Measured Laboratory and In-Use Fuel Economy 2012-01-2049

Published Observed over Targeted Drive Cycles for 09/24/2012 

Comparable Hybrid and Conventional Package 

Delivery Vehicles 

Michael P. Lammert, Kevin Walkowicz, Adam Duran and Petr Sindler 

National Renewable Energy Laboratory 

ABSTRACT 

This research project compares the in-use and laboratoryderived fuel economy of a medium-duty hybrid electric 

drivetrain with “engine off at idle” capability to a 

conventional drivetrain in a typical commercial package 

delivery application. Vehicles in this study included eleven 

model year 2010 Freightliner P100H hybrids that were placed 

in service at a United Parcel Service (UPS) facility in 

Minneapolis, Minn., during the first half of 2010. These 

hybrid vehicles were evaluated for 18 months against eleven 

model year 2010 Freightliner P100D diesels that were placed 

in service at the same facility a couple months after the 

hybrids. Both vehicle study groups use the same model year 

2009 Cummins ISB 200 HP engine. The vehicles of interest 

were chosen by comparing the average daily mileage of the 

hybrid group to that of a similar size and usage diesel group. 

The driving characteristics of the two study groups were 

examined in detail by collecting and analyzing two periods of 

Global Positioning System and controller area network 

(CAN) bus data. The results of this drive cycle study 

indicated a need to have the two investigated groups switch 

route assignments mid-study because of significantly 

different driving patterns, which UPS accommodated, to 

facilitate an accurate comparison. 

The in-use fuel economy was evaluated using UPS's fueling 

and mileage records, periodic electronic control module 

image downloads, and J1939 CAN bus recordings during the 

two periods of duty cycle study. The drive cycle analysis was 

used to select three standardized laboratory drive cycles that 

would encompass the range of real world in-use data. The 

NYC Composite cycle, the HTUF Class 4 cycle, and the 

CARB HHDDT cycle were tested at the National Renewable 

doi:10.4271/2012-01-2049 

Energy Laboratory's Renewable Fuels and Lubricants 

Laboratory. The hybrid vehicle demonstrated 39%, 45%, and 

21% improvement in ton-mi./gal fuel economy on these test 

cycles, respectively. Analysis of the in-use fuel economy 

ranged from 13% to 29% hybrid advantage depending on 

measurement method and the associated delivery route 

assignment switch analysis showed 13% to 26% hybrid 

advantage on the less kinetically intense original diesel route 

assignments and 20% to 33% hybrid advantage on the more 

kinetically intense original hybrid route assignments. 

INTRODUCTION 

Parallel hybrid drivetrains are an important petroleum 

reduction strategy for fleets to employ. These drivetrains 

consist of a traction battery and an electric motor integrated 

into a traditional powertrain. These systems offer the 

potential to save fuel by limiting the transient operation of the 

engine and by recapturing energy as the vehicle slows down 

through regenerative braking. 

Because hybrid technology is still relatively new, particularly 

to the medium duty vehicle segment, and because fuel 

savings from hybrids are highly dependent on the duty cycle 

they are driven on, there are still questions to be answered 

about when and where this technology offers a valuable 

return on investment in the form of fuel savings. 

The objective of this project was to evaluate the actual fuel 

economy under a range of well-documented duty cycles in 

the field and in the laboratory. 

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BACKGROUND AND METHODS 

United Parcel Service (UPS) placed eleven new hybrids into 

service in Minneapolis in April 2010 as part of a purchase of 

200 new hybrid step delivery vans. Driver training on hybrid 

operation was implemented at the same time. Conventional 

vans of the same model were placed in service in 

Minneapolis from April thru June 2010. The National 

Renewable Energy Laboratory (NREL) evaluated the first 

generation of these UPS/Eaton hybrids in Phoenix, Ariz., 

from January 2008 thru December 2010 [1,2]. The secondgeneration hybrids studied in Minneapolis include an “engine 

off at idle” function and meet 2007 emissions standards, 

among other improvements that warranted further study of 

this evolving technology. 

