Browse Publications Technical Papers 2015-26-0036
2015-01-14

Accurate Measurements in Proving Ground for Fuel Consumption Reduction Study in Heavy-Duty Vehicles 2015-26-0036

The running costs of heavy-duty trucks are strongly influenced by fuel consumption. Even a small improvement in fuel economy has a big effect on fleet cost savings and pollutant contamination. From the different possible sub-systems to be improved, reducing the rolling resistance of the tires is a cost-effective option due to its relatively high influence on the fuel consumption without negatively affecting the overall performance. Nevertheless, the other causes of resistance forces, such as mechanical friction and aerodynamics must be optimized as well.
The focus of the work is to propose an accurate methodology specifically for heavy trucks for the proper evaluation of resistance forces to allow accurate fuel consumption simulation. For this purpose, the results obtained in proving ground were post-processed by applying different resistance forces characterization methodologies and the results were analyzed theoretically and numerically. In a second step, the relative impact of the accuracy of the resistance forces on constant speed and FIGE cycle fuel consumption simulation was assessed with vemSim simulation software. The software was fed with the coast-down coefficients obtained with the different methodologies and the specific engine fuel consumption maps. The results obtained from the simulation were correlated with those measured in proving ground. The correlated model of the vehicle allowed selecting the most proper methodology for measuring resistance forces and performing a virtual sensitivity analysis to evaluate the potential fuel consumption savings that can be obtained using low rolling resistance tires.
This study gives guidelines for the fuel consumption reduction process for heavy-duty vehicles. Through this work, a better understanding of the influence of the accuracy of the measurement of vehicle losses on fuel consumption prediction can be achieved.

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