ArticlePDF Available

How hybrid-electric vehicles are different from conventional vehicles: The effect of weight and power on fuel consumption


Abstract and Figures

An increasingly diverse set of hybrid-electric vehicles (HEVs) is now available in North America. The recent generation of HEVs have higher fuel consumption, are heavier, and are significantly more powerful than the first generation of HEVs. We compare HEVs for sale in the United States in 2007 to equivalent conventional vehicles and determine how vehicle weight and system power affects fuel consumption within each vehicle set. We find that heavier and more powerful hybrid-electric vehicles are eroding the fuel consumption benefit of this technology. Nonetheless, the weight penalty for fuel consumption in HEVs is significantly lower than in equivalent conventional internal combustion engine vehicles (ICEVs). A 100 kg change in vehicle weight increases fuel consumption by 0.7 l/100 km in ICEVs compared with 0.4 l/100 km in HEVs. When the HEVs are compared with their ICEV counterparts in an equivalence model that differentiates between cars and sports-utility vehicles, the average fuel consumption benefit was 2.7 l/100 km. This analysis further reveals that a HEV which is 100 kg heavier than an identical ICEV would have a fuel consumption penalty of 0.15 l/100 km. Likewise, an increase in the HEV's power by 10 kW results in a fuel consumption penalty of 0.27 l/100 km.
Content may be subject to copyright.
Environ. Res. Lett. 2(2007) 014003 (8pp) doi:10.1088/1748-9326/2/1/014003
How hybrid-electric vehicles are different
from conventional vehicles: the effect of
weight and power on fuel consumption
1and M Kandlikar2,3,4
1Institute for Resources, Environment and Sustainability, University of British Columbia,
2202 Main Mall, Vancouver, BC, V6T 1Z4, Canada
2Institute of Asian Research, University of British Columbia, 1855 West Mall, Vancouver,
BC, V6T 1Z2, Canada
3Liu Institute for Global Issues, University of British Columbia, 6476 NW Marine Drive,
Vancouver, BC, V6T 1Z2, Canada
E-mail: and
Received 26 January 2007
Accepted for publication 7 March 2007
Published 28 March 2007
Online at
An increasingly diverse set of hybrid-electric vehicles (HEVs) is now available in North
America. The recent generation of HEVs have higher fuel consumption, are heavier, and are
significantly more powerful than the first generation of HEVs. We compare HEVs for sale in
the United States in 2007 to equivalent conventional vehicles and determine how vehicle weight
and system power affects fuel consumption within each vehicle set. We find that heavier and
more powerful hybrid-electric vehicles are eroding the fuel consumption benefit of this
technology. Nonetheless, the weight penalty for fuel consumption in HEVs is significantly
lower than in equivalent conventional internal combustion engine vehicles (ICEVs). A 100 kg
change in vehicle weight increases fuel consumption by 0.7l/100 km in ICEVs compared with
0.4l/100 km in HEVs. When the HEVs are compared with their ICEV counterparts in an
equivalence model that differentiates between cars and sports-utility vehicles, the average fuel
consumption benefit was 2.7l/100 km. This analysis further reveals that a HEV which is
100 kg heavier than an identical ICEV would have a fuel consumption penalty of
0.15 l/100 km. Likewise, an increase in the HEV’s power by 10 kW results in a fuel
consumption penalty of 0.27 l/100 km.
Keywords: hybrid-electric vehicles, fuel consumption, performance, vehicle weight, system
1. Introduction
Hybrid-electric vehicles (HEVs) use batteries, electric motors,
regenerative braking and reduction of engine idling time to
enhance a conventional internal combustion engine. This
approach is a proven means of reducing the fuel consumption
and/or improving the performance of light-duty passenger
vehicles [1,2]. The fuel consumption of HEVs is lower for
4Author to whom any correspondence should be addressed.
a number of reasons: the electric motor provides a portion of
the power for propulsion, especially at high-load conditions
when the vehicle is accelerating; some of the vehicle’s inertial
energy can be recaptured through regenerative braking systems
and stored in vehicle batteries; an engine-stop feature reduces
idling fuel consumption whenever the car is coasting, braking,
or stopped5. It has been argued that policies are needed
5This feature is also employed by so-called ‘mild hybrids’, which are
essentially conventional vehicles with oversized starter motors. Mild hybrids
such as the Saturn VUE and the GM Silverado are excluded from this study.
1748-9326/07/014003+08$30.00 1 ©2007 IOP Publishing Ltd Printed in the UK
Environ. Res. Lett. 2(2007) 014003 C Reynolds and M Kandlikar
to reduce transportation fuel consumption, as part of a suite
of approaches to reduce carbon emissions worldwide [3–6].
Hybrid-electric technology is expected to play a significant
role in achieving this. Yet these studies estimate potential fuel
savings by simply scaling the fleet average fuel consumption
by a given percentage. In order to more accurately calculate
the magnitude of fuel savings, a more robust quantitative
analysis of the relationship between weight, performance and
fuel consumption of HEVs vis-`a-vis conventional internal
combustion engine vehicles (ICEVs) is needed.
Previous studies have successfully modeled the fuel
consumption, weight, and performance tradeoffs of HEVs
based on an assessment of one or two HEV models [7,8].
This study aims to add to that body of work by analyzing
specifications and fuel consumption data for 2007 model
year hybrid-electric vehicles and their ICEV counterparts in
the North American market. We begin by examining the
expanding hybrid fleet in North America, and present data on
the changing nature of the US HEV fleet as hybrid-electric
technology is adopted across a more diverse array of light-
duty passenger vehicle platforms. Using linear regression
models, we analyze how vehicle weight and power affect the
fuel consumption of HEVs and ICEVs independently, and then
we compare the set of currently available HEVs (2007 model
year) against a functionally equivalent set of conventional
ICEVs. The ‘equivalent set’ are ICEVs of the same make
and model, and with similar power. These analyses provide a
more accurate quantitative picture of the relationship between
fuel consumption, vehicle weight and system power, using
empirical data from commercially available vehicles.
2. Diversifying hybrid-electric vehicle fleet
Automotive manufacturers are offering an increasing variety
of HEV model types. In 2007, there were nine light-duty HEV
models available in the US, and this number is projected to
double in the next two to three years [9]. Figure 1shows
the annual and cumulative sales of HEVs in the US between
1999 and 2006, as well as the number of car and sports-
utility vehicle (SUV) models available in each year. SUVs
made up approximately 30% total hybrid sales in 2006. In
addition to the growing diversity of model offerings, HEV
annual sales continue to grow both in numbers and as a
proportion of the total light-duty vehicle sales. 2006 HEV
sales in the US represent approximately 1.6% of the year’s
new vehicle sales [10,11], with predictions of continued
increase [12]. Vehicle manufacturers publish performance
specifications for individual HEV models, including gasoline
engine size (displacement), vehicle curb weight, net hybrid-
electric system power6, and acceleration. These HEV
data are presented for all of the HEVs investigated in this
study (table 1). An investigation of air pollutant emissions
performance of HEVs is beyond the scope of this paper.
However, it should be noted that since HEVs were introduced
6Torque is another characteristic that can be used to compare performance,
since it influences vehicle acceleration. However, we have chosen system
power as it is more commonly presented as a measure of powertrain
performance, and it is directly related to torque through engine speed.
