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Chapter 4: Vehicle Simulation Model

4.1 Purpose and Scope 

In designing a regulatory GHG emission control and fuel consumption program, it is 

necessary to estimate the performance of technologies, verify compliance with the regulatory

standards, and estimate overall benefits of the program. The agencies developed the Greenhouse 

gas Emission Model (GEM) to serve these purposes for Phase 1, which was consistent with 

recommendations by the National Academies of Sciences (NAS) to use vehicle simulation to 

demonstrate compliance.

A GEM is currently being used to certify the fuel consumption and CO2

benefits of the Phase 1 rulemaking for all heavy duty vehicles except for HD pickups and vans, 

which require a chassis dynamometer test for certification. While the version of GEM used in 

Phase 1 contained most of the technical and mathematical features needed to run a vehicle 

simulation, the model was limited. For example:

 Only manual transmissions were used in the model for all tractor and vocational vehicle 

simulations, which is not always the case for real world applications, especially for 

vocational vehicle applications

 The model did not include engine torque interruption during gear shifting

 Engine control were simplified, with no fueling cut-off features

 Only the agencies’ pre-specified engine fuel maps were used

The Phase 1 certification process only required up to five user inputs, and all other 

vehicle parameters and their inputs were pre-specified by the agencies.

1 Phase 1 GEM only 

recognized the benefits of aerodynamics improvement, tire rolling resistance, vehicle speed 

limiter, weight reduction, and idle reduction (only for high roof sleeper tractors).

Because the proposed Phase 2 standards are predicated on the performance of a broader 

range of technological improvements than Phase 1, including changes to transmissions and better 

integration of engines and transmissions, a more comprehensive vehicle simulation model is 

required. This chapter describes a new version of this vehicle simulation model, referred to as 

Phase 2 GEM. It should be noted that all changes to GEM described in this chapter remain 

potential, since the agency is proposing these changes, and will make a final determination as to 

what changes are appropriate only after considering the entire record after the close of the public 

comment period.

 

A National Academies of Science. “Technologies and Approaches to Reducing the Fuel Consumption of Mediumand Heavy-Duty Vehicles.” 2010. Recommendation 8-4.
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4.2 Model Code Description 

4.2.1 Engineering Foundations of the Model

EPA developed GEM to be a forward-looking Matlab /Simulink-based model for heavyduty (Class 2b-8) vehicle compliance in 2011.

1

 A more detailed description of this model and its 

engineering foundation can be found in Reference 1. The underlying GEM code was originally

developed to simulate a broad range of vehicle speeds over essentially any in-use duty cycle. 

However, the official version that is used for determining compliance with the Phase 1 standards 

incorporates the regulatory duty cycles into the code. In other words, manufacturers cannot run 

other duty cycles with the official version of GEM. We propose to continue this approach for 

Phase 2.

In order to meet proposed Phase 2 rulemaking requirements in recognizing most of the 

technologies that are measured in both engine and chassis dynamometers, GEM has been

considerably enhanced. Specifically, the agencies are proposing to implement the following key

technical features into Phase 2 GEM:

• An upgraded engine controller, which includes engine fuel cut-off during braking and 

deceleration

• An upgraded transmission model, which includes an upgraded manual transmission, 

along with newly developed automatic and automated manual transmissions

• An upgraded driver model with a distance-compensated driver that will drive the 

certification drive trace over a prescribed distance regardless of increased drive time due 

to vehicle under-performance, for example.

4.2.2 Model Components

The GEM architecture is comprised of four systems: Ambient, Driver, Powertrain, and 

Vehicle as seen in Figure 4-1. With the exception of Ambient and Driver, each system consists 

of one or more subcomponents. The function of each system and its respective component 

models, wherever applicable, is discussed in this chapter. Many changes and modifications 

described in this chapter have resulted from numerous constructive comments from both public 

comments and GEM peer reviews.2The model has been upgraded to improve the fidelity of the 

model and better match the function of the simulated vehicles, which also meets our primary

goal to accurately reflect changes in technology for compliance purposes. As part of this effort, 

substantial effort has been put forth to accurately track and audit power flows through the model 

to ensure conservation of energy.
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Figure 4-1 GEM model structure

4.2.2.1 Ambient Subsystem

This system defines ambient conditions such as pressure, temperature, and road gradient, 

where vehicle operations are simulated. Just like in Phase 1 GEM, the conditions have been 

maintained in accordance with standard SAE practices. The road gradient has been modified to 

accept a road grade that varies as a function of distance traveled. 

