Hybrid-Electric Bus Fuel Consumption Modeling:
Model Development and Comparison to
Conventional Buses
Jinghui Wang, Graduate Research Assistant
Charles E. Via, Jr. Department of Civil and Environmental Engineering
Center for Sustainable Mobility at the Virginia Tech Transportation Institute
3500 Transportation Research Plaza, Blacksburg, VA 24061
Email: jwang@vtti.vt.edu
Hesham A. Rakha (Corresponding author)
Samuel Reynolds Pritchard Professor of Engineering at Virginia Tech
Center for Sustainable Mobility at the Virginia Tech Transportation Institute
3500 Transportation Research Plaza, Blacksburg, VA 24061
Email: hrakha@vtti.vt.edu
Phone: (540)-231-1505
Submitted for Presentation and Publication at the 95th Transportation Research Board
Annual Meeting, Washington D.C., January, 2016
Date of Re-submission: November 2015
4054 words + 6 figures + 2 tables = 6054 words equivalent
November 8, 2015
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ABSTRACT
Electric hybridization technologies appear to be one of the most promising approaches to improve
bus energy efficiency, however this improvement has not been systematically quantified. The accuracy of fuel consumption estimates is critical to precisely quantify the benefits of hybrid buses.
Consequently, the objective of this study is to develop a hybrid-bus fuel consumption model based
on the Virginia Tech Comprehensive Power-based Fuel Consumption Model (VT-CPFM) framework to enhance the accuracy of fuel estimates, and thereafter quantify the benefits associated with
hybridization technologies relative to conventional diesel bus operations. The model estimates are
demonstrated to be consistent with in-field measurements, and the optimum fuel economy cruise
speed is demonstrated to be approximately 50 km/h. The results demonstrate that hybrid buses
produce lower fuel consumption levels overall, while heavier buses and higher passenger loads
may reduce the fuel saving benefits. The results also reveal that more fuel savings can be achieved
for cruise and stop-and-go activity compared to idling behavior, and that stop-and-go operation
generates the highest level of fuel efficiency benefits. The conclusions of this paper can support
bus planning applications to achieve fleet fuel savings.
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INTRODUCTION
Transportation-related energy consumption accounts for 28% of the total U.S. energy use, accompanied by producing a share of 34.7% of carbon dioxide (CO2 ) emissions, as reported by (1).
Improving energy efficiency and reducing Greenhouse Gas (GHG) emissions have attracted significant attention in the transportation sector. It is expected that GHG emissions be reduced from
2005 levels by over 30% by 2030 and by 80% by 2050 (2). Public transit outperforms private vehicles in reducing per capita fuel consumption and GHG emissions due to its higher ridership, and
thus is considered as an effective countermeasure to mitigate fossil fuel consumption and climate
change. Nonetheless, bus fuel consumption has been continually increasing from 827 million gallons/year to 2,059 million gallons/year between 1960 and 2012, which makes it critical to enhance
bus fuel efficiency in reducing energy consumption and CO2 production.
Electrical hybridization appears to be one of the most promising and cost-effective approaches to improve bus energy efficiency. Hybrid Electric Buses (HEBs) have two advantages
over conventional diesel buses (CDBs) in saving fuel energy. First, the energy lost while braking
could be partly recovered and stored in the form of electricity by the regenerative system embedded in HEBs; second, there are no mechanical links between the HEBs’ engines and their wheels,
which makes the engine always operate in the most fuel efficient manner. Consequently, HEBs
have the potential to minimize bus fuel consumption levels and CO2 emissions.