1999 2000 2001 2002 2003 2004 2005 2006
(1/0) ( 2/0) ( 2/0) (2 /0) (2 /0)
HEV Sales, 1000s
Toyota Prius 2nd
generation introduced;
Also, first hybrid-electric
SUV available for sale
(Ford Escape/Mercury
Nine HEV models
SUV sales reach
29% of total 2006
First HEVs sold
(17 Honda
Cars: annual sales
SUVs: annual sales
Cumulative HEV sales
Figure 1. Car and sports-utility vehicle (SUV) shares of HEV annual
sales in the United States; annual SUV sales have been increasing
rapidly since their introduction in 2004. Cumulative HEV sales are
also shown, which are assumed to closely represent the number of
HEVs currently in use. Numbers in parentheses show numbers of
car/SUV models available for sale in a given year [11].
in North America, they have been lower-emitting than their
ICEV counterparts.
The combined fuel consumption rate is from US
Environmental Protection Agency (EPA) 2-cycle test results,
and refers to 45% highway and 55% city driving. The
EPA has recently proposed a new 5-cycle methodology that
will more accurately reflect actual driving behaviour and
corresponding fuel consumption [13]. The EPA predicts that
this methodology will increase combined fuel consumption
by 8% for conventional light-duty passenger vehicles, and by
16% for hybrid-electric vehicles. It is likely that the new
methodology will be implemented for the 2008 model year
in the US, so this study uses fuel consumption figures that
have been corrected by these factors (table 1). This approach
has been used before [14], and is supported by the reported
difference between real-world fuel consumption for the Toyota
Prius and its EPA 2-cycle fuel consumption rating [15].
From 2000 to 2006, the sales-weighted average hybrid-
electric vehicle in the US fleet has changed significantly, driven
largely by the introduction of new sports-utility and high-
performance HEV models. The average curb weight has
increased by 30%. Propelling this larger weight is a hybrid-
electric system that delivers 60% more power. The gasoline
engine component of this system is 43% larger in terms of
engine displacement. Some of the observed net power increase
is explained by the need to provide a larger vehicle with
acceptable performance. Over the same period, however, the
manufacturer-reported acceleration times also increased: the
average HEV in 2004 reaches 96.6kmh
1(60 miles per hour)
from a standing start in 20% less time than the average in 2003.
Because vehicle weight and power both strongly influence fuel
consumption, it is not surprising that average fuel consumption
has gone up by 15% with the shift towards higher-performance
HEVs (figure 2).
Environ. Res. Lett. 2(2007) 014003 C Reynolds and M Kandlikar
Tab l e 1. Specifications of the HEV models considered in the analysis (ref: manufacturer specifications). The first-generation Toyota Prius
and the Honda Insight were discontinued in 2003 and 2006, respectively, and the reported fuel consumption for the Honda Accord was
improved for the 2007 model year.
Combined fuel
vehicles Model
year Drive
km h1)
l/100 km
l/100 km
Toyota Prius (1st Gen.) 2003 FWD ECVT 1.5 1237 75 12.7 4.9 (48) 5.7 (41)
Toyota Prius (2nd Gen.) 2006/7 FWD ECVT 1.5 1310 82 10.2 4.3 (55) 5.0 (47)
Toyota Camry 2006/7 FWD ECVT 2.4 1669 140 8.9 6.0 (39) 7.0 (34)
Honda Insight 2006 FWD CVT 1.0 896 53 10.7 4.2 (56) 4.9 (48)
Honda Civic 2006/7 FWD CVT 1.3 1304 82 11.0 4.7 (50) 5.5 (43)
Honda Accord 2006 FWD Auto 3.0 1627 189 6.5 8.4 (28) 9.7 (24)
Honda Accord 2007 FWD Auto 3.0 1635 189 6.5 7.6 (31) 8.8 (27)
Lexus GS 450h 2006/7 RWD ECVT 3.5 1875 254 5.2 9.0 (26) 10.5 (22)
Nissan Altima 2007 FWD ECVT 2.5 1588 148 6.0 (39) 7.0 (34)
Sports-utility vehicles
Toyota Highlander 2006/7 AWD Auto 3.3 1925 200 7.2 8.1 (29) 9.4 (25)
Ford Escape/Mercury Mariner 2006/7 AWD ECVT 2.3 1718 116 9.0 7.6 (31) 8.8 (27)
Lexus RX 400h 2006/7 AWD ECVT 3.3 1980 200 7.3 8.1 (29) 9.4 (25)
aFWD =front-wheel drive; RWD =rear-wheel drive; AWD =all-wheel drive.
bECVT =electronically controlled continuously variable transmission; CVT =continuously variable transmission;
Auto =automatic transmission.
cCombined fuel consumption represents 55% city, 45% highway driving.
2000 2002 2004 2006
Relative Change, %
Curb Weight
Net hybrid system peak power
Combined fuel consumption
Figure 2. Vehicle performance trends over time for the average
HEV: curb weight, net hybrid system peak power, and fuel
consumption. Variability across models is indicated by the error bars
representing one (sales-weighted) standard deviation.
The changing nature of HEV technology can be gauged
by two further parameters, namely, the ratio of net system
power to vehicle curb weight and the ratio of power/engine dis-
placement. These parameters have been chosen because they
are primarily a function of the propulsion system, and they
offer insight into how hybrid-electric system technology has
evolved over time (figure 3). In comparison, fuel consump-
tion (another metric of performance) is influenced by vehicle
aerodynamics and curb weight, as well as the choice of test-
ing drive-cycle. Between 2000 and 2003, both parameters
stayed approximately constant, since only two vehicles, the
2004 20062000 2002
(Units indicated in legend)
Net Power / Vehicle Weight (W/kg)
Net Power / Engine Displacement (kW/litre)
Figure 3. Trends in net system power per vehicle weight (a measure
of vehicle performance) and net system power per engine volumetric
displacement (a measure of the technological development of the
propulsion system). Data dispersion is indicated by the weighted
standard deviation.
Toyota Prius and the Honda Insight, were offered for sale. In
2004, three new HEVs were released (the second-generation
Toyota Prius, the Honda Civic and the Ford Escape) and the
first-generation Prius was discontinued. The increased power
per unit vehicle weight between 2003 and the present indi-
cates a trend towards new vehicles having higher performance.
This is similar to what has been observed across the
whole light-duty vehicle fleet. For hybrid-electric propulsion
systems, power per engine displacement reveals technological
development; in other words, more system power is made
Environ. Res. Lett. 2(2007) 014003 C Reynolds and M Kandlikar
Tab l e 2. Vehicle weight, net system power, and corrected fuel consumption differences between 2007 model year HEVs and their ICEV
Hybrid-electric vehicles ‘Equivalent’ ICEV Vehicles
Delta weight
Delta power
Delta fuel
(l/100 km)
Toyota Prius
2nd Gen. (I4) Toyota Matrix (I4) 61 12.03.25
Toyota Camry (I4) Toyota Camry (I4) 169 21.5 2.40
Honda Civic (I4) Honda Civic (I4) 84 22.42.21
Honda Accord (V6) Honda Accord (V6) 110 6.7 1.27
Lexus GS 450h (V6) Lexus GS 430 (V8) 175 37.3 1.60
Nissan Altima (I4) Nissan Altima (I4) 150 17.2 1.75
Sports-utility vehiclesa
Toyota Highlander (V6) Toyota Highlander (V6) 166 39.5 2.69
Ford Escape/Mercury Mariner (I4) Ford Escape/Mercury Mariner (I4) 165 1.5 2.75
Lexus RX 400h (V6) Lexus RX 350 (V6) 125 1.52.69
aInternal combustion engine type is indicated as follows: I4 =inline 4-cylinder engine; V6 and V8 refer to 6- and 8-cylinder
engines in ‘V’ configuration, respectively.
available for a given internal combustion engine size. This
parameter drops between 2000 and 2004 because the first-
generation Toyota Prius, which has a lower power per engine
displacement, captured an increasing proportion of sales.
