Effect of Pavement Conditions on Rolling
Resistance and Fuel Consumption
Karim Chatti, Ph.D.
Department of Civil & Environmental Engineering
Michigan State University
East Lansing, MI 48824
Pavement Life Cycle Assessment Workshop
University of California, Davis
Davis, California
May 5-7, 2010
What do we mean by driving
resistance and rolling resistance?
• air resistance
• rolling resistance
• inertial resistance
• gradient resistance
• side force resistance
• transmission losses
• losses from the use of auxiliaries
• engine friction
2
Factors affecting rolling resistance• Most important factors in rolling resistance:
– Vehicle weight
– Tire inflation
• Less important:
– Vehicle speed
• Least important:
– Tire tread design, composition and width
– Tire temperature
– Road structure and conditions3
Influence of IRI and MPD on RR
(Sandberg, 1997)
• Results of coast-down measurements on 34 test sections
• Increases in car RR based on ECRPD results
– at speed of 54 km/h:
• IRI from 1 to 10 m/km: increase in RR by 19 %
• MPD from 0.3 to 3 mm: increase in RR by 46 %
– at speed of 90 km/h:
• IRI from 1 to 10 m/km: increase in RR by 48 %
• MPD from 0.3 to 3 mm: increase in RR by 72 %
4
Effect of IRI and MPD on fuel
consumption (TRB special report 286)
2 m/km reduction in
roughness (IRI)
10 % reduction in average
rolling resistance
1 to 2% reduction in fuel
consumption
5
5
Gaps in knowledge
• The understanding of the relationship
between pavement surface characteristics
and vehicle fuel consumption is still in
development.
• Current models require improvement.
6
NCHRP 1-45 : Effect of pavement
conditions on fuel consumption
• Recommend models for estimating the effects of
pavement surface condition on VOC. These
models should be able to:
a) Take into account pavement, traffic and
environmental conditions encountered in the US
b) Address the full range of vehicle types
7
United States VOC Models
Development
Winfrey,
Claffey
1968-1971
Intermediate
Brazil Study
1975-1980
US Data on
1970's Vehicle
France Price
Indexing
1976
Red Book
AASHTO
1978
TRDF VOC Model
1982
MicroBENCOST
VOC
1991-1992
Canada: HUBAM
Alberta
United States:
HIAP
HPMS
HERS
State DOT
FHWA
State DOT
Counties
Municipalities
8
8
De Weille
1966
Highway Cost Model
1971MIT, TRRL & LCPC
Kenya, India &
Caribbean
1971-1986TRRL & CRRI
Brazil Study
1975-1984TRDF & TRRL
HDM-III
VOC
1987
HDM-IV
VOC
1994-2000
TRDF VOC
1980-82
COBA
VETO
NITRR
NZVOC
PMIS
CB-Roads
Background Work
LEGEND
Major VOC Model
Other VOC Model
World Bank VOC Models
Development
Zaniewski et al.
Most recent
model
Source: HDM IV manual
9
9
HDM 4 Model
PengPaccsPtrfIFC ,
Ptr= Power required to overcome traction forces (kW)
Paccs= Power required for engine accessories (e.g. fan belt,
alternator etc.) (kW)
Peng = Power required to overcome internal engine friction (kW)
10
10
HDM 4 model (cont.)
Aerodynamic forces
Rolling resistance
Gradient forces
Curvature forces
Inertial forces
1000
ircga
tr
FFFFFP
2*****5.0 AFCDCDmultFa
gGRMFg **
3
22
10**
***
,0maxCsNw
egMR
M
Fc
2*13*12*1*11**2 bMbCRNwbFCLIMCRFr
aa
aaMFi *2
arctan*10*3
Tractive power
DEFaIRIaTdspaaKcrCR *3*2*1022 Surface factor
11
11
Field tests matrix
Section
ID
Pavement TypeIRI range
(m/Km)
Length
(Km)
Speed
limit
(Km/h)
Test Speed
(Km/h)Replicates
AC PCC
AB X 1.3 - 8.5 1.44 72 56 72 2
BC X 1.7 - 7 1.6 72 56 72 2
DE X 3.5 - 6 0.48 72 56 72 2
EF X 3.3 - 6 0.64 72 56 72 2
GH X 1.1 - 2.5 4.8 112 88 104 2
JI X 1.5 - 2.6 6.4 80 56 72 2
IJ1 X 1.5 - 2.6 0.64 80 72 88 2
IJ2 X
0.8 - 4.6
1.6 80 56 72 2
IJ3 X 0.48 80 56 72 2
IJ4 X 1.28 72 56 72 2
12
12
Data acquisition system
• The data acquisition system could access and log
data from the vehicle’s Engine Control Unit
(ECU) via On Board Diagnostic (OBD) connector
13
13
Profile and Texture Measurements:
MDOT test vehicles
Road Surface Analyzer
This equipment computes a Mean Profile
Depth (MPD) based on the ASTM Standard
E1845
Rapid Travel ProfilometerThis vehicle measures the ride quality or
smoothness of pavements. Operating at
highway speeds, it uses a laser to measure the
profile of the roadway and an accelerometer to
determine the movement of the truck.
