Rolling Resistance

Rolling resistance and forces opposing motion.

The application of Life Cycle Assessment (LCA) to road pavements has been continuously evolving and improving over the last years, however there are several limitations and uncertainties in the introduction of some components in the framework, such as road pavement rolling resistance – in terms of pavement surface properties – and traffic delay during maintenance activities.   This paper analyses the influence of methodological assumptions and the model used to estimate the increased emissions for traffic delay and road pavement rolling resistance on the results of an LCA. The Greenhouse Gases (GHG) emissions related to these two phases of a pavement LCA will be calculated for a UK case study, using different models, and a sensitivity test is performed on some specific input variables. The results show that the models used and the input variables significantly affect the LCA results, both for the rolling resistance and the traffic delay.

Although the impact of road pavement surface condition on rolling resistance has been included in the life cycle assessment (LCA) framework of several studies in the last years, there is still a high level of uncertainty concerning the methodological assumptions and the parameters that can affect the results. In order to adopt pavement carbon footprint/ LCA as a decision-making tool, it is necessary to explore the impact of the chosen methods and assumptions on the LCA results.

This paper provides a review of the main models describing the impact of the pavement surface properties on vehicle fuel consumption and analyses the influence of the methodological assumptions related to the rolling resistance ontheLCAresults.ItcomparestheCO2 emissions,calculated with two different rolling resistance models existing in literature, and performs a sensitivity test on some specific input variables (pavement deterioration rate, traffic growth, and emission factors/fuel efficiency improvement). Results and discussion The model used to calculate the impact of the pavement surface condition on fuel consumption significantly affects the LCAresults.The pavementdeterioration rate influences the calculation in both models, while traffic growth and fuel efficiency improvement have a limited impact on the vehicle CO2 emissions resulting from the pavement condition contribution to rolling resistance.

The rolling resistance, contact forces and fuel consumption of a heavy duty truck were computed as a function of pavement type. Measurements were conducted at the Mainline MnROAD test track near Albertville, Minnesota and at two highway sections with distressed pavements. Test procedure consisted of driving the instrumented MnROAD heavy-duty truck on the selected pavement sections while recording signals from the chassis-mounted accelerometers, differential GPS, and the Controller Area Network. The truck was driven at cruise speeds of 55 and 64 MPH on roads with live traffic and at cruise speeds from 30 to 65 MPH on the Mainline. In addition, weather data from two MnROAD stations, wind velocity from two ultrasonic anemometers, road elevation, and IRI were collected during the tests. Data were analyzed with a novel and comprehensive mechanistic model of vehicle dynamics. Dynamical rolling resistance and its contribution to fuel consumption was estimated from the spectra analysis of accelerometers signals. The coefficient of rolling resistance of the truck tires varied from 0.0044 to 0.0072 on the Mainline cells. Fuel consumed by the rolling resistance force at 30 MPH varied between 0.006 liter and 0.009 liter per cell, for an average consumption of 5 liter/100 km. Rolling resistance was 0.0072 on bituminous TH 66 and 0.0061 on concrete TH 10 sections. Spectral analysis of accelerometer data revealed vibrational modes unique to either bituminous or concrete pavements. The power loss caused by the vibrations of suspensions and tires was also computed.

There is an increased focus worldwide on understanding and modeling rolling resistance because reducing the rolling resistance by just a few percent will lead to substantial energy savings. This paper reviews the state of the art of rolling resistance research, focusing on measuring techniques, surface and texture modeling, contact models, tire models, and macro-modeling of rolling resistance.

Read More: http://ascelibrary.org/doi/full/10.1061/(ASCE)TE.1943-5436.0000673

In order to evaluate traffic energy changes due to the improvement of road surface standard one need to describe:

• rolling resistance at different road surface conditions

• all other driving resistance

• fuel consumption (Fc) as a function of driving resistance.

Based mainly on empirical data from coastdown measurements in Sweden a general rolling resistance model – with roughness (iri), macrotexture (mpd), temperature and speed as explanatory variables – was developed and calibrated for a car; a heavy truck and a heavy truck with trailer.

This rolling resistance model has been incorporated into a driving resistance based Fc model with a high degree of explanation. The Fc function also includes variables for horizontal curvature (ADC) and the road gradient (RF).