Between 2003 and 2004 the power to engine displacement
ratio increased by about 10%, indicating that the newer HEV
technology was significantly improved.
3. Weight, power and fuel consumption
A number of factors can independently affect the fuel
consumption of conventional vehicles, including differences
in powertrain type and peak power, vehicle weight,
aerodynamics, rolling resistance, and accessory power
demand. In general, heavier and more powerful vehicles
have higher fuel consumption rates. Due primarily to
the significant difference in powertrain type, engineering
models have shown that conventional ICEVs have a different
relationship between vehicle inertial weight, net system power
and fuel consumption than HEVs [7,16]. Until recently there
have been insufficient HEV models to perform a statistical
comparison with conventional ICEVs that differ only in terms
of powertrain, so other analysts have used hypothetical hybrids
based on the best understanding of the technology [16]. To
evaluate the impact of current commercially available hybrid-
electric technology on light-duty vehicle fuel consumption,
we compare a set of nine model year 2007 HEVs to a set of
nine equivalent 2007 vehicles powered by internal combustion
engines. For the purposes of this study, ‘equivalence’ for any
given ICEV is defined as being the same make and model as
the HEV. Where different engine options are available for the
ICEV, the engine with the closest power is used7(table 2).
7The analyses shown in this paper were also performed with three other
assumptions for equivalent ICEs where different engine options were available.
These included equivalents based on similar engine size, smallest IC engines,
largest IC engines. Since there are not three powertrain options for each
equivalent ICEV, these different choices made little or no difference to the
conclusions of this study.
Only the Toyota Prius has no direct equivalent, so the Toyota
Matrix (a mid-size hatchback of similardimensions) is selected
as its counterpart. Automatic transmissions were chosen for
the equivalent ICEVs in the set, and each has the same drive
type (two-wheel or all-wheel drive) as their HEV counterpart.
Vehicle weight and engine power data were obtained from
the published manufacturer specifications, and combined fuel
consumption estimates were used, corrected using the EPA
factors as described earlier.
First, we examine how vehicle weight and net system
power affects the fuel consumption within each vehicle set
(HEVs and ICEVs). A simple regression model is used,
with fuel consumption (Fc in table 3) as the dependent
variable. Weight (100 kg) and power (10 kW) are examined
independently in turn as predictors of fuel consumption. Each
analysis yields significant results at the 95% confidence level
for both HEVs and ICEVs. The results of this regression
analysis are presented in table 3. A 100 kg increase in weight
(not considering power) results in 0.72 l/100 km increase in
fuel consumption for HEVs, and 0.77 l/100 km increase in
fuel consumption for ICEVs. Similarly, a 10 kW power
increase (not considering weight) results in 0.29 l/100 km
increase in fuel consumption for HEVs, and an almost identical
increase for ICEVs. When weight and power are evaluated
simultaneously as predictors of fuel consumption, weight is
found to be a significant predictor of fuel consumption for
ICEVs, but not power. However, we find that the HEV
sample does not produce significant results for weight and
power coefficients at the 95% confidence interval. This is not
surprising since weight and power are correlated (correlation
coefficient =0.84). Tolerance tests using inflated variation
factors [17] did not detect significant multi-collinearity, and the
Cook’s-D test [18] did not reveal any outliers.
One way of coping with high correlation between weight
and power is to directly estimate the relationship between
differences in fuel consumption and differences in weight and
power within each vehicle class (HEVs and ICEVs). This
significantly increases the sample size (N=36), and helps
Environ. Res. Lett. 2(2007) 014003 C Reynolds and M Kandlikar
Tab l e 3. Regression analyses results for HEVs and equivalent ICEVs. N=9 for the simple regressions, and N=36 for the pairwise
difference regression. Fuel consumption is the predicted variable.
Regression coefficients
Predicted variableaPredictor variablesbCW P 95% confidence intervals R2
Fc C,W3.94 0.72c—(C:8.6, 0.7) (W: 0.44, 0.99) 0.84
Fc C,P3.34c0.29c(C: 1.2, 5.5) ( P: 0.16, 0.42) 0.8
Fc C,W,P1.5 0.43 0.14 (C:7,4) (W:0.03,0.9)
(P:0.6, 0.33)
Fc (pairwise
C,W,P0.16 0.4c0.14c(C:0.2, 0.51) (W: 0.23, 0.58)
(P: 0.07, 0.2)
Fcw C,P0.34 0.008c(C: 0.25, 0.44) (P: 0.002, 0.014) 0.6
Fc C,W1.60.77
c(C:6, 2.9) (W: 0.49, 1.07) 0.85
Fc C,P5.68c0.31c(C: 2.2, 9.16) (P: 0.09, 0.54) 0.61
Fc C,W,P1.12 0.70c0.04 (C:6.8, 4.5) (W: 0.17, 1.24)
(P:0.2, 0.3)
Fc (pairwise
C,W,P0.13 0.72c0.04 (C:0.51, 0.25) (W: 0.53, 0.92)
(P:0.04, 0.13)
Fcw C,P0.74 0.002 (C: 0.6, 0.88) (P:0.01, 0.007) 0.025
aFc =fuel consumption (l/100 km);Fcw=fuel consumption per 100 kg vehicle weight (l/100 km/100 kg).
bC=regression constant; W=weight coefficient (100 kg); P=power coefficient (10 kW).
cSignificant at the 95% confidence level.
mitigate the effects of high correlation between power and
weight. Here we find that HEVs and ICEVs perform quite
differently. Both weight and power difference are significant
for the HEVs, with a 100 kg weight increase and 10 kW
power increase resulting in 0.4l/100 km and 0.14 l/100 km
increase in fuel consumption, respectively. However, only a
change in weight is a significant predictor of fuel consumption
for the ICEVs, with a 100 kg increase in weight resulting in
.72 l/100 km increase in fuel consumption. Additionally,
the coefficient for power is not significant at the 95% level.
Increasing the sample size of ICEVs to include all engine
options available does not change this result. In order to
confirm our findings we also tested an alternative model using
weight-corrected fuel consumption (Fcw in table 3) to control
for the effect of correlation between weight and power. We
again found that a change in power is a significant predictor of
weight-corrected fuel consumption only for HEVs.
HEVs tend to have lower fuel consumption in urban
driving (with more stop–start driving) than highway driving
due to regenerative braking technology. This is contrary to fuel
consumption findings in conventional vehicles, which have
significantly lower fuel consumption under highway driving
conditions. Therefore we separately analyze fuel consumption
in city and highway driving as a function of weight and
power (table 4). Under city driving conditions, differences
in weight are not a significant predictor of differences in
fuel consumption for HEVs, but power is important. This
is because braking and acceleration are common during city
driving, and the full fuel-saving benefits of hybrid-electric
technology are realized. In contrast, during city driving
weight is a significant predictor of fuel consumption for ICEVs
because conventional technology cannot recover the energy
lost during deceleration. For highway conditions, on the
other hand, differences in weight are a strong determinant of
differences in fuel consumption for both HEVs and ICEVs.