The energy and environmental benefits of hybrid electric vehicles (HEVs) have been the
focus of many studies. For example, Reynolds et al. (3) compared hybrid light-duty vehicles
(HLDVs) for sale in the United States to equivalent conventional vehicles using regression analysis and found that the average fuel consumption benefit was 2.7 (l/100 km); while they also
indicated that heavier and more powerful hybrid vehicles might reduce such benefits. Fontaras et
al.(4) tested two HLDVs (Honda Civic and Prius) using real world simulation cycles. The authors
demonstrated that both vehicles produced improved energy efficiency and reduced CO2 emissions
with a fuel saving of between 40% and 60%. They also indicated that ambient temperature was an
important factor that affected the fuel savings of hybridization technologies, which was empirically
verified by Zahabi et al.(5) who found that low ambient temperature was a detrimental factor in reducing fuel consumption levels and might cause a decrease of 20% in fuel efficiency in the winter
relative to the summer. Ahn et al.(6) analyzed the impact of a regenerative braking system on the
improvement of fuel efficiency, and demonstrated that HEVs were capable of improving the fuel
economy by 20%-50% depending on the engine size. In addition to light duty vehicles (LDVs),
attention has been devoted to heavy duty trucks (HDTs) as well. Lajunen (7) simulated conventional diesel powered and parallel hybrid heavy vehicle combinations in the Autonomie vehicle
simulation software, and the results showed that fuel savings of up to 6% were achieved through
hybridization. Specifically, the authors indicated that hybridization would achieve higher fuel efficiency in a hilly terrain. The simulation-based approach was also adopted by (8) to compare
the fuel consumption and CO2 production between conventional- and hybrid-commercial vehicles.
The results concluded that hybridization was an efficient technology reducing fuel consumption
and CO2 emission levels by approximately 30% . Russell et al.(9) investigated the fuel performance of class eight heavy duty trucks and concluded that hybrid trucks achieved up to a 60%
reduction in fuel consumption levels and up to 36% reduction in CO2 emissions.
The aforementioned modeling and simulation efforts have explicitly addressed various degrees of benefit in terms of energy efficiency and emissions from LDVs and HDTs; however, only a
few studies have provided any insights into public transit buses. Soylu (2) quantified the improve-
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ment of bus energy efficiency derived from electricity hybridization using the cumulative energies
(CEs) per kilometer of travel of buses (CEs consist of positive traction energy (PTE), negative traction energy (NTE), brake engine energy (BEE)). The results indicated that the CEs of hybrid buses
were 12%(PTE), 8%(NTE), and 21% (BEE) lower than those of conventional buses, respectively.
However, hybrid buses did not produce the expected fuel savings in (10)’s study. Consequently,
the effectiveness of hybrid technology in reducing bus fuel consumption levels remains open to
further investigation.
The accuracy of bus fuel consumption estimates is of essence to precisely model differences
in fuel consumption levels between conventional- and hybrid- buses. An efficient and accurate fuel
consumption modeling approach is thus needed. Traditional bus fuel consumption modeling approaches are classified into two categories: physically based analytical models and empirically
based models (11). The typical examples of physically based analytical models include the Comprehensive Modal Emission Model (CMEM) (12), EcoGest (13), and the Physical Emissions Rate
Estimator (PERE) (14). The empirically based models are mainly exemplified by VT-Micro (15),
the Motor Vehicle Emissions Simulator (MOVES) and VSP-based approaches. Apart from the
VT-Micro model, all these models produce a bang-bang type of control system in which the partial derivative of fuel consumption with respect to vehicle power is not a function of the vehicle
power. This results in a type of control in which the optimum fuel economy may be achieved at
full throttle acceleration or full braking deceleration. In this case drivers have to either accelerate
at full throttle or brake at full braking in order to minimize their fuel consumption levels, which
appears to contradict empirical observations. The Virginia Tech Comprehensive Power-based Fuel
Consumption Model (VT-CPFM) modeling approach, initially developed by (16) to estimate LDV
fuel consumption levels, eliminates the bang bang control by introducing a quadratic term in the
relationship between fuel consumption and vehicle power. This study extends the VT-CPFM to
model hybrid bus fuel consumption levels in order to accurately capture the fuel efficiency of
hybrid buses.