14
14
Slope surveys: High Precision GPS
• The sampling rate is every 1 second
at highway speed (every 100ft).
• The average error is 0.5 inch per
0.3 miles,
15
15
16
16
Loading conditions
Light truck Heavy truck
6,210 lb 47,000 lb
17
17
Calibration of the HDM 4 fuel
consumption model
RPMIdleRPM
RMPIdleRPMaPaccsaPaccsaPaccsPKPea
PaccsPengPengaccs
100*1_0_(1_max**
DEFaIRIaTdspaaKcrCR *3*2*1022
Rolling resistance Surface factor
Engine and accessories power
18
18
Effect of engine speed prediction errors
on the calibration
0
500
1000
1500
2000
2500
0 20 40 60 80Speed (Km/h)
Engin
e sp
eed (
rpm
)
measured engine speed
engine speed model (HDM 4)
Overestimation of the engine
speed
Overestimation of the engine
and accessories power
Underestimation of the
traction power
=
Underestimation of the effect
of pavement conditions
19
19
Calibration of the HDM 4 engine
speed model
0
500
1000
1500
2000
0 500 1000 1500 2000
Measured Engine Speed (rpm)
Pre
dic
ted
En
gin
e S
pee
d (
rpm
) .
Van
y = 0.0062x3 - 0.3018x
2 + 6.7795x + 671.98
R2 = 0.96
0
500
1000
1500
2000
2500
0 20 40 60Speed (Km/h)
En
gin
e sp
eed
(rp
m)
measured engine speed-Wet conditionengine speed model (HDM 4)measured engine speed-Dry conditionCalibrated model
20
20
Observed fuel consumption versus
estimated after calibration
y = x
R2 = 0.90
SSE = 4.09
0
20
40
60
80
100
0 20 40 60 80 100
Measured Fuel rate (mL/Km)
Cal
ibra
ted F
uel
rat
e (m
L/K
m)
. y = x
R2 = 0.89
SSE = 4.19
0
20
40
60
80
100
120
0 20 40 60 80 100 120
Measured Fuel rate (mL/Km)C
alib
rate
d F
uel
rat
e (m
L/K
m)
.
Passenger car SUV
21
21
y = x
R2 = 0.83
SSE = 9.58
0
50
100
150
200
0 50 100 150 200
Measured Fuel rate (mL/Km)
Cal
ibra
ted
Fu
el r
ate
(mL
/Km
) . y = x
R2 = 0.82
SSE = 10.16
0
50
100
150
200
250
300
0 50 100 150 200 250 300
Measured Fuel rate (mL/Km)
Cal
ibra
ted
Fu
el r
ate
(mL
/Km
) .
y = x
R2 = 0.88
SSE = 5.29
0
100
200
300
400
0 100 200 300 400
Measured Fuel rate (mL/Km)
Cal
ibra
ted F
uel
rat
e (m
L/K
m)
.
Van Light truck
Articulated truck
22
22
Heavy Truck: Analysis of
covariance at 55 mph
23
23
Heavy truck: Analysis of covariance
at 35 mph
24
24
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1 2 3 4 5
IRI (m/km)
Chan
ge
in f
uel
consu
mption (
%)
.
Medium car- HDM 4 Medium car - Regression
SUV - HDM 4 SUV - Regression
Van - HDM 4 Van - Regression
Light truck - HDM 4 Light truck - Regression
Articulated truck - HDM 4 Articulated truck - Regression
Effect of roughness:
HDM 4 versus regression data
25
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1 2 3 4 5
IRI (m/km)
Chan
ge
in f
uel
consu
mption (
%)
.
Medium carSUVVanLight truckArticulated truck
Before calibration After calibration
25
Effect of Texture on Fuel Consumption -
Regression
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 0.5 1 1.5 2 2.5 3MPD (mm)
Ch
ang
e in
fu
el c
on
sum
ptio
n (
%)
.