Vehicle Selection and Details 

All eleven hybrid P100 vehicles in service in Minneapolis 

were included in the study. Eleven vehicles from the more 

extensive conventional fleet of new P100s were chosen to be 

included in the study by comparing UPS route data from the 

routes the new hybrids and conventional vehicles were being 

assigned to. These route data were collected by UPS from 

older vehicles assigned to these routes. Conventional vehicles 

were chosen from available route assignments that matched 

most closely to the statistics available for the hybrid route 

assignments. However, the hybrids had been assigned to the 

most dense, lower average speed, and higher stops per mile 

routes, and it was suspected that a route mismatch would be 

inevitable and would require a mid-study route switch 

between the study groups. Table 1 lists the vehicles' 

specifications, and Table 2 lists pertinent hybrid system 

details. 

Table 1. Study Van Details. 

Table 2. Hybrid System Details.
	

Duty-Cycle Analysis and Test Cycle 

Selection 

GPS and J1939 Vehicle Data Logging 

Isaac Instruments DRU900/908 data logging devices with 5 

Hz Global Positioning System (GPS) antennas and J1939 

CAN bus connections were deployed to the UPS fleet on two 

occasions. The first was from July 14, 2010, to July 29, 2010, 

and the second was from April 12, 2011, to June 21, 2011. 

On the first occasion only three Isaacs were available (other 

GPS-only devices were also deployed but are not included in 

this report), and on the second occasion seven vans from each 

group were instrumented with the devices for most of the 

duration. In total, 338 vehicle days of hybrid operation and 

252 vehicle days of conventional operation on eight vans 

from each group were documented. The GPS and J1939 

channels were recording at a 5-Hz rate. J1939 CAN bus 

channels included wheel-based vehicle speed, engine speed, 

and engine fuel rate among others (see Appendix Table A1 

for a complete list). These same devices and channel settings 

(minus GPS) were used during laboratory dynamometer 

testing to capture vehicle systems activity during the test 

runs. 

DRIVETM Analysis 

Filtration and analysis of the in-use field data collected as 

part of the study were performed using NREL's Drive-Cycle 

Rapid Investigation, Visualization, and Evaluation 

(DRIVETM) analysis tool [3,4]. Employing NREL's DRIVE 

analysis tool, researchers were able to ensure data quality by 

analyzing daily vehicle operation via a list of approximately 

150 unique drive cycle metrics. The 150 drive cycle metrics 

calculated as part of the analysis ranged from high level route 

descriptors such as average driving speed (mph) and stops per 

mile, down to vehicle energy level metrics such as kinetic 

power density consumed (W/kg) and kinetic intensity, all of 

which were calculated using different variations of the 

fundamental road load equation [5]. When performing the 

road load calculations, it was assumed, due to a lack of 

2
reliable elevation data, that the vehicle differential elevation 

component was negligible as were the effects of road grade. 

Lab Test Cycle Selection 

When selecting standard test cycles for lab testing purposes, a 

multivariate least squares method was employed in an effort 

to select standard cycles most reflective of the aggregate 

group in-use data. By performing a comparison of drive cycle 

metrics such as average driving speed, stops per mile, and 

others, a highly representative set of test cycles was chosen 

representing driving conditions displaying the least, average, 

and greatest hybrid advantage. The corresponding cycles 

chosen in order of least to greatest advantage were the 

California Air Resources Board Heavy Heavy-Duty Diesel 

Truck (HHDDT), HTUF4, and New York City (NYC) Comp. 

Laboratory Chassis Dynamometer 

Testing Procedures 

When tested, the vehicle is secured to the dynamometer with 

the drive axle(s) over the rollers. The vehicle is exercised by 

a driver following a prescribed speed trace on the test aid 

monitor. A large fan is typically used to force cooling air onto 

the vehicle radiator to roughly simulate the ram cooling effect 

of a vehicle in motion. The engine exhaust stream is collected 

by the emissions measurement system for analysis, and 

various vehicle parameters are monitored and logged by the 

data acquisition system. 