Bus fuel consumption and efficiency depend on vehicle and engine attributes, passenger
load, travel speed, road grade, stop-and-go activity, etc. (17). In Soylu’s study (2), the authors
demonstrated that the route vertical profiles (road grade) and the frequency of stop-and-go operations had a dramatic impact on the braking and traction energies of HEBs and CDBs. Frey et
al.(11) developed a vehicle specific power (VSP)-based approach to model conventional dieseland hydrogen-fueled bus fuel consumption levels, and strongly emphasized that passenger load
significantly affected bus fuel consumption levels. Specifically, higher passenger loads resulted
in higher fuel consumption levels for identical driving conditions; however, they did not consider
hybrid-electricity buses.
To the authors’ knowledge, the electricity hybridization benefit in bus fuel consumption has
not been extensively studied. Consequently, the objective of this study is to extend the VT-CPFM
framework to model hybrid bus fuel consumption levels in order to quantify the improvement
in fuel efficiency associated with hybrid-electric technologies, and to identify the impacts of the
associated influencing factors on the performance of the improvement, specifically focusing on
the impacts of bus curb weight, passenger load and bus behavior (idling, cruising and stop-and-go
events).
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0.01
0.009
Fuel Consumption Rate(l/s)
0.008
0.007
0.006
0.005
0.004
0.003
0.002
0.001
0
−800
−600
−400
−200
0
Vehicle Power(kW)
200
400
600
FIGURE 1 Vehicle power vs. hybrid bus fuel consumption functional form
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THE VT-CPFM FRAMEWORK
The VT-CPFM framework models the fuel consumption as a second-order polynomial function of
vehicle power. Although the model has been proven to be capable of accurately capturing LDV and
CDB fuel consumption levels (18), it has not been extended to model HEBs. The data in FIGURE
1, gathered for an HEB under real-world driving conditions, indicate that the fuel functional form
can approximately be characterized by a parabola for positive power conditions and remains nearly
constant for negative conditions, which is similar to LDVs and CDBs. Consequently, the VTCPFM is applicable to modeling hybrid bus fuel consumption, whereas the accuracy of the model
estimates remains to be studied in this paper.
VT-CPFM model structure
Rakha et al. (16) developed two VT-CPFM model formulations (VT-CPFM-1 and VT-CPFM-2),
only the VT-CPFM-1 was applied in this study due to a lack of engine gear data that would be
required to run the VT-CPFM-2 model. The framework of the VT-CPFM-1 is presented in Eq.(1):
(
α0 + α1 P (t) + α2 P (t)2 , ∀P (t) ≥ 0
F CR(t) =
α0 ,
∀P (t) < 0
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(1)
where F CR(t) is the instantaneous fuel consumption rate [l/s]; α0 , α1 and α2 are the vehiclespecific model coefficients that remain to be calibrated. P (t) is the vehicle power (kW), which is
computed as (19):
P (t) =
R(t) + (1 + λ + 0.0025ξv(t)2 )ma(t)
v(t)
3600η
(2)
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where R(t) is the vehicle resistance force [N]; λ is the mass factor accounting for rotational masses,
a value of 0.1 is used for heavy duty vehicles (HDVs) (20); ξ is assumed to be zero due to the lack
of gear data; m is the total mass of the bus [kg] (including bus curb weight and passenger load);
a(t) is the instantaneous acceleration [m/s2 ] and v(t) is the vehicle speed in the units of km/h; η
is the driveline efficiency. R(t) is the sum of the aerodynamic, rolling and grade resistance forces
as expressed by Eq.(3) (21), where ρa is the air density at sea level at a temperature of 15 ◦ C (59
◦
F) (equal to 1.2256 kg/m3 ), CD is the drag coefficient (unitless), the correction factor for altitude
(unitless) is calculated by 1 − 0.085H (H is in km), Af is the frontal area of buses (m2 ), Cr , c1
and c2 are the rolling resistance parameters (unitless), G(t) is the instantaneous road grade which
is determined by elevation profiles.
