Rolling Resistance

The road-transport sector has a significant impact on energy consumption. A relevant component of this energy usage is associated with rolling resistance (RR) between tires and pavement. In Denmark, CO2 emissions from road transport alone have been quantified as 4.6 Mt/yr. Replacing standard stone asphalt with durable low-RR pavements is expected to reduce CO2 emissions up to 1%. The Danish Road Directorate started, back in 2012, optimising a surface layer for reducing RR. The low-RR mixture was designed to provide a durable texture capable to meet all safety requirements. In 2016, two low-RR mixtures and reference asphalt concrete were paved on a test section. To evaluate the texture durability these mixtures were sampled at the construction site and tested with a circular road tester. The results show that the durability of low-RR pavements can be enhanced by using premodified binder, which reduces changes in texture properties and increases rutting resistance.

The effects of pavement characteristics on rolling resistance of heavy vehicles have gained more interest in recent years. Rolling resistance is the result of the combination of independent (but sometimes correlated) physical phenomena that dissipate energy, which can be regrouped under three different main themes. Road roughness (wavelengths between 0.5 and 50 m) causes movements in vehicle suspensions, which dissipate energy. Pavement macrotexture (wavelengths between 0.5 and 50 mm) creates additional viscoelastic deformations on tire treads. The viscoelastic behavior of the flexible pavement structure, which is referred to as structure-induced rolling resistance, is responsible for a perpetual upward slope perceived by heavy vehicle tires. Secondary aspects can also affect rolling resistance, such as road wetness and snow. This paper addresses each of these three main phenomena from three angles of analysis: (1) theoretical modeling, (2) laboratory experiments, and (3) in situ measurements. The literature on road roughness and structure-induced rolling resistance modeling is extensive compared to macrotexture-effect modeling, as the underlying physical mechanisms are still not well understood. There is, however, strong experimental evidence that the pavement macrotexture can significantly affect rolling resistance, but these studies are mostly related to cars. There are many in situ approaches, but the results are usually based on an indirect method and the different studies are difficult to compare and sometimes inconsistent. It appears that the bottleneck of scientific research on this topic is the fundamental inability to measure the rolling resistance of heavy vehicles with a direct in situ approach under real driving conditions.

Very interesting paper summarizing the research into rolling resistance with a particular focus on the influence of asphalt pavement properties.

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.

The current study focuses on options to improve vehicle energy efficiency by reducing rolling resistance on Dutch national highways. Different studies of pavement materials have been evaluated, and models have been compared to experimental data to review rolling resistance indicators. The study shows that texture parameters MPD (mean profile depth) and RMS (root mean square) are relevant indicators for rolling resistance, whereas the effect of road roughness (IRI) is found to be larger than indicated in the evaluated models. The effects of other wavelength regions, texture orientation, and road wear need further investigation. Switching to DPAC 2/6 for highways would result in energy savings of 2–2.5%. These calculations are based on estimations and results with high uncertainty and should therefore be taken as a rough estimate of the potential energy savings. Further research is recommended to further refine and validate the results of this study.

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.

MIRAVEC Report D2.1.  This is a report of the findings in Work Package 2 (WP2) in MIRAVEC. The objective of this WP is to describe existing modelling tools and evaluate their capabilities with respect to analysing the effects identified in WP1 “Road infrastructure influence effects on vehicle energy consumption and associated parameters”. The variables identified in WP1 and considered to be the most important to take into consideration when estimating the impact of road infrastructure on road traffic energy use are texture (MPD), IRI (unevenness), rut depth (RUT), gradient (RF), crossfall, horizontal curvature (ADC), road width, traffic volume (AADT) and speed (v). In this report, a selection of projects that have evaluated road characteristics and the effect on energy use are described and analysed. The results of these project shows that there can be benefits energy wise in taking the energy aspect into consideration when planning a new road or choosing rehabilitation measure of the pavement. 

MIRAVEC Report D1.1: This document describes the different road infrastructure parameters which can contribute to the overall road vehicle energy consumption and highlights those which can be influenced by infrastructure design. It is a report on the effects and parameters that need be considered in order to determine the influence of road infrastructure on road vehicle energy consumption by modelling. The effects and properties were divided into the following five groups: A. Effects of pavement surface characteristics (rolling resistance, texture, longitudinal and transversal unevenness, cracking, rutting, other surface imperfections) B. Effects of road design and layout (e.g. road curvature, gradient and crossfall, lane provision) C. Traffic properties and interaction with the traffic flow (e.g. free flowing traffic vs. stop-and-go, speed limits, access restrictions) D. Vehicle and tyre characteristics including the potential effect of technological changes in this area E. Meteorological effects (e.g. temperature, wind, water, snow, ice)

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.

This report is about rolling resistance, fuel consumption and emissions.

Memo describing changes to parameter values

Memo describing changes to parameter values

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