Headline Section

Fuel economy and exhaust emissions of a diesel vehicle under real traffic conditions


Traffic and vehicle simulations are often developed individually. However, vehicle performance is heavily affected by traffic conditions. Cosimulations of traffic and vehicle under real-road situations can reflect the semi-real-world performance of vehicles, with traffic conditions being taken into considerations. This paper proposed an approach to combine the traffic and vehicle simulations that are realized by simulation of urban mobility (SUMO) and GT-Suite software, respectively. Read more.

Published on Energy Science & Engineering

Authors: Jianbing Gao, Haibo Chen, Kaushali Dave, Junyan Chen, Dongyao Jia.



Analysis of driving behaviours of truck drivers using motorway tests


Road transportations still play a dominant role in goods delivery, and driving behaviours significantly affect the fuel economy of heavy-duty trucks. Plenty of fossil fuel is wasted as a result of unreasonable driving behaviours even in the case of highly experienced drivers. The objective of this paper is to analyse drivers’ behaviours over two segments of motorways and estimate the potential benefits of fuel saving caused by a change in driving habits during national and international goods delivery. Drivers’ habits on motorways change depending on the road situations. In the acceleration process, the fuel consumption rates are huge even under low-speed conditions. The truck fuel consumption rates are
exaggerated by positive road slopes, but still dominated by acceleration. Accelerations are generally in normal distributions, with the median value being approximately 0.5 m/s2. The speed ranges corresponding to each gear enlarge with the increase in gear number. The potentials of annual fuel saving for parts of European Union countries are nearly 2 3 106 m3 by adopting proper driving behaviours.vely. Read more.

Published on Jounrnal of AUTOMOBILE ENGINEERING

Authors: Jianbing Gao, Haibo Chen, Kaushali Dave, Junyan Chen, Ying Li, Tiezhu Li and Biao Liang.



This project has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No 815189.