R(t) =
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Cr
ρa
CD Ch Af v(t)2 + 9.8066m
(c1 v(t) + c2 ) + 9.8066mG(t)
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1000
Model Calibration Discussion
According to Rakha et al. (16), the model can be calibrated using publicly available data (e.g. fuel
economy for standard drive cycles, engine displacement, drag coefficient), which enables the calibration work to be cost-effective and time-efficient. For LDVs, the vehicle-specific coefficients α0 ,
α1 , and α2 can be estimated using Eq.(4)-(6), where Pf mp is the idling fuel mean pressure (400,000
Pa); d is the engine displacement (liters); HE is the fuel lower heating value (43,000,000 J/kg for
gasoline fuel, not available for the buses in this paper); N is the number of engine cylinders; ωidle
is the engine idling speed (rpm); Fcity and Fhwy (liters) are the fuel consumed for the EPA city
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2
are the sums of the power and power squared
, Phwy , Phwy
and highway drive cycles; Pcity , Pcity
over the EPA city- and highway- cycle respectively; Tcity and Thwy are the duration of EPA city and
highway drive cycles (s); F Ecity and F Ehwy are the fuel economy estimates for the EPA city and
highway cycles (km/l). The use of ε term ensures that the fuel consumption is not affine to vehicle
power. For LDVs, the value of ε is 1E − 06 (16), whereas for hybrid buses the value remains to be
determined.
α0 =
Pf mp ωidle d
22164(HE)N
(4)
P
α2 =
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P
city
city
) − (Tcity − Thwy Phwy
)α0
(Fcity − Fhwy Phwy
P
city
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Pcity
− Phwy
Phwy
(5)
2
Fhwy − Thwy α0 − Phwy
α2
(6)
Phwy
However, hybrid buses do not report the fuel economy for standard drive cycles (e.g. the
EPA highway and city drive cycles), which implies that Fcity and Fhwy are not available. This lack
of fuel economy data for buses makes results in the need to gather real-world data for calibration
purposes. The calibration effort was performed using the General Linear Regression (GLR) analysis since the fuel consumption functional form is not affine as illustrated in FIGURE 1. Although
the publicly available data were not been used in this paper, the introduction of Eqs.(4)-(6) gives
some insights into calibrating the hybrid bus fuel consumption model if/once bus fuel economy
data are reported in the future.
α1 =
143
(3)
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DATA PREPARATION AND MODEL TESTING
Data Collection and Preparation
The field data were collected by test driving the buses around the town of Blacksburg, VA. The
test was conducted on two road sections: US 460 business (highway with a speed limit of 65
mi/h (104 km/h) and local streets with a speed limit ranging between 25 mi/h and 45 mi/h (40-72
km/h) in order to cover a wide range of real-world driving conditions. A total of 8 hybrid buses
were tested under similar ambient temperature conditions to minimize the impact of other external
factors on the data. A HEM data logger was used for data acquisition due to its portability and
the capability of collecting data autonomously without any maintenance. The data were saved on
a micro SD card and uploaded to a server via WiFi. Up to 46 parameters were collected, six of
which were extracted for the proposed study, namely: time stamp, vehicle speed, fuel consumption
rate, latitude, longitude, and altitude. The data were recorded at a frequency of 2 Hz or 5 Hz.
In order to generate second-by-second data, the original data were combined by averaging the
values of the data points within each individual second. The null data, which had a value of "0"
for longitude/latitude/altitude, were removed. For each bus, the cleaned data were separated into
three sub-datasets: 50% of the data points were set for calibration purposes, 25% of which used
for validation purposes; and for the remaining 25%, the data collected on a specific route were
extracted and set aside to compare the fuel consumption levels between HEBs and CDBs.
TABLE 1 gives a generalization of the model input parameters along with their sources.