In highway driving mode, both types of vehicle operate
primarily using their internal combustion engine, and hence
differences in weight (rather than peak system power) dictate
fuel consumption.
In summary, the analysis has demonstrated that differ-
ences in fuel consumption between different HEVs are ex-
plained by both weight and power, while for the equivalent
ICEVs they are explained primarily by weight. This is an im-
portant finding: despite the fact that hybrid-electric propulsion
systems use less fuel than conventional internal combustion
engines, heavier and more powerful vehicles are eroding the
fuel consumption benefit. Also, due to the way that hybrid-
electric systems recapture energy during vehicle braking, in-
creased weight is less of a factor in HEVs than in ICEVs.
4. Comparing HEV and ICEV fuel consumption
The share of HEVs in the North American light-duty passenger
vehicle fleet is expected to continue increasing, driven by their
potential for fuel savings and steadily reducing cost. A 2004
study forecasted that HEVs could capture 4–7% of the light-
duty vehicle market by 2008 and 10–15% by 2012 [12]. This
raises the question of how best to estimate the change in fuel
consumption that a given vehicle would realize, were it to use
a hybrid-electric system rather than a conventional engine. We
approach this problem by comparing the set of HEVs against
the sample of equivalent ICEVs, rather than within each set.
We regress fuel consumption differences using three ‘models’
of equivalence between HEVs and ICEVs (table 5).
Environ. Res. Lett. 2(2007) 014003 C Reynolds and M Kandlikar
Tab l e 4. Regression analyses results for HEVs and equivalent ICEVs with city and highway fuel consumption (pairwise comparison,
Regression coefficients
Predicted variableaPredictor variablesbCW P 95% confidence intervals R2
Fc (City) C,W,P0.11 0.15 0.26c(C:0.33, 0.56) (W:0.06, 0.36)
(P: 0.17, 0.34)
Fc (Highway) C,W,P0.32 0.75c0.02 (C: 0.13,0.53) (W: 0.67, 0.87)
(P:0.06, 0.014)
Fc (City) C,W,P0.16 0.29c0.26 (C:0.53, 0.68) (W: 0.02,0.61)
(P:0.13, 0.38)
Fc (Highway) C,W,P0.32 1.21c0.18c(C:0.91, 0.28) (W: 0.9, 1.5)
aFc =fuel consumption (l/100 km).
bC=regression constant; W=weight coefficient (100 kg); P=power coefficient (10 kW).
cSignificant at the 95% confidence level.
Tab l e 5. Across-group comparison of fuel consumption differences between HEVs and ICEVS. Crepresents the difference in fuel
consumption between HEVs and ICEVs due to the difference in propulsion system technology.
Regression coefficients
Dependent variableaIndependent variablesbCWPConfidence intervals R2
Model 1: Each HEV versus equivalent ICEV (N=9)
Fc C,W3.06c0.5 — (C: 1.68, 4.4) (W:0.48, 1.48) 0.17
Fc C,P2.5c0.1 (C: 1.66, 3.46) (P:0.1, 0.3) 0.17
Fc C,W,P2.8c0.25 0.06 (C: 0.4, 5.2) (W:1.75, 2.26)
(P:0.35, 0.47)
Model 2: HEV versus ICEV (group comparison N=81)
Fc C,W3.4d0.74d—(C: 3.15, 3.63) (W: 0.67, 0.81) 0.85
Fc C,P2.7d0.30d(C: 2.24, 3.0) (P: 0.25, 0.34) 0.70
Fc C,W,P3.25d0.56d0.1d(C: 3.01., 3.48) (W: 0.45, 0.68)
(P: 0.04, 0.14)
Model 3: HEV versus ICEV (group comparison with car and SUV sub-groups N=162)
Fc C,W3.3d0.86d—(C: 2.97, 3.7) (W: 0.75, 1) 0.86
Fc C,P2.5d0.32d(C: 2.34, 2.67) (W: 0.3, 0.35) 0.96
Fc C,W,P2.7d0.15c0.27d(C: 2.43, 2.87) (W: 0.02, 0.3)
(P: 0.22, 0.32)
aFc =fuel consumption (l/100 km).
bC=regression constant; W=weight coefficient (100 kg); P=power coefficient (10 kW).
cSignificant at the 95% confidence level.
dSignificant at the 99% level.
In each model, the dependent variable is the fuel
consumption difference between an HEV and an equivalent
ICEV, and corresponding differences in weight and power
are the independent variables. In each case, the regression
constant, C, is the difference in fuel consumption (in units
of l/100 km) between HEVs and ICEVs. This difference
can be attributed to the effect of using hybrid-electric
technology instead of conventional internal-combustion-
engine technology for the vehicle’s propulsion system. Model
1 is based on the assumption that consumers will switch from
a conventional vehicle to its hybrid counterpart; model 2
allows for any HEV to substitute for any ICEV; and model
3 embeds the assumption that consumers will only switch
from conventional cars to hybrid-electric cars, and likewise
for SUVs. In model 1, each HEV is compared only with its
equivalent (N=9). In model 2, we compare HEVs and
ICEVs as a class, i.e., each HEV was compared with every
ICEV (N=81). In model 3 we split each HEV and ICEV set
into two groups, SUV and cars, and compare all vehicles within
each group (N=45). This is because car and SUV HEVs are
likely to contribute to different relationships between weight,
power and fuel consumption.
Environ. Res. Lett. 2(2007) 014003 C Reynolds and M Kandlikar
Equivalence model 1
Only ‘technology’ (i.e. the use of a hybrid-electric propulsion
system rather than an internal combustion engine) was found to
be a significant predictor of the difference in fuel consumption.
On an average, the use of hybrid-electric technology resulted in
a fuel consumption benefit of 2.8l/100 km. Both weight and
power differences were found not to be significant at the 95%
level. The mean differences in power and weight for an HEV
and its ICEV equivalent were small (5% and 9%, respectively),
and the impact of these changes cannot be observed in the
face of substantial differences in fuel consumption. Though
significant, the constant has a large confidence interval and the
model has a low R2(0.18). This suggests wide variation in
the implementation of hybrid technology from model to model,
resulting in large variation in fuel consumption benefits.
Equivalence model 2
When HEVs and ICEVs are compared as a class, the model
shows highly significant results for each of the three predictors
(at the 99% confidence level). An HEV of the same weight and
power uses approximately 3.2l/100 km less fuel than its ICEV
counterpart. Further, an increase in weight of 100 kg results in
a fuel consumption increase of 0.56 l/100 km, and an increase
in power of 10 kW results in a fuel consumption increase
of 0.10 l/100 km. On an average 2007 HEVs are 136 kg
heavier than equivalent ICEVs, resulting in a weight-related
fuel consumption penalty of 0.75 l/100 km. Further, 2007
HEVs have 10 kW more average power than equivalent ICEVs,
resulting in a fuel consumption penalty of 0.1l/100 km. The
total penalty amounts to a reduction in the fuel consumption
benefits of HEVs by 27%.