To assure the accuracy and consistency of road load 

simulation, the dynamometer is subjected to various 

procedures and checks. From a practical perspective, the 

daily testing routine consists of the following steps. In the 

morning, the vehicle is lifted off the rollers and the 

dynamometer is subjected to its warm-up procedure until the 

parasitic losses stabilize. Then the unloaded coastdown 

procedure is used to verify that the parasitic losses did not 

change from the previous testing due to component failure 

and that the load cell calibration has not drifted. Following 

this verification, the vehicle is dropped on the rollers and 

driven for roughly 20 minutes to warm up. After the warmup, a conditioning test run is performed to stabilize the 

vehicle's temperature for a given test cycle. At this point, the 

system is ready to either set or verify the correct road load 

simulation through a loaded coastdown procedure. The 

following test runs are considered usable in terms of data 

validity provided the road load simulation proves consistent. 

This is verified after each test to ensure that changing 

conditions (test facility temperature) are not affecting vehicle 

loading. To maximize consistency, the soak period between 

one test end and the following test start is kept at 20 minutes. 

Emissions Measurement 

The emissions measurement system is based on the 

recommendations in Code of Federal Regulations Section 40, 

Part 86, Subpart N. The system consists of a full flow dilution 

tunnel with a constant volume sampling system for mass flow 

measurement. The tunnel flow rate is measured and 

controlled using critical flow venturis. The dilution and 

engine combustion air is supplied by an air handling unit that 

maintains the desired air temperature and humidity. 

The diluted engine exhaust was sampled for gravimetric 

particulate matter analysis and by a Horiba MEXA 7100 

series system for gaseous analysis, including total 

hydrocarbons, oxides of nitrogen, carbon monoxide (CO), 

and carbon dioxide. The gas analytical system was verified 

prior to beginning the testing period, including linearization 

checks and oxides of nitrogen efficiency test. On a daily 

basis, the analyzers were zero and span calibrated, and each 

test was bracketed by zero and span response readings used 

for corrections. The emissions measurement data were then 

reduced to distance specific mass results using the Code of 

Federal Regulations-recommended calculations, including 

humidity, dry to wet, zero, span, and background corrections. 

Fuel Consumption Measurement 

The fuel consumption measurement in this project relied 

primarily on a gravimetric approach. The engine fuel supply 

and return lines were connected to a fuel container placed on 

a scale. The scale mass measurements were recorded in a real 

time along with all the test data. The difference between the 

beginning and the end test mass measurement indicated the 

mass of fuel consumed during the test. Prior to testing, the 

scale calibration was verified with a known calibration 

weight. A Sartorius Midrics MAPP1U-60ED-L was used for 

this test. The fuel consumption measurement was also backed 

up using the carbon balance method back-calculating the 

mass of fuel consumed from measurement of exhaust 

emission constituents. 

State-of-Charge Considerations 

SAE Recommended Practice J2711 is a recommended 

protocol for measuring fuel economy and emissions of 

hybrid-electric and conventional heavy-duty vehicles and was 

used for this project. The recommended practice describes a 

state-of-charge correction for charge-sustaining hybrid 

electric vehicles. All the tests in this program involving the 

hybrid electric vehicle resulted in negligible net energy 

changes and thus did not require correction as per SAE 

J2711. 