4.2.2.2 Driver Subsystem

The driver model in Phase 2 GEM has been substantially reorganized to simplify

operation and to add support for distance compensated drive cycles. The result is a purely

proportional-integral control driver that features a small lookahead to anticipate the drive cycle, 

especially useful at launch where the vehicle response may be delayed due to the large effective 

inertia in low gears. The proposed target drive cycle consists of a road grade versus distance and 

a vehicle speed target as a function of the time required to achieve those speeds as a function of 

distance (i.e. desired cycle time). The drive cycle speed can be converted to a target speed 
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versus distance travelled, however such a conversion involves the complication of tracking 

vehicle stop times separately since they necessarily occur over zero distance.

Because the simulation itself is time-based, we consider the driver to be distance 

compensated rather than distance-based. The driver always operates in the time domain. To 

implement the distance compensated driver, the cycle time is tracked separately from simulation 

time, based on the ability of the target vehicle to meet the target speed trace. If the vehicle meets 

the target speed trace then cycle time is equivalent to simulation time as there is no difference in 

the distance travelled. If the vehicle under-performs the drive cycle then cycle time proceeds 

more slowly than simulation time, forcing the vehicle to drive for a longer amount of time in 

order to cover an equivalent distance.

In terms of implementation, to apply distance compensation at each time step, the current 

model vehicle speed is divided by the target speed from the drive cycle. This value is integrated 

to produce the current cycle time and an updated speed target. The result is that if a simulated

vehicle is traveling at half the drive cycle speed the simulation will progress through the drive 

cycle at half the rate. This behavior is disabled at speeds below 1 meter per second to provide 

reasonable launch behavior (which necessarily occurs over short distances), division by zero and 

to maintain vehicle stop times independent of small discrepancies in total distance travelled.

The addition of distance compensation allows all simulated vehicles to complete an 

equivalent trip such as traveling from point A to point B. Without distance compensation, underpowered vehicles might complete the drive cycle by time but not distance and would have done 

less work than higher powered vehicles as measured in ton-miles. Distance compensation also 

allows for the variation in road grade to be kept in synchronization with the drive cycle speed 

trace.

4.2.2.3 Powertrain Subsystem

The engine, transmission, electric accessories, and portions of the vehicle models from

Phase 1 GEM have been upgraded and merged into a conventional vehicle powertrain system as 

shown in Figure 4-2. The conventional powertrain system contains sub-models representing the 

engine, transmission, electric accessories, and driveline. Only conventional powertrains are 

modeled in Phase 2 GEM, and no hybrid power systems are modeled and certified with GEM. 

Rather, hybrid powertrains would be certified through powertrain dynamometer tests as 

described in Chapter 3 of the draft RIA. 
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Figure 4-2 GEM Powertrain Model

4.2.2.3.1 Engine Subsystem

The engine model is based on a steady-state fuel map covering all engine speed and 

torque conditions with torque curves for wide open throttle (full load) and closed throttle (no 

load). The engine fuel map in Phase 2 is the input provided by users. The engine fuel map 

features three sets of data: engine speed, torque, and fueling rate at pre-specified engine speed 

and torque intervals. In-cylinder combustion processes are not modelled. The engine speed at a 

given point in the drive cycle is calculated from the physics of the downstream speeds. The 

quantity of torque required is calculated from the driver model accelerator demand, an idle speed 

governor, and requests from the transmission during shifts. The torque request is then limited by

the maximum torque curve. The engine torque and speed are used to interpolate a fuel rate from 

the fuel map. The engine model also includes a constant power loss to simulate mechanical 

accessories. Most vehicles run a number of accessories that are driven via mechanical power 

from the engine. Some of these accessories are necessary for the vehicle to run, like the engine

coolant pump or power steering, while others are only used occasionally and at the operator’s 

discretion, such as the air conditioning compressor. Some heavy-duty vehicles also use Power 