Equivalence model 3
This model of equivalence allows us to differentiate between
cars and SUVs. Here we find significant effects (at the
95% confidence level) for all three predictors. The average
gain from using hybrid technology was 2.7l/100 km, while
the weight and power penalties were 0.15 l/100 km and
0.27 l/100 km, respectively. This is an interesting result,
because the weight coefficient is almost half the magnitude
of the power coefficient. Compare this result to model 2,
where the weight coefficient is over five times as large as the
power coefficient. The reason for the change in magnitude of
the coefficients is because this model compares like vehicles
with like, and cars are different from SUVs in a number of
important ways. The three hybrid-electric SUVs use all-wheel
drive rather than two-wheel drive, which results in an extra fuel
saving since all of the four wheels provide regenerative braking
capability [7]. Also, SUVs generally have a higher coefficient
of drag than cars and have a larger frontal area.
5. Conclusions
In summary, based on this statistical analysis of 2007 model
year HEVs and their equivalent ICEVs sold in the United
States, this study presents the following conclusions.
(1) Heavier and more powerful hybrid-electric vehicles are
eroding the fuel consumption benefit of this technology.
(2) The fuel consumption penalty imposed by increased
vehicle weight is significantly lower in HEVs than in
equivalent ICEVs. A 100 kg change in vehicle weight
increases fuel consumption by only 0.4l/100 km in
HEVs, compared with 0.7l/100 km in ICEVs.
(3) Three different equivalence models (based on different
comparisons of HEVs against ICEVs) yielded fuel
consumption benefits ranging from 2.7 to 3.25 l/100 km,
with varying effectsof changes in weight and power.
(4) When the HEVs are compared with their ICEV
equivalents (model 3, grouped into cars and SUVs),
the average fuel consumption benefit of an HEV was
2.65 l/100 km. This analysis reveals that an HEV
that is 100 kg heavier than an identical ICEV, holding
everything else constant, has a fuel consumption penalty
of 0.15 l/100 km. Likewise, an HEV that is 10 kW more
powerful than the ICEV results in a fuel consumption
penalty of 0.27 l/100 km.
Hybrid technology is new, and it will evolve as the technology
matures and diffuses within the automobile fleet. We have
shown that, even in the relatively short time that HEVs
have been commercially available, there have been significant
changes in the dimensions, performance and fuel consumption
of the fleet. The number of models available is small, and this
small sample size will be an inevitable challenge for analyses
of the fleet done today. As the number of HEV types increases,
we will no doubt get a more robust picture of the technology.
We thank Kimberly Jones for her assistance assembling vehicle
sales and specifications data, and Gordon McTaggart-Cowan
and Julian Marshall for valuable comments on a draft of this
article. The University of British Columbia Bridge Program,
the Natural Science and Engineering Research Council of
Canada, and the Auto21 Network of Centres of Excellence
provided support for this research.
[1] An F, Vyas A, Anderson J and Santini D 2001 Evaluating
commercial and prototype HEVs SAE Technical Paper
[2] An F, Stodolsky F and Santini D 1999 Hybrid options for
light-duty vehicles SAE Technical Paper 1999-01-2929
[3] Hoffert M I et al 2002 Advanced technology paths to global
climate stability: energy for a greenhouse planet Science
298 981–7
[4] Pacala S and Socolow R 2004 Stabilization wedges: solving the
climate problem for the next 50 years with current
technologies Science 305 968–72
[5] Jackson R B and Schlesinger W H 2004 Curbing the U.S.
carbon deficit Proc. Natl Acad. Sci. 101 15827–9
[6] Romm J 2006 The car and fuel of the future Energy Policy
34 2609–14
[7] An F and Santini D J 2004 Mass impacts on fuel economies of
conventional vs hybrid electric vehicles SAE Trans. 113
Environ. Res. Lett. 2(2007) 014003 C Reynolds and M Kandlikar
[8] An F and Santini D 2003 Assessing tank-to-wheel efficiencies
of advanced technology vehicles SAE Technical Paper
[9] Berman B 2006 Hybrid vehicles in the US: available and
expected models available from
[10] Heavenrich R M 2006 Light-duty automotive technology and
fuel economy trends: 1975 through 2006 report no.
EPA420-R-06-011, Office of Transportation and Air Quality,
US Environmental Protection Agency
[11] Millikin M 2006 Green car congress: energy, technologies,
issues and policies for sustainable mobility available from
[12] Greene D L, Duleep K G and McManus W 2004 Future
Potential of Hybrid and Diesel Powertrains in the US
Light-Duty Vehicle Market report no. ORNL/TM-2004/181
(Oak Ridge, TN: Oak Ridge National Laboratory)
[13] US Environmental Protection Agency 2006 Fuel economy
labeling of motor vehicles: revisions to improve calculation
of fuel economy estimates report no EPA420-D-06-002,
Office of Transportation and Air Quality, US Environmental
Protection Agency
[14] Lipman T E and Delucchi M A 2006 A retail and lifecycle cost
analysis of hybrid electric vehicles Transport Res. D
11 115–32
[15] B C Climate Exchange 2005 The hybrid experience report:
Toyota Prius fuel performance available from http://www. Prius Fuel Performance.htm
[16] Santini D, Vyas A D and Anderson J L 2002 Fuel economy
improvement via hybridization vs vehicle performance level
SAE Technical Paper 2002-01-1901
[17] Mansfield E R and Helms B P 1982 Detecting multicollinearity
Am. Stat. 36 158–60
[18] Cook R D 2000 Detection of influential observation in linear
regression Technometrics 42 65–8
... And the total duration of the test was 200s and all the parameters such as engine power, battery power, electric power, speed of engine and the electric motor, fuel consumption, etc. were determined by different sensors connected to each component of the simulation. According to some research [3], [4], [5], the comparison between the Parallel Hybrid electric vehicle (PHEV) and the conventional ICE Vehicles (ICEV) were made by collecting the data for every parameter in real world driving of both the vehicle type PHEV and ICEV and compare them. The Software use here was known as Autonomie. ...
... The simulation shows that the vehicle can run on electric motor independently for performance requirement in parallel mode of HEV powertrain. According to studied research [3], [4], [5], it can be concluded that PHEV improves the fuel economy as well as engine efficiency in standard driving cycle as well as real world driving cycle. The improvement in engine efficiency also increased the life span of the engine. ...
Full-text available
This paper discusses about the Hybrid Electric Vehicle (HEV) and the simulation on the Parallel Hybrid Electric Vehicle Drivetrain. The concept and the classification of the HEV is discussed here and the simulation and analysis of a drivetrain for Parallel Electric Hybrid Engine (PHEV) was formulated on MATLAB Simulink. The methodology of simulation was to accelerate or decelerate the vehicle independently with the electric motor with engine running at constant speed. And the parameters such as electric motor and engine power were compared and observed. And research on CO2 emission and fuel consumption was made between the PHEV and Conventional Vehicle (ICE Vehicle). And the conclusion was made according to the research and the simulation that the development of HEV will have a great potential in fuel economy improvement.
... The study [10] showed that rising in vehicles' weight average by 30% between (2000)(2001)(2002)(2003)(2004)(2005)(2006) increased the combined fuel consumption by approximately 15%. The vehicle weight affected the fuel consumption of the ICEVs in a way that each additional 100 kg in vehicle weight (separately from system power) can increase the fuel consumption by 0.7 l/100 km. ...
... However, under highway driving conditions, the ICEVs fuel consumption differences are mainly affected by the vehicle weight rather than the system peak power. The vehicle weight differences primarily attribute the fuel consumption differences for the ICEVs under both city and highway driving conditions [10]. ...