In-Use Vehicle Fueling Records 

Collection and Statistical Analysis 

UPS provided electronic records of daily mileage and fueling 

event records for analysis for each vehicle studied in the 

field. Monthly fuel economy results were tabulated from 

these records for each van. Due to inconsistencies in fueling 

records that resulted in some unattainable monthly fuel 

economy results, a statistical method known as Chauvenet's 

3
Criterion [4] was used to identify van-monthly mile-pergallon (mpg) results that were statistical outliers. 

Fueling Record Outlier Analysis via 

Chauvenet's Criterion 

Chauvenet's Criterion was applied to the data set to identify 

individual monthly mpg outliers within the overall group of 

fueling records. Chauvenet's Criterion is an iterative 

statistical method applied to data sets that either accepts or 

rejects individual statistical outliers found in experimental 

data based on the data set size, mean, and standard deviation 

[6]. As part of employing Chauvenet's Criterion, the 

probability of the largest outlier in the data set is determined 

using the data set mean and standard deviation. If the 

probability of the observed outlier multiplied by the size of 

the data set results in a value less than 0.5, it is concluded that 

the statistical outlier observed as part of the data set can be 

rejected based on the limited probability of this outlier 

occurring as part of the data set. If the outlier is rejected, the 

data set mean and standard deviation are recalculated 

excluding the rejected data point as part of the data set, and 

the probability for the next largest remaining outlier is 

determined. This process is repeated until no further outliers 

are found to be rejected. From the 198 vehicle-months of 

recorded fuel economy data on each vehicle group, this 

method removed 22 vehicle-months from the diesel van 

group and 48 vehicle months from the hybrid van group 

(seven had the value of divided by zero gallons of fuel) that 

were statistically impossible based on the characteristics of 

the 13-month first route assignment data set and the 5-month 

switched route assignment data set. In addition to employing 

Chauvenet's Criterion to remove statistically impossible data 

points, an additional 95% confidence interval analysis was 

performed on the remaining outliers not rejected via the 

Chauvenet's Criterion approach to remove an additional four 

vehicle-months from the diesel group. The 95% confidence 

interval was chosen to ensure that the data analyzed as part of 

the study reflected typical vehicle behavior and would not be 

influenced by the effect of low-probability events. A visual 

inspection of the data upon the conclusion of this process 

revealed that most of the data points removed by this 

approach were obvious data artifacts impossible for 

individual vehicles to attain (see Appendix Table A2). 

However, by employing a consistent statistical approach to 

the entire fueling data set, the grey areas for each vehicle 

were treated in the same manner and either accepted or 

rejected, resulting in a realistic and consistent data set. 

Additionally, one hybrid van had to be removed from fueling 

records analysis for the duration of the first route assignment 

because the driver consistently did not report fueling events. 

RESULTS 

Delivery Van in Use Duty Cycle Results 

Comparing the routes driven by the two vehicle groups is 

difficult because of the disparity in the average daily miles 

driven. The conventional vans averaged 64 miles a day while 

the hybrids averaged only 43 miles a day due to their route 

assignments. Figure 1 shows the average distance (as a 

percentage) that vans with GPS loggers drove at different 

vehicle speeds. The hybrids drove a greater percentage of 

their distance at slower speeds than the diesels did, and the 

diesels drove more of their miles operating at higher speeds. 

• The hybrid vans drove 44% of their miles below 20 mph, 

while the conventional vans drove only 30.5% of their miles 

at those slow speeds. 

• Both van groups drove a similar percentage of their miles at 

the intermediate speeds of 20-50 mph: 47% for conventional 

vans and 43% for hybrid vans. 

• The hybrid vans drove only 13% of their miles above 50 

mph, while the conventional vans drove 22% of their miles at 

those highway speeds. 

The greater percent of miles driven by the hybrids at slower 

speeds is an indication of a more urban duty cycle. The lower 

percentage of miles driven at highway speeds is an indication 

of routes closer to the depot. 

Figure 1. Hybrid and diesel duty cycle breakdown by 

miles (%) 

Figure 2 shows the average distance (in miles) that vans with 

GPS loggers drove at different vehicle speeds. This distancebased chart highlights a different breakdown of the routes. 

• The conventional vans drove more miles at all speeds above 

10 mph. 

• The hybrid and conventional vans both drove 17 miles in 

the 0-20 mph range. 

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• The hybrid vans drove 37% fewer miles in the 20-50 mph 

range: 19 vs. 30. This indicates the conventional vans had 

longer surface street drives between delivery stops. 

• The hybrid vans drove 62% fewer miles in the 50+ mph 

highway range: 6 vs. 15. This indicates the conventional vans 

had longer highway sections to get to their assigned delivery 

areas. 