Some parameters, such as the rolling resistance coefficients and driveline efficiency were obtained
from the literature; while some of the parameters are computed using the field data. The total mass
(m) is the sum of the bus curb weight and passenger load which is computed as the dot product of
the ridership and the average weight of individual passengers. It should be noted that 179 lb (81.5
kg) was assumed to be the average passenger weight in this study. Road grade was computed using
Eq. (7):
Elv(t + ∆t) − Elv(t)
G(t) = p
(D(t + ∆t) − D(t))2 − (Elv(t + ∆t) − Elv(t))2
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(7)
with Elv(·) the elevation over the time span [t, t + ∆t] and D(·) the distance a bus travels in a
single time step ∆t (1s in this case). Given that the measured elevation data were not accurate,
high accuracy Lidar data were used to compute the grades by superimposing the location data on
a Geographic Information System (GIS) database.
Model Calibration
A total of eight HEBs were tested and classified into two bus series (601X series and 602X series)
based on the bus-specific properties, including: model year, manufacturer, size, engine model,
horsepower and curb weight. The buses within the same series have identical vehicle properties.
The buses of the 602X series are much heavier, with a curb weight of 20,845 kg (45,860 lb)
and a larger bus capacity (107 passengers) compared to the 601X series which has a curb weight
of 14,154 kg (31,140 lb) and a capacity of 80 passengers. As demonstrated in (18), the model
calibrated for an entire bus series can accurately capture the fuel consumption behavior of each
individual bus within that series. Consequently, as part of this effort the VT-CPFM model was
calibrated for each entire bus series instead of individual buses.
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Parameter
TABLE 1 Parameters required for model calibration
Value
Source
Drag coefficient (CD )
Altitude correction factor (Ch )
Bus frontal area (Af )
Vehicle speed (v)
Mass (m)
Rolling coefficient (Cr )
c1
c2
Road grade (G)
Acceleration (a)
Driveline efficiency (η)
Bus Series No.
601X series
602X series
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0.8
/
6.824 m2
/
/
1.25
0.0328
4.575
/
/
0.95
(21)
Computed from field data
Computed from bus dimensions
Measured in field
Bus weighed
(21)
(21)
(21)
Computed from field data
Computed from field data
(21)
TABLE 2 The calibrated model coefficients
α0
α1
1.493e-03
1.321e-03
3.829e-05
6.651e-05
α2
-1.284e-08
-3.821e-08
TABLE 2 summarizes the calibration results by providing the values of the parameter sets.
Interestingly, the second-order coefficients (α2 ) are negative, which suggests that the bus fuel consumption functional form is concave. This is similar to CDBs but unlike LDVs whose functional
is convex. Furthermore, the magnitude of the α2 parameter is greater than 1E − 08, implying that
a minimum value of 1E − 08 may ensure that the hybrid bus optimum fuel economy cruise speed
is in a typical range which is to be determined in section 4.3.2. Consequently, the value of ε term
for HEBs is equal to 1E − 08 which is identical to CDBs but different from LDVs (1E − 06).
Model Validation
The models were tested at the instantaneous fuel consumption level and the optimum cruise speed
level, as described in this section.
Instantaneous Fuel Consumption Rates
The fuel consumption estimates were compared to the measurements at an instantaneous level in
order to quantify the goodness of the model fit. FIGURE 2 demonstrates that the model estimates
are generally consistent with the field measurements. Even when the models overestimate or underestimate the field measurements, in general the predicted fuel consumption rates follow the
peaks and valleys of the measured data. Statistically, the coefficient of determination (R2 ) is 0.61
for the 601X series and 0.70 for the 602X series, demonstrating the power of VT-CPFM to can
explain a substantial portion of the variability in hybrid bus fuel consumption data.