Medium car - 35 mph Medium car - 55 mph
SUV - 35 mph SUV - 55 mph
Van - 35 mph Van - 55 mph
Light truck - 35 mph Light truck - 55 mph
Articulated truck - 35 mph Articulated truck - 55 mph26
Effect of pavement type on fuel
consumption
• Conduct univariate analysis having IRI as
a covariate and pavement type as fixed
factor
• Repeat the analysis for 35, 45 and 55 mph
27
27
Effect of pavement type on fuel
consumptionSummer Winter
Sig. Not Sig. Sig. Not Sig.
Passenger Car √ √
VAN √ √
SUV √ √
Light Truck √* √† √
Articulated Truck √* √† √
* Trucks driven over AC at 35 mph consumes more than trucks
driven over PCC
† not significant at 45 and 55 mph
28
28
Articulated truck
29
29
Part I
Summary and conclusions
• Field tests as part of NCHRP 1-45 confirmed the
effect of roughness on fuel consumption and allowed
for calibration and validation of the HDM 4 FC
model.
• Effect of texture depth on fuel consumption could
only be seen for heavy truck at low speed (35 mph)
• Effect of pavement type could only be seen in
summer conditions, only for trucks and only at low
speed (35 mph)
30
30
Part II:
Effect of Roughness on Repair and
Maintenance Costs
31
HDM 4 Repair and Maintenance Model
• HDM4 Repair and Maintenance Cost model
is empirical.
• HDM-4 model was calibrated using data
from developing countries (e.g., Brazil,
India).
– Labor hours are much higher than in the US
– The inflation in the parts and vehicle prices
between the US and developing countries.
32
32
HDM 4 repair and maintenance costs
model
kp
pc 0 1 pcPARTS = K0 CKM (a + a RI) + K1 1 + CPCON dFUEL
lh
a
lh KPARTSaKLH 10 3
2
• Parts consumption
• Labor hours
30
30
0
0
0
7
7
5
7
0
7
5
74
IRI
IRIa
a
IRIa
IRI
aa
aIRIa
a
IRI
Smoothing equation
6
540 *,min,max aIRIaaIRIIRIRI
33
33
Updating Zaniewski’s tables
1969 1982 2007
Time (years)
R&
M c
ost
s ($
)
1969 1982 2007
Time (years)
R&
M c
ost
s ($
)
34
Tables
and
Charts
Average
Roughness
Data from DOT fleet
economic analysisPrevious
Tables/Data
Develop Time
Series TrendsUpdate
34
Data Analysis (Empirical approach)
• Repair and maintenance costs from
Texas DOT and Michigan DOT
• Extract only repair costs related to damage
from vibrations:
– Underbody inspection
– Axle repair and replacement
– Shock absorber replacement
35
35
R&M Costs
from MDOT$0
$200
$400
$600
$800
1.40 1.60 1.80 2.00 2.20 2.40
IRI (m/km)
Par
ts c
ost
($
)
$0
$100
$200
$300
$400
$500
Lab
or
cost
s ($
)
Parts Labor
(a) Passenger Car
$0
$200
$400
$600
$800
$1,000
1.2 1.7 2.2 2.7
IRI (m/km)
Par
ts c
ost
($
)
$0
$100
$200
$300
$400
$500
Lab
or
cost
s ($
)
Parts Labor
(b) Light Trucks
$0
$200
$400
$600
$800
$1,000
$1,200
1.2 1.7 2.2 2.7
IRI (m/km)
Par
ts (
$)
$0
$200
$400
$600
$800
Lab
or
cost
s ($
)
Parts Labor
(c) Medium Trucks
$0
$200
$400
$600
$800
$1,000
1.2 1.7 2.2 2.7
IRI (m/km)
Par
ts c
ost
s ($
)
$0
$100
$200
$300
$400
$500
$600
Lab
or
cost
s ($
)
Parts Labor
(d) Heavy Trucks
$0
$500
$1,000
$1,500
$2,000
1.2 1.7 2.2 2.7
IRI (m/km)
Par
ts c
ost
($
)
$0
$500
$1,000
$1,500
Lab
or
cost
s ($
)
Parts Labor
(e) Articulated Trucks
$0
$200
$400
$600
$800
1.2 1.7 2.2 2.7
IRI (m/km)
Par
ts C
ost
s ($
)
$0
$100
$200
$300
$400
$500
$600
$700
Lab
or
cost
s ($
)
Parts Labor
(f) Buses
36
36
Mechanistic Approach
• A mechanistic-empirical approach was
proposed to conduct fatigue damage analysis
using vehicle-pavement interaction modeling.