If mpd per road link is reduced by up to 0.5 mm, the total Fc in the road network will be reduced by 1.1%. By reducing iri per link by 0.5 m/km, speed will increase in parallel to reduced rolling resistance and there will be approximately no resulting effect on Fc. If rut depth is decreased in parallel to iri there will be a further increase in speed. For individual road links there might be an energy saving potential if the proportion of heavy vehicles is big enough.

The main objective of the ECPRD-project is to develop models and methods to minimize the sum of energy use for road construction, for road maintenance and for the traffic. In order to estimate energy use for road traffic the influence of road surface conditions on driving resistance and energy use is of main importance. This part of driving resistance effects have been categorized as rolling resistance.


The literature presents effects of road surface condition on rolling resistance in a wide range of values. The background to this wide range could be:

• different methods: fuel consumption; coast down; laboratory methods etc.
• a measuring problem in general isolating small additional forces
• use of different measures for characterizing a specific road condition
• a lack of control of other variables than for the road surface
• high correlations in the group of road surface variables
• high correlations between road surface and other variables depending on study design

When adding a new study of road surface rolling resistance effects to the long list of other studies it should be of big importance to prove that the accuracy is high. It is difficult to judge the level of accuracy in different studies. A possible criterion in such comparisons could be: which variables are under control. Another criterion could be if these variables are included or not into the analysis. If they have not been included, effects will still be there but may appear disguised in other variables like road roughness and macrotexture.
In this study the coastdown method is used to estimate driving resistance.

The reason for selecting this method is:
• the acceleration level gives a true measure of the driving resistance under real conditions
• the costs for equipment is comparatively low
• to avoid uncertainties caused by the engine and used fuel if compared to fuel consumption measurements
• there is a good potential for recording of all explanatory variables of importance.
Used explanatory variables in analyses:
• speed and acceleration
• gradient
• curvature
• crossfall
• roughness
• macrotexture
• ruts
• ambient temperature
• wind speed
• air pressure.

In total, 34 road strips have been used for the measurements. These strips have been selected in order to cover the main variation in roughness and macrotexture for Swedish roads with the extra requirement that there should be a low correlation.

Road surface conditions have been recorded with a Road Surface Tester (RST). The RST system reports roughness and macrotexture by several different measures.

In total three test vehicles have been used: a car; a van (RST) and a truck (RDT). The operating weights have approximately been 1700, 3300 and 14500 kg.

The literature points out that even small effects on rolling resistance should be possible to detect. This raises a high demand in registration of conditions with high accuracy or controlled conditions. One very important condition used should be: the same tyre pressure before measurements on each test strip.

Estimated effects per unit change of IRI and MPD for the car are depending on speed level:
• at 15 m/sec:
- IRI: increase in rolling resistance by 2.3%
- MPD: increase in rolling resistance by 5.5 %
• at 25 m/sec:
- IRI: increase in rolling resistance by 6.2 %
- MPD: increase in rolling resistance by 9.3 %

In the function used for regression an ambient temperature correction term is included. The presented effects then represent 25 °C.

The IRI and MPD results for the other two test vehicles are not proved speed dependent. For the RST the road surface effects are not proved different from zero. The RDT results in some cases having a wrong sign are judged being not reliable.
Compared to the literature, IRI effects are in the middle of the survey interval and MPD effects are in the upper part of the survey interval.

The analyses include tests with different road surface measures for roughness and macrotexture. Even if differences are small, IRI and MPD gives the best fit of measured coastdown data to the model function compared to other alternative measures.
The dynamic behaviour of a road vehicle on an uneven road is possible to simulate. The additional driving resistance from road roughness is then estimated based on damping losses in tyres and shock absorbers.

The coastdown measurements were used to validate such a simulation routine:
• the simulated additional resistances were far below those estimated by measurements
• the correlation between simulated and measured values was very good.

Simulations should at least be possible to use after calibration.
In ECRPD there is need for a general model representative for all type of vehicles and all models of tyre per vehicle type. Such a general model has been expressed based on the coastdown results and on literature.

The results of this ECRPD study should represent an important contribution to road surface rolling resistance effects both for methodology and for presented effects. Still there are several shortcomings:
• the quality in describing road conditions
• the importance of different aggregation levels
• the lack of data for other vehicle types than cars
• the lack of data for different tyre models
• the lack of data for different load conditions
• the lack of data for different load levels
• the discrepancy between simulations and measurements etc.

It should be of big importance for the future to reduce the mentioned shortcomings.

Report describing the basis for the Energy Balance method in HDM-4