These figures and statistics also show a very clear picture of a 

bi-modal duty cycle. We can separate out the “leg” or 

highway section from the depot to the delivery area from the 

delivery routes zone itself. The conventional vans were on 

routes with a longer leg portion, which leaves less time to be 

in the delivery zone. They also tended to have less dense 

delivery zones. The hybrids, on the other hand, had a shorter 

leg from the depot to the delivery zone and had a denser 

delivery zone, resulting in a high percentage of low-speed, 

slow mile accumulation driving. 

Figure 2. Hybrid and diesel duty cycle breakdown by 

miles traveled 

Table 3 presents other duty-cycle statistics gathered from the 

GPS data logging, which may be summarized as follows. 

• The hybrids' average driving speed of 16.5 mph was 21% 

lower than the conventional vans' 20.7 mph. 

• The hybrids averaged roughly the same number of stops per 

day as the conventional vans' (205 vs. 220 for the diesels). 

• The hybrids had 5.3 stops per mile, 37% more than the 

conventional vans' 3.9. 

• The hybrids had 25.7 acceleration events per mile, 34% 

more than the conventional vans' 19.2. 

These statistics indicate that the hybrids were operating on 

very different routes than the diesels. Because of these major 

differences, the study groups switched routes in the summer 

of 2011 (June - August). As of August 2011 the hybrid vans 

had assumed the drive characteristics of the conventional 

group and the conventional vans had assumed the drive 

characteristics of the hybrid group. The results of the route 

switch are discussed below. Hybrid fuel economy advantage 

is compared for the groups on the same routes as well as over 

the full study period. 

Delivery Van Fuel Economy 

Laboratory Testing Gravimetric and J1939 

Reported Fueling Comparison 

All laboratory fuel economy results are averaged from four 

test runs of each cycle. Gravimetric fuel economy results for 

the vans are shown in Table 4. The hybrid vans showed a 

13%-36% improvement in fuel economy over the 

conventional vans on the tested duty cycles. 

Fuel economy results from recording of the J1939 reported 

fuel rate for the vans are shown in Table 5. The hybrid vans 

showed a 16%-43% improvement in fuel economy over the 

conventional vans on the tested duty cycles using this 

method. 

Data logger measured and calculated fuel economies were 

consistently 6% better on the conventional van and 11% 

better on the hybrid van as compared to the laboratory 

gravimetric measurements of those test runs. It is supposed 

that J1939 fuel rate reporting is not at a high enough 

resolution to accurately calculate fuel economy in-use 

because the error was solely in fuel consumed, not in miles 

traveled during the test. However, the run by run repeatability 

of the offset percentage was such that a correction factor can 

be applied to achieve more accurate in-field analysis with 

data logging of this channel. Applying 6% and 11% 

reductions, respectively, to each laboratory test run resulted 

in much more acceptable error values (less than 2%); 

therefore, this adjustment is applied later to the in-field data 

collected by the same data logging device, J1939 channel and 

analysis method to better represent the real-world data. Table 

6 shows the J1939 fuel economy results using this correction 

of individual test runs. 

Ton fuel economy results (based on gravimetric fuel 

consumption measurement and tested vehicle weight) for the 

vans are shown in Table 7. The hybrid vans showed a 

21%-45% improvement in fuel economy over the 

conventional vans on the tested duty cycles. 

5
Table 3. Drive Cycle Statistics from Vans with GPS Loggers from Each Study Group
	

Table 4. Fuel Economy (gravimetric) of Hybrid and Diesel Van on Various Cycles on Chassis Dynamometer.
	

Table 5. Fuel Economy from J1939 data logger of Hybrid and Diesel Van on Various Cycles on Chassis Dynamometer.
	

6
Table 6. Corrected Fuel Economy from J1939 Data Loggers of Hybrid and Diesel Vans on Various Cycles on Chassis
	

Dynamometer.
	

Table 7. Ton Fuel Economy (gravimetric) of Hybrid and
	

Diesel Van on Various Cycles on Chassis Dynamometer.
	