Take Off (PTO) to operate auxiliary equipment, such as refuse compactors or lift forks. These 

would also be modeled as a mechanical accessory. The mechanical accessory load is proposed 

to be fixed for all vehicles based on regulatory subcategory, as shown below in Table 4-6, Table 

4-7, and Table 4-8. The actual power consumed for this loss would differ for actual vehicle 

configurations, but the agencies do not propose to allow users to change this value in GEM. If a 

manufacturer uses a hybrid powertrain for power take-off devices, it may make use of the 

hybrid-PTO test procedure. See, 40 CFR 1037.525.
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4.2.2.3.2 Electric Subsystem

The electric subsystem is modeled as a constant power loss. The power consumed for 

this loss is based on the vehicle subcategory. It represents the power loss associated with the 

starter, electric energy system, alternator and the electrically driven accessories. The 

simplification has a negligible impact on the fuel consumption and CO2 emissions results. The 

power losses for different vehicles are shown in tables from Table 4-5 to Table 4-10.

4.2.2.3.3 Transmission Subsystem

The transmission subsystem features three different variants representing the three major 

types of transmissions that are currently in use in the heavy-duty sector, which are the 

transmission types on whose performance the various proposed standards are predicated. The 

variants are manual transmission (MT), automated manual transmission (AMT), and automatic 

transmission (AT) (planetary gear set with torque converter). The different transmission models 

are built from similar components, but each features a unique control algorithm matching 

behaviors observed during vehicle testing3.

4.2.2.3.3.1 Transmission Gear Selection

All of the transmission models use a dynamic shift algorithm to determine the operating 

gear over the cycle3. This employs a rule based approach utilizing the engine torque curve and 

fuel map to select gears that optimize efficient engine operation and provide a torque reserve as a 

traditional transmission calibration would.

4.2.2.3.3.2 Clutch

The clutch model in Phase 2 GEM replaces the simplified model found in Phase 1 GEM. 

The original clutch model had no transition between the fully engaged and fully disengaged 

states and provided no commensurate torque impulse to the driveline. In the new clutch model, 

engagement and disengagement occur over time, torque is conserved across the clutch and the 

inertial effects of accelerating and decelerating the upstream inertias are captured.

4.2.2.3.3.3 Gearbox

The gearbox model has also been substantially revised in Phase 2 GEM to provide more 

realistic operation. The gearbox contains gear ratios and efficiencies for each gear. Each gear 

also has spin (churning) loss torques that can vary by current gear number and input speed. 

GEM assumes higher efficiency for direct drive than any other gear for manual and automated 

manual transmissions. Shifting behavior is more realistic than in Phase 1 GEM with appropriate 

delays provided by a synchronizer clutch model. This layout is most similar to a manual 

transmission, but the application for a planetary gearbox is a reasonable approximation as this 

type of gearbox can utilize a variety of topologies. A detailed description on the shifting strategy

can be seen in reference3. The gearbox rotational inertias are split between a common input 

inertia, common output inertia and a gear specific inertia. The common inertias represent 

rotational inertia always coupled to the input or output shafts. The gear specific inertias are

added or removed as gears are engaged or disengaged and incur additional losses.
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4.2.2.3.3.4 Hydrodynamic Torque Converter

The torque converter model in Phase 2 GEM simulates a lockup-type torque converter. 

The torque multiplication and resulting engine load are calculated via torque ratio and K-factor 

curves that vary as a function of speed ratio. A base torque ratio curve is used for all simulations 

and the K-factor curve is scaled based on the engine torque curve to provide a good match 

between the torque converter stall speed and the engine’s speed at maximum torque. This 

approximation could result in some simulation differences for highly specialized vehicles 

equipped with torque converters matched to their specialized duty cycle, but for the vast majority 

of vehicles, the effect of this approximation on simulated CO2 emissions is negligible. The 

lockup behavior of the torque converter is accomplished by integrating a clutch model similar to 

the one discussed in Chapter 4.2.2.3.3.2. The torque converter model also contains a pump loss 

torque that varies with input speed to simulate the power required to operate the pump on an 

automatic transmission.

