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AERODYNAMIC AND ROLLING RESISTANCES OF HEAVY DUTY VEHICLES. SIMULATION OF ENERGY CONSUMPTION

Applied Computer Science, ISSN: 2353-6977, Vol: 20, Issue: 3, Page: 116-131
2024
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  • Mentions
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    • News Mentions
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Most Recent News

Lublin University of Technology Researchers Yield New Study Findings on Computer Science (Aerodynamic And Rolling Resistances of Heavy Duty Vehicle. Simulation of Energy Consumption)

2024 OCT 28 (NewsRx) -- By a News Reporter-Staff News Editor at Computer News Today -- A new study on computer science is now available.

Article Description

The main objective of the work was to develop a comprehensive model of energy consumption simulation of heavy duty vehicles using the VECTO simulation tool. The research issue was the impact of aerodynamic drag and rolling resistance on fuel consumption and emissions under various driving conditions described in four driving cycles: Urban Delivery, Regional Delivery, Urban, and Suburban. Each cycle differed in driving time, distance and average speed to represent different operational scenarios. The methodology involved defining vehicle parameters such as weight, aerodynamic coefficients and tyre rolling resistance. The main findings show that the impact of both aerodynamic drag and rolling resistance on fuel consumption can be efficiently modelled. It has been proven that the proposed modifications to aerodynamic drag and rolling resistance can reduce fuel consumption by more than 8%. The lowest fuel consumption was achieved in the Regional Delivery cycle, while the Urban cycle had the highest fuel consumption due to frequent vehicle stops. The results show that optimization of vehicle design and its performance can significantly improve energy efficiency and reduce emissions. A computational modelling tool such as VECTO can contribute to sustainable transport solutions and improve the efficiency of heavy duty vehicle.

Bibliographic Details

Łukasz Grabowski; Arkadiusz Drozd; Mateusz Marcin Karabela; Wojciech Karpiuk

Politechnika Lubelska

Engineering; Computer Science; Economics, Econometrics and Finance

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