Full-text available
This manuscript instrumented two light-duty passenger cars to construct real-world driving cycles for the Baghdad-Basrah highway road in Iraq using a data logger. The recorded data is conducted to obtain typical speed profiles for each vehicle. Each of the recruited vehicles is modelized using Advanced Vehicle Simulator and conducted on the associated created driving cycle to investigate fuel economy and analyze performance. Moreover, to inspect the influence of driving behavior on fuel consumption and emissions, the simulation process is re-implemented by substituting the conducted real-world driving cycle. The analyses are done for the first and second stages of simulation predictions to explore the fuel-penalty of aggressive driving behavior. The analysis for substitution predictions showed that fuel consumption could be reduced by 12.8% due to conducting vehicle under the more consistent real-world driving cycle. However, conducting vehicle under the more aggressive one would increase fuel consumption by 14.6%. The associated emissions change prediction due to the substitution is also achieved and presented.
... A combination of regulatory requirements [1] and market demand [2,3] have pushed for continuous improvements in energy efficiency and performance in transport solutions. Weight reduction through the use of new lightweight alloys and new structural designs is a way to achieve these goals [3]. ...
... A combination of regulatory requirements [1] and market demand [2,3] have pushed for continuous improvements in energy efficiency and performance in transport solutions. Weight reduction through the use of new lightweight alloys and new structural designs is a way to achieve these goals [3]. In order to implement lighter materials and innovative structural designs new manufacturing processes are required. ...
Aeronautical structures have been assembled for decades using a wide variety of welding and joining techniques. Over the last 15-20 years significant developments in joining techniques have occurred. Aluminium alloys have been the main traditional materials in civil aeronautical industry for the fuselage and structural parts. In order to reduce weight, improving fuel efficiency, there is the need to develop innovative solutions to join aluminium components in a single lap joint (SLJ) configuration with higher strength to weight ratio than riveting and fastening. In this work, a combination of the friction stir welding (FSW) and adhesive bonding (AB) processes is presented. Quasi-static mechanical properties, fatigue behaviour and other properties of the friction stir weld-bonding joints were assessed and compared with adhesive only and friction stir welded only joints. The development of this new joining technology, combining FSW with AB, resulting in friction stir weld-bonding, aims to incorporate properties and characteristics of both joining technologies, as well as improving damage tolerance. The present research involved the production of two types of overlap joints - FSW and hybrid friction stir weld-bonding. The main objective of this study is to compare the different joining technologies in lap joint configuration and evaluate the influence of different parameters on the mechanical behavior of the joints. The hybrid joints present higher strength, ductility and hardness, with the highest joint efficiency achieved in the hybrid joint produced with 450 kgf. These findings lead to the conclusion that - hybridization process confers a joint efficiency improvement between 20-30 % in most cases.
... The relationship between fuel consumption and vehicle weight is significantly influenced by the type of hybrid used [32]. In their work, Reynolds et al. [33] indicate that in an HEV, an increase in weight by every 100 kg results in an increase in fuel consumption of 0.4 dm 3 /100 km, while in conventional vehicles it is 0.7 dm 3 /100 km. A significant number of the papers comparing the performance of powertrains in passenger vehicles are simulation works, or those based on manufacturers' catalog data. ...
Full-text available
Hybrid propulsion dedicated to light duty vehicles is seen as an evolutionary change from internal combustion engine (ICE) to electric propulsion. Widespread direct replacement of convection ICEs in the current energy system is impossible because ICEs are vehicles’ main source of mechanical energy. The hybrid powertrain uses the advantages of electric propulsion with the ability to charge the traction battery or have the internal combustion engine assist the system. The article compares different types of hybrid drives (with a small share of plug-in hybrid propulsion) under typical urban driving conditions. Nine vehicles were tested, and the tests were conducted over several months in various cities in Poland. The terms of the research conducted were not under the requirements of the driving test. However, they are authoritative when using the vehicle in real traffic conditions. Such conditions take into account many aspects that are relevant to a road test. It was found that urban conditions are a very suitable environment for hybrid propulsion systems, as they cover more than 50% of the distance in electric mode, regardless of the initial battery charge, in most cases.
... The benefits of Li-ion batteries as energy storage devices have been evaluated in [167]. The fuel economy is dropped due to the high weight of hybrid electric vehicles [168]. Nickel-metal hydride (NiMH) batteries perform better in hybrid and electric vehicles and have trustworthy automotive applications [107]. ...
Full-text available
Transportation electrification is a pivotal factor in accelerating the transition to sustainable energy. Electric vehicles (EVs) can operate either as loads or distributed power resources in vehicle-to-grid (V2G) or vehicle-to-vehicle (V2V) linkage. This paper reviews the status quo and the implications of transportation electrification in regard to environmental benefits, consumer side impacts, battery technologies, sustainability of batteries, technology trends, utility side impacts, self-driving technologies, and socio-economic benefits. These are crucial subject matters that have not received appropriate research focus in the relevant literature and this review paper aims to explore them. Our findings suggest that transitioning toward cleaner sources of electricity generation should be considered along with transportation electrification. In addition, the lower cost of EV ownership is correlated with higher EV adoption and increased social justice. It is also found that EVs suffer from a higher mile-per-hour charging rate than conventional vehicles, which is an open technological challenge. Literature indicates that electric vehicle penetration will not affect the power grid in short term but charging management is required for higher vehicle penetration in the long-term scenario. The bi-directional power flow in a V2G linkage enhances the efficiency, security, reliability, scalability, and sustainability of the electricity grid. Vehicle-to-Vehicle (V2V) charging/discharging has also been found to be effective to offload the distribution system in presence of high EV loads.
... where is the density of fuel ( = 0.725 kg∕L), q is the calorific value of fuel (q = 0.46 × 10 7 J∕kg), s is the power conversion efficiency of fuel oil ( s = 40%), is the fuel consumption caused per 100 kg weight increase, which is 0.7 L in this study. 35 ...
Full-text available
The organic Rankine cycle (ORC) system can effectively recover waste heat from engines of heavy‐duty trucks, and is a promising method to improve the efficiency of on‐board engines. However, engine operating conditions fluctuate greatly while driving, the waste heat recovery system must often work under off‐design conditions, which significantly affects system performance. Further, different component structures can also affect the off‐design performance of the system. Thus, a novel design method of preheating organic Rankine cycle (P‐ORC) system harvesting waste heat of heavy‐duty trucks based on off‐design performance is proposed in this study. The design method includes selection of the optimal types of components and design point to optimize the comprehensive performance of the waste heat recovery system in all road conditions. In this study, different heat exchanger combinations are applied to the P‐ORC system to obtain six different design systems. According to the principle of uniform coverage, the scatter diagram of exhaust temperature and mass flow rate of the engine under real road conditions are discretized into 19 alternative design points. Each system is designed with 19 discretized design points, and a total of 114 design systems are obtained. The optimal heat exchanger combination and design point are selected based on the off‐design performance. It is concluded that a P‐ORC system using a combination of plate preheater, finned tube air cooler, and shell‐tube evaporator is the optimal system. The optimal design point number is 10, and the corresponding engine speed at is 1471 rpm, the engine torque is 474 Nm, the occurrence probability is 14.48%, the exhaust temperature is 350°C, the exhaust mass flow rate is 0.11 kg/s, and the maximum combined net power output is 4.26 kW. The results reveal that the optimal design point of the system can be selected at the design point with medium engine load and high occurrence probability. It guides the system design toward a more practical direction, so as to obtain an optimal system that could operate efficiently and recover more waste heat under the full working conditions of the engines. This novel design method can be extended for other cycle configurations.