UPS Fueling and Mileage Records Analysis 

Using the Chauvenet's Criterion and 95% confidence interval 

analysis described above, 26 diesel-group vehicle months of 

fuel economy data and 48 hybrid-group vehicle months of 

fuel economy data were removed from the total of 198 

vehicle months of fuel economy results presented in this 

report. The resulting fuel economy comparison results are 

shown in Table 8. The lower than expected 13% 

improvement is driven primarily by the route assignment 

mismatch; for about 13 of the 18 months the conventional 

group was on a higher speed duty cycle, accumulating many 

more miles at a higher mpg than they would have had they 

been on the same routes as the hybrids. 

Table 8. Fuel Economy (in-use records) of Hybrid and
	

Diesel Vans.
	

Because the duty cycle analysis revealed that the study 

groups were operating on significantly different duty cycles, 

a group route switch was arranged with UPS so that all 

hybrid vehicles took on a route previously operated by a 

conventional study group van and the conventional study 

group vans were assigned to the hybrid routes. This enabled a 

comparison of the study groups on the same routes, but 

during different time periods. The whole 18 months is not 

represented, rather the same 5-month calendar period is 

compared to eliminate any seasonal variation in weather and 

loads. Table 9 shows the fuel economy of the study groups on 

the original conventional group routes and the original hybrid 

group routes. “Conventional Route 1” indicates the original 

routes the conventional vans were assigned to and the hybrids 

took over after the route switch. “Conventional Route 2” 

indicates the original routes the hybrids were assigned to and 

the conventional vans took over after the route switch. Both 

study groups had higher mpg on Conventional Route 1 than 

on Conventional Route 2, which validates the statistical 

analysis indicating that the conventional group was on a 

somewhat less demanding duty cycle while the hybrids were 

on a harder one. Also of note is that the hybrid advantage was 

13% on the less kinetically intense, more highway biased 

route assignments, matching well to the laboratory results on 

the HHDDT cycle while they achieved 20% hybrid advantage 

on the more kinetically intense Conventional Route 2 

assignments (although the second route comparison was not 

quite statistically significant because of removed fueling 

months eliminating some vans, which reduced the sample 

set). 

Table 9. Route Switch Fuel Economy (in-use records) of
	

Hybrid and Diesel Van on two route sets.
	

7
Periodic ECM Image Download Analysis 

ECM “image snapshots” were collected from each van nine 

times between December 7, 2010, and January 18, 2012. 

These images report numerous counters within the ECM, 

including how many miles have been driven and how much 

fuel has been consumed as well as breaking down those 

values into categories (idle fuel, top gear fuel, fuel used for 

DPF regenerations, top gear miles, etc.). By comparing these 

images with those taken previously, it is possible to calculate 

fuel economy over a given period of time for each van. The 

accuracy of these ECM fueling records over time has not 

been evaluated, and it is not known if the inaccuracy 

demonstrated during the J1939 CAN bus recording of the 

laboratory test was due to reporting frequency or ECM 

accuracy. As such, the correction factors discussed above 

have not been applied to these results. Table 10 shows the 

miles driven, gallons consumed, and fuel economy for each 

of the study vehicles. The hybrid group demonstrated 29% 

better fuel economy than the conventional group over the 

whole time covered by these image comparisons, which 

includes both route assignments in a nearly equal time split, 

although seasonal differences are not balanced due to the 

later start of ECM image downloading as compared to the 

beginning of the study. 

Table 10. Fuel Economy (ECM records) of Hybrid and
	

Diesel Vans.
	

Periods before and after the route switch were also evaluated 

so the groups could again be compared on the same routes 

albeit at different time periods. Data on the original routes 

came from the period of December 7, 2010, to June 24, 2011, 

and the second route data are from the period of August 16, 

2011, until January 18, 2012. Table 11 shows the group fuel 

economy results for each group on each route assignment. 

