2 above 0.8.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Towards driving-independent prediction of fuel consumption in heavy-duty trucks


    Contributors:


    Publication date :

    2023-07-17


    Size :

    807249 byte



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Fuel Rate Prediction for Heavy-Duty Trucks

    Liu, Liangkai / Li, Wei / Wang, Dawei et al. | IEEE | 2023


    Transient fuel consumption prediction for heavy-duty trucks using on-road measurements

    Peng, Chong / Wang, Yiyi / Xu, Ting et al. | Taylor & Francis Verlag | 2023




    Heavy Duty Truck Fuel Consumption Prediction Based on Driving Cycle Properties

    Delgado, Oscar F. / Clark, Nigel N. / Thompson, Gregory J. | Taylor & Francis Verlag | 2012