... However, Bushi et al. (2015) found the GHG emission mitigation potential for the MMLV relative to the Ford Fusion (Ford Motor Company, 2016) to be highly sensitive to lifetime driving distance and the fuel savings associated with lightweighting. Fuel savings depend on the powertrain type (Pagerit et al., 2006;Reynolds and Kandlikar, 2007;Brooker et al., 2013;Carlson et al., 2013;An and santini, 2004;Wohlecker et al., 2007;Wilhelm et al., 2012). ...
Full-text available
Vehicle lightweighting reduces fuel cycle greenhouse gas (GHG) emissions but may increase vehicle cycle (production) GHG emissions because of the GHG intensity of lightweight material production. Life cycle GHG emissions are estimated and sensitivity and Monte Carlo analyses conducted to systematically examine the variables that affect the impact of lightweighting on life cycle GHG emissions. The study uses two real world gliders (vehicles without powertrain or battery) to provide a realistic basis for the analysis. The conventional and lightweight gliders are based on the Ford Fusion and Multi Material Lightweight Vehicle, respectively. These gliders were modelled with internal combustion engine vehicle (ICEV), hybrid electric vehicle (HEV), and battery electric vehicle (BEV) powertrains. The probability that using the lightweight glider in place of the conventional (steel-intensive) glider reduces life cycle GHG emissions are: ICEV, 100%; HEV, 100%, and BEV, 74%. The extent to which life cycle GHG emissions are reduced depends on the powertrain, which affects fuel cycle GHG emissions. Lightweighting an ICEV results in greater base case GHG emissions mitigation (10 t CO 2 eq.) than lightweighting a more efficient HEV (6 t CO 2 eq.). BEV lightweighting can result in higher or lower GHG mitigation than gasoline vehicles, depending largely on the source of electricity.
Supercapacitors based on carbon fiber reinforced polymer (CFRPs) were studied and the influence of surface treatment on mechanical and electrochemical properties was explored. Electrodes were prepared by deposition of graphene nanoplatelets (GNPs) combined with different binders (PVDF and PVA) onto the surface of a carbon fiber fabric. A significant decrease in the Interlaminar Shear Strength (ILSS) is observed when comparing the solid polymer electrolyte to the structural resin (around 50 %). Moreover, the addition of any binder promotes a decrease in the ILSS due to lower interfacial properties (around 20 % when compared to the GNP-coated condition). Electrochemical impedance spectroscopy (EIS) analysis proves that the structural capacitor can be fitted with an equivalent circuit consisting of R-CPE series elements. An increase of the bulk resistance was observed when using a binder (29.7 and 22.7 kΩ) when compared to the GNP-only-coated (10.2 kΩ). For this reason, the structural supercapacitor with the best properties was the GNP-only-coated one with a specific capacitance and coulombic efficiency, calculated by Galvanostatic charge-discharge (GCD), of 5.2 mF/g, showing also high stability of electrochemical properties over time. Energy storage capability was successfully demonstrated by a proof of concept consisting of powering a LED after a short charge time of the device.
In this paper, hybrid electric vehicle (HEV) that powered by hydrogen (H2) enriched internal combustion (ICE) engine was studied both simulation and experimental. As an alternative fuel, the usage of H2 as additional fuel on an ICE that used for HEV, investigated in this partially simulational study for the first time. The study was consisted two parts. In the first part, the effects of 10% H2 enrichment on performance and emissions were experimented in a 1.8 L Ford Spark Ignition (SI) engine. Then, AVL Boost tool was used for simulation and validation under this ICE's properties and has compared with experimental results. After the simulations and experiments results were found consistent each other, AVL Cruise was used for hybridization of H2 enriched ICE, for the second part. Combustion, performance and emission values are given comparatively with selected driving cycle of model vehicle were realized with simulation tools. Hybrid mode's ICE becomes more environmentally friendly due to H2 enrichment with increasing performance. The model HEV has delivered promising results on performance and emission values and this improvement added to literature with this study. Results showed that, enrichment of H2 is presented 3.56% improvement in ICE torque and 2.37% for ICE power. Cumulative fuel consumption and emission pollution decreased by 12.6% and 14–33% respectively, for hybrid mode.
Full-text available
Although many of the studies that use vehicle simulation models to estimate fuel economy gains for a range of hybrid vehicles have attempted to control for the comparability of performance between conventional and hybrid vehicles, different rules and simulation models have been used. This paper reviews the estimates of city, highway, and corporate average fuel economy gain vs. varying measures of performance change for a set of those studies. We examine the causes for the wide range in estimates when hybridizing a vehicle, establish a database, and provide detailed discussions of relationships using several of the studies. Statistical models developed on the basis of the data reveal the causes of variation in mpg gain among conventional/hybrid pairs that have the same 0-60 mph acceleration times. Our study reveals that potential mpg gain via hybridization is greater as the 0-60 mph acceleration time of the pair of compared vehicles drops (and power-to-weight ratios increase). We demonstrate that engine downsizing is necessary to obtain large benefits, and that an increase in electric motor power relative to engine power - up to a point - improves the fuel economy of hybrids.
Full-text available
This paper analyzes four recent major studies carried out by MIT, a GM-led team, Directed Technologies, Inc., and A. D. Little, Inc. to assess advanced technology vehicles. These analyses appear to differ greatly concerning their perception of the energy benefits of advanced technology vehicles, leading to great uncertainties in estimating full-fuel-cycle (or "well-to-wheel") greenhouse gas (GHG) emission reduction potentials and/or fuel feedstock requirements per mile of service. Advanced vehicles include, but are not limited to, advanced gasoline and diesel internal combustion engine (ICE) vehicles, hybrid electric vehicles (HEVs) with gasoline, diesel, and compressed natural gas (CNG) ICEs, and various kinds of fuel-cell based vehicles (FCVs), such as direct hydrogen FCVs and gasoline or methanol fuel-based FCVs. We focus on variations in estimates of vehicle gasoline-equivalent fuel energy use, glider and powertrain masses, and introduce powertrain effectiveness as a new surrogate measure for tank-to- wheel vehicle efficiency. We conclude that, while the degree of uncertainty across studies is considerable, it is not as great as a summary investigation and direct comparison implies. Our investigation suggests that there are logical and systematic reasons for variations among the studies. Further studies are required to improve both assessment and understanding of technical potentials of these advanced technologies, and to narrow the range of uncertainty currently present.