The same trends seen in the fueling records analysis of the 

route switch are seen in the ECM data. Both study groups had 

higher mpg on Conventional Route 1 than Conventional 

Route 2, and the hybrid advantage was greater on 

Conventional Route 2 than Conventional Route 1. The hybrid 

group demonstrated 26% better fuel economy on the original 

conventional routes and 33% better fuel economy on the 

more kinetically intense original hybrid routes. 

Table 11. Fuel Economy (ECM Records) of Hybrid and
	

Diesel Vans.
	

GPS/J1939 Duty Cycle Study Daily Fuel 

Economy 

The same Isaac data logging device used during the 

laboratory testing was used during two periods to capture inuse fleet fuel economy. Seven vans from each study group 

were instrumented for most of the second period. In total, 338 

days of hybrid operation and 252 days of conventional 

operation were documented. The fuel economy calculations 

from these in-use days of J1939 and GPS recording are 

assumed to be affected by the same offsets seen in the 

laboratory tests and as such these data have been corrected 

using the factors discussed above. Table 12 shows the miles 

driven, fuel consumed, and average fuel economy from the 

study vehicles during this time. 

Table 12. Fuel Economy (Data Logging W/Correction
	

Factor) of Hybrid and Diesel Vans.
	

However, because detailed driving behavior is also known for 

each in-use driving day, comparisons of fuel economy to 

kinetic intensity, average driven speed, and stops per mile are 

possible. Figure 3 shows individual days of operation, vehicle 

averages, and the laboratory dynamometer results presented 

8
earlier compared to the average driven speed of the drive 

cycle. Figure 4 shows the same data compared to kinetic 

intensity. There is clear indication that the laboratory results 

bracketed the in-use operation of the study groups and 

accurately represented the trends for the two vehicle groups, 

while the in-use daily data points clearly show the random 

effects of varying drivers, loads, traffic, and weather that are 

not captured in laboratory testing and that tend to reduce fuel 

economy as compared to laboratory results. Also of note is 

how the hybrid days of operation clump at the lower driven 

speeds and higher kinetic intensity while the conventional 

days of operation are spread more evenly across the range. 

Figure 3. Laboratory and in-use fuel economy compared
	

to average driven speed.
	

Figure 4. Laboratory and in-use fuel economy compared 

to kinetic intensity. 

Discussion 

The variety of fuel consumption measurement methods 

employed in this study all have their positive attributes and 

negative attributes. Laboratory dynamometer testing has 

definitive measurement of fuel consumed and miles traveled 

and shows what a vehicle is capable of under very specific 

circumstances. However, laboratory results are always going 

to miss some real in-use variables that can have significant 

effect on fuel economy such as idle time, changing load, 

weather, traffic conditions and driver aggressiveness. While 

some fleets utilize wireless recording of refueling events from 

vehicles, most, like this location, require drivers to take note 

of fueling events. This means the records are open to human 

error, which was demonstrated in the number of months that 

needed to be removed because of unattainable fuel economy 

numbers. The engine ECM keeps track of its commanded 

fueling and has the advantage of assigning that fuel to 

specific operations such as idle, cruise control, DPF 

regeneration etc. However, the error associated with these 

histories is unknown and likely to vary by engine model and 

possibly even within the engine RPM/load map of the engine. 

It also has the drawback of requiring periodic downloads 

from the study vehicles, which often are irregular because of 

logistics. Tabulating the engine fuel rate reported on the 

J1939 CAN bus takes any engine ECM error and adds error 

on top of that with possible issues of update rate and 

rounding/decimal clipping by the J1939. It has the advantage, 

though, of capturing true in-use conditions and all variability 

of those operations and through post processing fueling can 

be attributed to highway speeds, slower road speeds, and 

“idle” or zero vehicle speed operation. By utilizing this 

method both in the laboratory and in-use, a rough error 

correction can be calculated and applied. It should be noted 

that while the different methods varied somewhat in specific 

mpg numbers and in the resulting percent improvement from 

the hybrid drivetrain, all fuel economy measurement methods 

demonstrated very strong statistical significance through use 

of a t-test showing that the two study groups were indeed 

delivering different fuel economy results. 