Optimum Cruise Speed
The relationship between the fuel consumption rate per unit distance and the cruising speed is
bowl shaped, as illustrated in FIGURE 3. The optimum fuel economy cruise speed is the cruise
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−3
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601X Series
x 10
Measurements
Estimated
Fuel Consumption Rate(l/s)
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2
1
0
0
200
400
600
800
Time(s)
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1200
1400
1600
(a) 601X Series
602X Series
0.012
Measurements
Estimated
Fuel Consumption Rate(l/s)
0.01
0.008
0.006
0.004
0.002
0
0
200
400
600
800
Time(s)
1000
1200
1400
(b) 602X Series
FIGURE 2 Estimates vs. measurements at the fuel consumption level
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601X Series
602X Series
Fuel Consumption(l/km)
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3
2
1
0
0
20
40
60
Cruise Speed(km/h)
80
100
FIGURE 3 Fuel consumption per unit distance vs. cruise speed
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speed for which the fuel consumption rate is minimum. The study demonstrated that the hybrid
bus distance-based fuel consumption bowl shaped curve is very similar to that of regular buses
(18), producing the minimum fuel consumption level at a speed of 50 km/h (601X series) and 49
km/h (602X series), respectively. In addition, the 602X series generates higher fuel consumption
levels than the 601X series. This is attributed to the fact that the 602X series buses have a higher
bus curb weight.
HYBRID ELECTRIC AND CONVENTIONAL BUS COMPARISON
To quantify the potential fuel efficiency benefits of hybrid technology, the fuel consumption levels
were estimated for both HEBs and CDBs using the calibrated VT-CPFM models. It should be
noted that the CDB fuel consumption models were calibrated in an earlier study (18). In order to
eliminate differences in vehicle trajectory and topographical conditions, the same vehicle trajectories were applied to both models. A pairwise comparison was conducted between the 19XX series
(conventional diesel) and 601X series (hybrid), and 632X series (conventional diesel) and 602X
series (hybrid). Since the buses within each pair of series have an identical capacity and bus curb
weight, the only confounding factor is the propulsion technology.
As illustrated in FIGURE 4, the 601X series / 602X series always produce lower fuel consumption levels compared to the 19XX series / 632X series, demonstrating that the fuel efficiency
could be improved through the use of hybrid technologies. Furthermore, the fuel consumption
levels for the 632X- and 602X- series are higher than those of the 19XX- and 601X- series. This
finding empirically demonstrates that the heavier buses may produce higher fuel consumption levels. FIGURE 4 also indicates that the fuel consumption levels increase monotonically with the
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−3
5
The Impact of Passenger Load on Fuel Consumption
x 10
19XX Series
601X Series
632X Series
602X Series
Average Fuel Consumption(l)
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4
3.5
3
2.5
0
10
20
30
40
50
60
70
Passenger Load Percentage (%)
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90
100
FIGURE 4 The impact of passenger load on fuel consumption levels
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passenger load for each bus series, and the differences in the fuel consumption levels between
hybrid- and conventional- bus series diminish with the passenger load increases. This implies that
higher passenger loads may reduce the hybrid-induced fuel benefits.
To quantify the fuel benefits, the relative differences in fuel consumption rates were computed, as illustrated in FIGURE 5. The positive differences reflect lower fuel consumption levels
for hybrid buses and thus achieve fuel savings. In summary, the savings range between 17% and
26% for the lower curb weight buses (601X Series vs. 19XX Series shown in FIGURE 5a), and between 10% and 13% for the heavier pair (602X Series vs. 632X Series shown in FIGURE 5b). This
quantitatively demonstrates that higher bus curb weight significantly reduces the hybrid-induced
fuel efficiency benefits. Furthermore, a decrease in fuel savings is observed with an increase in
the number of passengers. This indicates that passenger load is also a major trigger that impacts
hybrid-induced fuel savings, which empirically verifies the conclusion obtained in FIGURE 4.
