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

Farea, Shawqi Mohammed Othman and Mumcuoğlu, Mehmet Emin and Ünel, Mustafa and Mise, Serdar and Unsal, Simge and Yilmaz, Metin and Köprübaşı, Kerem (2023) Towards driving-independent prediction of fuel consumption in heavy-duty trucks. In: AEIT International Conference on Electrical and Electronic Technologies for Automotive, AEIT AUTOMOTIVE 2023, Modena

Full text not available from this repository. (Request a copy)


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.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: artificial neural networks; Fuel consumption; heavy-duty vehicles; regression; route planning
Divisions: Faculty of Engineering and Natural Sciences
Depositing User: Mustafa Ünel
Date Deposited: 04 Oct 2023 22:02
Last Modified: 04 Oct 2023 22:02

Actions (login required)

View Item
View Item