SUMMARY/CONCLUSIONS 

By all methods of measure, the hybrids consistently are 

demonstrating a fuel economy advantage, vehicle by vehicle, 

month by month, day by day. Laboratory testing, long-term 

in-use analysis over a route switch, and daily calculated 

fueling all demonstrate a distinct relationship between duty 

cycle and the magnitude of demonstrated hybrid fuel 

economy advantage. The laboratory tests fully encompassed 

the range of fuel economy improvement found by the three 

in-use methods of measure of 13% to 36%. 

• Fueling records analysis seemed the most error-prone with 

roughly 20% of the data having to be statistically removed. 

• Corrected GPS/J1939 and ECM analysis showed 26%-33% 

in-use fuel economy improvement. 

• The majority of daily GPS route statistics placed most 

driving between the laboratory test cycles of NYC Comp and 

HTUF4, which showed 29% and 36% improvement, 

9
respectively, which matches well with the in-use data from 

J1939 and ECM images. 

REFERENCES 

1. Lammert, M., “Twelve-Month Evaluation of UPS Diesel 

Hybrid Electric Delivery Vans,” NREL/TP-540-44134, 

December 2009. 

2. Lammert, M. and Walkowicz, K., “Thirty-Six Month 

Evaluation of UPS Diesel Hybrid-Electric Delivery Vans,” 

NREL/TP-5400- 53503, March 2012. 

3. NREL Vehicle Drive Cycle Tool, User Guide. © 2009 

Alliance for Sustainable Energy, LLC. All Rights Reserved. 

4. Duran, A. and Earleywine, M., “GPS Data Filtration 

Method for Drive Cycle Analysis Applications,” SAE 

Technical Paper 2012-01-0743, 2012, doi: 

10.4271/2012-01-0743. 

5. O'Keefe, M., Simpson, A., Kelly, K., and Pedersen, D., 

“Duty Cycle Characterization and Evaluation Towards Heavy 

Hybrid Vehicle Applications,” SAE Technical Paper 

2007-01-0302, 2007, doi:10.4271/2007-01-0302. 

6. Taylor, J.R., An Introduction to Error Analysis. 2nd 

edition. Sausalito, California: University Science Books, 

1997. pp 166-168. 

CONTACT INFORMATION 

michael.lammert@nrel.gov 

1617 Cole Blvd. 

Golden, CO 80401 

(303) 275-4067 

adam.duran@nrel.gov 

1617 Cole Blvd. 

Golden, CO 80401 

(303) 275-4586 

petr.sindler@nrel.gov 

1617 Cole Blvd. 

Golden, CO 80401 

(303) 275-3142 

kevin.walkowicz@nrel.gov 

1617 Cole Blvd. 

Golden, CO 80401 

(303) 275-4492 

ACKNOWLEDGMENTS 

This work was supported by the U.S. Department of Energy 

(DOE) Advanced Vehicle Testing Activity and would not 

have been possible without the generous donation of time and 

vehicles from the United Parcel Service of America, Inc. The 

authors wish to thank Lee Slezak at DOE, and Mike Britt, 

James Francis, Andy Grzelak, Bill Brentar and Robert Hall at 

UPS for their support. 

DEFINITIONS/ABBREVIATIONS 

CAN - controller area network 

DPF - diesel particulate filter 

DRIVE - Drive-Cycle Rapid Investigation, Visualization, 

and Evaluation 

ECM - electronic control module 

GPS - Global Positioning System 

HHDDT - Heavy Heavy-Duty Diesel Truck 

HTUF - Hybrid Truck Users Forum 

NREL - National Renewable Energy Laboratory 

NYC Comp - New York City Comp 

ReFUEL - Renewable Fuels and Lubricants 

UPS - United Parcel Service 

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APPENDIX
	

Table A1. J1939 Data Logging Channel List.
	

Table A2. Monthly Vehicle Fuel Economy Results from UPS Records (raw with statistically removed months in gray and
	

remaining months from one driver removed in darker gray).
	

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