Full-text available
The U.S. emitted ≈1.58 petagrams (Pg) of fossil fuel carbon in 2001, approximately one-quarter of global CO2 production. With climate change increasingly likely, strategies to reduce carbon emissions and stabilize climate are needed, including greater energy efficiency, renewable energy sources, geoengineering, decarbonization, and geological and biological sequestration. Two of the most commonly proposed biological strategies are restoring organic carbon in agricultural soils and using plantations to sequester carbon in soils and wood. Here, we compare scenarios of land-based sequestration to emissions reductions arising from increased fuel efficiency in transportation, targeting ways to reduce net U.S. emissions by 10% (≈0.16 Pg of carbon per year). Based on mean sequestration rates, converting all U.S. croplands to no-till agriculture or retiring them completely could sequester ≈0.059 Pg of carbon per year for several decades. Summary data across a range of plantations reveal an average rate of carbon storage an order of magnitude larger than in agricultural soils; in consequence, one-third of U.S. croplands or 44 million hectares would be needed for plantations to reach the target of ≈0.16 Pg of carbon per year. For fossil fuel reductions, cars and light trucks generated ≈0.31 Pg of carbon in U.S. emissions in 2001. To reduce net emissions by 0.16 Pg of carbon per year, a doubling of fuel efficiency for cars and light trucks is needed, a change feasible with current technology. Issues of permanence, leakage, and economic potentials are discussed briefly, as is the recognition that such scenarios are only a first step in addressing total U.S. emissions. • agriculture and plantations • carbon sequestration • fossil fuel emissions • leakage and permanence • soil organic carbon
In recent years, vehicle manufacturers have made great progress in developing and demonstrating commercially available and prototyped hybrid electric vehicles (HEVs). These vehicles include commercially available gasoline hybrid cars (Toyota Prius and Honda Insight) and Partnership for the Next Generation Vehicle (PNGV) diesel hybrid prototypes (Ford Prodigy, GM Precept, and DaimlerChrysler ESX3). In this paper, we discuss tested and claimed fuel benefits and performance of these commercial and prototyped HEVs relative to conventional vehicles (CVs) that are otherwise similar to these HEVs, except for hybridization. We also describe a reverse-engineering approach to de-hybridize or "conventionalize" these five existing commercial and prototyped HEVs. Because these commercial and prototyped HEVs represent a variety of technological choices, configurations, and development stages, this analysis gives us in-depth knowledge about how each of these vehicles achieves high efficiency. An Argonne National Laboratory (ANL) component sizing model (HEVCOST) and a conventional vehicle fuel economy and performance model (Modal Energy and Emissions Model [MEEM]) are used to aid our analysis. To cross check the results, we also run the hybrid simulation model ADVISOR.
The strong correlation between vehicle weight and fuel economy for conventional vehicles (CVs) is considered common knowledge, and the relationship of mass reduction to fuel consumption reduction for conventional vehicles (CVs) is often cited without separating effects of powertrain vs. vehicle body (glider), nor on the ground of equivalent vehicle performance level. This paper challenges the assumption that this relationship is easily summarized. Further, for hybrid electric vehicles (HEVs) the relationship between mass, performance and fuel consumption is not the same as for CVs, and vary with hybrid types. For fully functioning (all wheel regeneration) hybrid vehicles, where battery pack and motor(s) have enough power and energy storage, a very large fraction of kinetic energy is recovered and engine idling is effectively eliminated. This paper assesses two important impacts of shifting from conventional to hybrid vehicles in terms of the mass vs. fuel economy relationship - (1) significant improvements in fuel economy with little or no change in mass, and (2) once a switch to hybrid powertrains has been made, the effectiveness of mass reduction in improving fuel economy will be diminished relative to conventional vehicles. In this paper, we discuss vehicle tractive load breakdowns and impacts of hybridization on vehicle efficiency, discuss capture of kinetic energy by conversion to electrical energy via regenerative braking, assess benefits of shutting off the engine when the vehicle does not require power, and investigate energy losses associated with vehicle mass.
A new measure based on confidence ellipsoids is developed for judging the contribution of each data point to the determination of the least squares estimate of the parameter vector in full rank linear regression models. It is shown that the measure combines information from the studentized residuals and the variances of the residuals and predicted values. Two examples are presented.
Conference Paper
Hybrid electric vehicles (HEVs) offer great promise in improving fuel economy. In this paper, we analyze why, how, and by how much vehicle hybridization can reduce energy consumption and improve fuel economy. Our analysis focuses on efficiency gains associated solely with vehicle hybridization. We do not consider such other measures as vehicle weight reduction or air- and tire-resistance reduction, because such measures would also benefit conventional technology vehicles. The analysis starts with understanding the energy inefficiencies of light-duty vehicles associated with different operation modes in US and Japanese urban and highway driving cycles, with the corresponding energy-saving potentials. The potential for fuel economy gains due to vehicle hybridization can be estimated almost exclusively on the basis of three elements: the reducibility of engine idling operation, the recoverability of braking energy losses, and the capability of improving engine load profiles to gain efficiency associated with specific HEV configurations and control strategies. Specifically, we evaluate the energy efficiencies and fuel economies of a baseline MY97 Corolla-like conventional vehicle (CV), a hypothetical Corolla-based minimal hybrid vehicle (MHV), and a MY98 Prius-like full hybrid vehicle (FHV). We then estimate energy benefits of both MHVs and FHVs over CVs on a performance-equivalent basis. We conclude that the energy benefits of hybridization vary not only with test cycles, but also with performance requirements. The hybrid benefits are greater for ''Corolla (high) performance-equivalent'' vehicles than for ''Prius (low) performance-equivalent'' vehicles. An increasing acceleration requirement would result in larger fuel economy benefits from vehicle hybridization.
This paper is based on a review of the technical literature on alternative fuel vehicles (AFVs) and discussions with experts in vehicle technology and energy analysis. It is derived from analysis provided to the bipartisan National Commission on Energy Policy.The urgent need to reverse the business-as-usual growth path in global warming pollution in the next two decades to avoid serious if not catastrophic climate change necessitates action to make our vehicles far less polluting.In the near-term, by far the most cost-effective strategy for reducing emissions and fuel use is efficiency. The car of the near future is the hybrid gasoline–electric vehicle, because it can reduce gasoline consumption and greenhouse gas emissions 30 to 50% with no change in vehicle class and hence no loss of jobs or compromise on safety or performance. It will likely become the dominant vehicle platform by the year 2020.Ultimately, we will need to replace gasoline with a zero-carbon fuel. All AFV pathways require technology advances and strong government action to succeed. Hydrogen is the most challenging of all alternative fuels, particularly because of the enormous effort needed to change our existing gasoline infrastructure.The most promising AFV pathway is a hybrid that can be connected to the electric grid. These so-called plug-in hybrids or e-hybrids will likely travel three to four times as far on a kilowatt-hour of renewable electricity as fuel cell vehicles. Ideally these advanced hybrids would also be a flexible fuel vehicle capable of running on a blend of biofuels and gasoline. Such a car could travel 500 miles on 1 gal of gasoline (and 5 gal of cellulosic ethanol) and have under one-tenth the greenhouse gas emissions of current hybrids.
This paper analyzes the manufacturing costs, retail prices, and lifecycle costs of five hybrid gasoline-electric vehicle types in high-volume production. Updating and major modifications are made to a detailed motor vehicle retail and lifecycle cost spreadsheet model that had previously been used to analyze the costs of conventional vehicles, electric-drive vehicles, and other alternative-fuel vehicles. This cost model is combined with a hybrid vehicle design and performance analysis using the ADVISOR vehicle simulation model. Five hybrid vehicle designs were examined for each vehicle type, for a total of 25 hybrid vehicle cases and a set of five baseline gasoline vehicles for comparison. It is found under various assumptions that combining the advanced package of vehicle improvements with mild vehicle hybridization provides the least-cost the hybrid vehicle option, with lifecycle costs very close to those of the baseline vehicles even using the relatively low historical gasoline price of $1.46 per gallon. However, with recent higher gasoline prices then many of the more fuel efficient, but costlier, hybrid vehicle designs become competitive from a lifecycle cost perspective.