37
37
Vehicle simulation
Rainflow counting algorithm
Vehicle damage models
stress
N
Component
properties
Roughness
features
distribution
Additional cost database
Artificial generation of road surface profile
m
m
• Repair cost of suspensions
• Typical life of suspensions
38
38
Failure threshold
• User perspective : Replace parts when certain
signs of wear become evident.
• Manufacturer lifetime warranty:
– Truck suspensions : 250,000 miles
– Car suspensions : 100,000 miles
39
39
13.6%
20.2%
29.1%
14.5%
8.7%
4.3% 3.1% 2.5% 2.3% 1.7%
0%
10%
20%
30%
40%
1 1.5 2 2.5 3 3.5 4 4.5 5 6
IRI (m/km)
Pro
ba
bilit
y d
ensi
ty f
un
ctio
n (
%)
.
Generate 30 Road Profiles
for each roughness level
Vehicle simulation
damage analysis
Multiply the PDF with
250,000 or 100,000 miles
Vehicle miles traveled
over each roughness level
Accumulated damage
caused by each
roughness level
.
.
.
.
.
.
.
.
= Damage threshold
40
40
Failure threshold (Cont’d)
• For cars: 87.3 %
• For trucks: 62.2 %
• Vehicle manufacturers design their vehicles
for:
– Cars: 90th to 95th percentile of roughness
– Trucks: 80th to 95th percentile of roughness
41
Car manufacturers design their vehicle for the 90th to 95th
percentile of roughness
93rd percentile
13.6%
20.2%
29.1%
14.5%
8.7%
4.3% 3.1% 2.5% 2.3% 1.7%
0%
10%
20%
30%
40%
1 1.5 2 2.5 3 3.5 4 4.5 5 6
IRI (m/km)
Pro
ba
bilit
y d
ensi
ty f
un
ctio
n (
%)
.
42For cars
0 0.5 1 1.5 2 2.5 3 3.5 4 4.50
0.2
0.4
0.6
0.8
1
IRI (m/km)
Acc
um
ula
ted
dam
age
3.9
84.5%
42
Truck manufacturers design their vehicle for the 80th to 95th
percentile of roughness
87th percentile
13.6%
20.2%
29.1%
14.5%
8.7%
4.3% 3.1% 2.5% 2.3% 1.7%
0%
10%
20%
30%
40%
1 1.5 2 2.5 3 3.5 4 4.5 5 6
IRI (m/km)
Pro
ba
bilit
y d
ensi
ty f
un
ctio
n (
%)
.
43For trucks
0 0.5 1 1.5 2 2.5 3 3.50
0.2
0.4
0.6
0.8
1
IRI (m/km)
Accu
mu
late
d d
am
ag
e
3.2
66%
43
0 0.5 1 1.5 2 2.5 3 3.5 4 4.50
0.2
0.4
0.6
0.8
1
IRI (m/km)
Acc
um
ula
ted
dam
age
Artificial profiles
Real profiles
0 0.5 1 1.5 2 2.5 3 3.5 40
0.2
0.4
0.6
0.8
1
IRI (m/km)
Acc
um
ula
ted
dam
age
Artificial profiles
Real profiles
Cars Trucks
Accumulated damage using actual
profiles from in-service pavements
44
44
Empirical versus mechanistic
predictions: Trucks
45
0 1 2 3 4 5 6 70
50
100
150
200
250
300
IRI (m/km)
Rep
air
and
Mai
nte
nan
ce c
ost
s ($
/10
00
km
) M-E w/ artificial profiles
M-E w/ actual profiles
Empirical:
Zaniewski/HDM4
45
Empirical versus mechanistic
predictions: Cars
0 1 2 3 4 5 6 730
35
40
45
50
55
60
65
70
75
80
IRI (m/km)
Rep
air
and
Mai
nte
nan
ce c
ost
($
/10
00
km
)
M-E w/ artificial profiles
M-E w/ actual profiles
Empirical:
Zaniewski/HDM4
46
Example: VOC for Trucks caused by
I69 condition
47
0 0.5 1 1.5 2 2.5 3 3.5 4 4.51
2
3
4
5
6
7
Distance (miles)
To
tal
Co
st (
Cen
ts p
er T
ruck
) FC
R&M
Total Cost
47
Example: VOC for Cars caused by
I69 condition
48
0 0.5 1 1.5 2 2.5 3 3.5 4 4.50.5
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.4
1.5
Distance (miles)
To
tal
Co
st (
Cen
ts p
er C
ar) FC
R&M
Total Cost
48
Thank you!
49
