Technical Papers
Nov 11, 2022

Mechanistic Excess Fuel Consumption of a 3D Passenger Vehicle on Rough Pavements

Publication: Journal of Transportation Engineering, Part B: Pavements
Volume 149, Issue 1

Abstract

A passenger vehicle’s fuel-powered engine compensates for its dissipated energy, which is caused by road roughness. This paper presents an integrated vehicle–pavement approach and investigates the excess fuel consumption of a seven-degree-of-freedom (DOF) full-car model on rough pavements. In the approach, a nonstationary Laplace process is used to artificially generate road roughness profiles with consideration of local roughness variance and parallel roughness correlation. The proposed mechanistic approach quantifies the impact of road roughness and vehicle dynamic characteristics on excess fuel consumption. The study concluded that overlooking local roughness variance, an indicator of road roughness nonstationarity, may underestimate excess fuel consumption by 30%. Compared with a two-dimensional (2D) half-car model, a three-dimensional (3D) full-car model reduces computation error of excess fuel consumption by approximately 11%. For practical implementation, the extended roughness-speed-impact (ERSI) model (R2=98%) is developed to estimate roughness-induced energy dissipation of five two-axle vehicles.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request:
Raw simulation data (available upon request), and
Models (provided in this paper).

Acknowledgments

The authors are representatives of Illinois Center for Transportation (ICT). The contents of this paper reflect the view of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official view or policies of ICT. This paper does not constitute a standard, specifications, or regulations.

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Go to Journal of Transportation Engineering, Part B: Pavements
Journal of Transportation Engineering, Part B: Pavements
Volume 149Issue 1March 2023

History

Received: Feb 18, 2021
Accepted: Sep 15, 2022
Published online: Nov 11, 2022
Published in print: Mar 1, 2023
Discussion open until: Apr 11, 2023

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Authors

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Graduate Research Assistant, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, 205 N. Mathews Ave., Urbana, IL 61801 (corresponding author). ORCID: https://orcid.org/0000-0001-5022-2435. Email: [email protected]
Bliss Professor of Engineering, Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign, 205 N. Mathews Ave., Urbana, IL 61801. ORCID: https://orcid.org/0000-0002-5824-103X. Email: [email protected]

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Cited by

  • Smartphone-Based Cost-Effective Pavement Performance Model Development Using a Machine Learning Technique with Limited Data, Infrastructures, 10.3390/infrastructures9010009, 9, 1, (9), (2024).
  • A quantitative methodology for measuring the social sustainability of pavement deterioration, Scientific Reports, 10.1038/s41598-024-52655-7, 14, 1, (2024).
  • Analysis of E-Scooter Vibrations from Health Perspective: A Case Study, Machines, 10.3390/machines11070761, 11, 7, (761), (2023).
  • Differences Evaluation of Pavement Roughness Distribution Based on Light Detection and Ranging Data, Applied Sciences, 10.3390/app13148080, 13, 14, (8080), (2023).
  • International Roughness Index Prediction for Jointed Plain Concrete Pavements Using Regression and Machine Learning Techniques, Transportation Research Record: Journal of the Transportation Research Board, 10.1177/03611981231173639, 2678, 2, (235-250), (2023).

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