In order to identify the optimum events to achieve fuel savings, the dataset was partitioned
into three portions: the first sub-dataset included only idling data; the second one included cruising
data; and the third captured stop-and-go behavior. The relative differences in fuel consumption
levels were compared between the HEBs and CDBs for each of the three datasets, respectively
in order to identify which type of bus behavior resulted in more fuel savings. As illustrated in
FIGURE 6, the positive differences reflect lower hybrid bus fuel consumption levels. In general,
the lower curb weight HEB achieves a fuel saving of 7% in idling events, 15%-28% in cruising
events and 21%-28% in stop-and-go events, respectively; and the heavier HEB realizes a savings
of 4% in idling, 7%-14% in cruising and 10%-14% for stop-and-go events, respectively. The
results reveal that the hybrid-induced fuel savings achieved in cruise and stop-and-go events are
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Bus 601X vs. 19XX Series
Bus 602X vs. 632X Series
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Differences in Fuel Consumption (%)
Differences in Fuel Consumption (%)
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20
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10
16
14
12
10
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6
4
5
2
0
−0.2
0
0.2
0.4
0.6
0.8
Percentage of Passenger Load
(a) 601X Series vs. 19XX Series
1
1.2
0
−0.2
0
0.2
0.4
0.6
0.8
Percentage of Passenger Load
1
1.2
(b) 602X Series vs. 632X Series
FIGURE 5 Improvement in fuel consumption for HEBs
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significantly higher than those while idling; and that heavier HEBs achieve lower fuel savings
compared to the lighter HEBs. Consequently, the bus curb weight is a key factor in affecting the
fuel efficiency benefits of HEBs. It should be noted that the idling fuel savings are impacted less by
the increase in the bus curb weight and passenger load demonstrating that the idling fuel rate is not
significantly affected by the vehicle mass (22). However, the fuel savings achieved in cruise and
stop-and-go operations have a downtrend when the passenger load increases. Consequently, high
passenger loads reduce the hybrid-induced fuel benefits achieved in cruise and stop-and-go events.
Furthermore, the stop-and-go operation produces the highest level of fuel savings compared to the
other two activities, especially for high passenger loads. This may be attributed to the frequent
stop-and-go behavior, which allows the regenerative system to recover more energy lost while
braking operation and stores energy in the form of electricity for standby applications.
CONCLUSIONS
Traditional HEB fuel consumption models produce a bang-bang type of control system. To overcome this shortcoming, the VT-CPFM framework, which models the fuel consumption as a secondorder polynomial function of vehicle power, is used to develop an HEB fuel consumption model.
The model is calibrated for two bus series, and the model estimates are validated by comparing
to empirical data. The validation effort demonstrates that the model estimates are consistent with
in-field measurements. The optimum fuel economy cruise speed is found to be in the range of 50
km/h which is lower than LDV optimum cruise speeds (60-80 km/h).
The calibrated models are used to compare the fuel estimates of HEBs with those of CDBs
and quantify the hybrid-induced fuel benefits. The results reveal that the hybrid buses produce
lower fuel consumption levels overall. Heavier buses may result in less fuel benefits, demonstrating
that the bus curb weight significantly affects the fuel economy benefits resulting from hybridization
technologies. Furthermore, the passenger load is also demonstrated to be a trigger that affects
the hybrid-induced fuel efficiency benefit. The fuel savings achieved by cruise and stop-and-go
operations are significantly higher compared to idling activity, and this fuel benefit will be eroded
with the increase of passenger load. The stop-and-go behavior produces the highest level of fuel
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(a) 601X Series vs. 19XX Series
(b) 602X Series vs. 632X Series
FIGURE 6 Fuel savings triggered by idling, cruise and stop-and-go behavior
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savings since more energy could be recovered by the regenerative system embedded in HEBs
during bus braking operation.
This study provides a simple, accurate and efficient model to predict hybrid bus fuel consumption levels. The results of the pairwise comparison may support bus planning efforts in developing fuel-efficient strategies.
ACKNOWLEDGEMENTS
This research effort was sponsored by Blacksburg Transit and the TranLIVE University Transportation Center. The authors would also like to thank all personnel who assisted with data collection
and processing.
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