4.2.2.3.3.5 Manual Transmission & Control

The manual transmission (MT) is composed of the clutch and gearbox systems discussed 

above. The gearbox spin losses for a particular simulation are scaled with the vehicle class. The 

manual transmission features minimal gear specific inertia. Control of the MT is accomplished 

via a low speed clutch engagement model that gets the vehicle moving by feathering the clutch 

during launch. Shifts are accomplished by reducing the requested engine load, disengaging the 

clutch, shifting the gearbox to the new gear and reengaging the clutch. In heavier vehicles 

shifting is accomplished by double-clutching to match transmission input speeds rather than 

relying purely on the gearbox synchronizers.

4.2.2.3.3.6 Automatic Transmission & Control

The automatic transmission (AT) is composed of the torque converter and gearbox 

systems discussed above. The gearbox gear specific inertias and spin loss torques are higher as 

would be expected from a conventional planetary automatic transmission gearbox. The AT is 

allowed to shift under load. During upshifts and torque converter lockup the engine output 

torque is slightly reduced to minimize the resultant torque pulse encountered by decelerating the 

engine inertia.

Torque converter lockup will be controlled by a predetermined lookup table or lockup 

strategy algorithm and at this time is not expected to be among the available user inputs with the 

possible exception of indicating the lowest gear in which lockup may occur.

4.2.2.3.3.7 Automated Manual Transmission & Control

The automated manual transmission (AMT) features the same clutch and gearbox models 

as the manual transmission with the addition of an inertia brake to slow the gearbox input inertia 

during upshifts. Control of the AMT during launch features a clutch feathering routine similar to 

the MT. Upshifts are handled by limiting the engine load, disengaging the clutch and shifting the 

gearbox to neutral. The inertia brake is then applied to slow the transmission input inertia before 

the gearbox engages the new gear. With the new gear engaged the clutch is reengaged and the 

engine is again allowed to operate at full load. Downshifts are handled by shifting the gearbox to 
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neutral and accelerating the gearbox input up to a speed matching the desired gear using the 

engine.

4.2.2.3.4 Driveline

The driveline system contains all of the components that convert the torque at the 

transmission output to force at the wheels. This includes drive shafts as well as driven axles, 

consisting of a differential, brakes and tires. Except as specified below, we are not proposing to 

change the Phase 1 GEM approach to driveline modeling. For example, both Phase 1 and Phase 

2 GEM can model all axles individually, or a single composite axle can be substituted to reduce 

simulation time.

4.2.2.3.4.1 Driveshaft

The driveshaft is a simple component for transferring torque while adding additional 

rotational inertia.

4.2.2.3.4.2 Final Drive

The final drive is modeled as a gear ratio change and an associated fixed efficiency. 

Various combinations of spin loss and efficiency, including a look-up table as a function of 

wheel speed and axle output torque, were considered. Table 4-1 below shows the comparisons 

between single value and look-up table efficiency approaches for a Class 6 box truck simulation.

Table 4-1 Axle Efficiency Modeling and Comparisons

 Axle Study Results (Box Truck)

 Fixed Efficiency v. Look-up Table

Fuel 

Economy (MPG)

Drive Cycle

Look-up Table

MPG

Fixed 95.5% 

Efficiency MPG Difference

55 mph 11.19 11.15 0.36%

65 mph 9.13 9.06 0.77%

CARB HHDDT 8.43 8.29 1.67%

In this table, 95.5 percent is a fixed efficiency, which is intended to be used as the GEM 

predefined value. As can be seen, the difference between these two approaches is fairly small, 

and the fuel economy with the fixed efficiency is more conservative (lower) than the look-up 

table approach as far as certifications are concerned. We are open to the approach of using a 

look-up table and request comment on its use. At this time, however, a single efficiency value 
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was selected to simplify simulation and model inputs. The final drive model also adds some 

rotational inertia to the system.

4.2.2.3.4.3 Brakes 

The brake system on each axle applies a torque to the axle proportional to the brake pedal 

position from the driver model. The scaling factor to determine this force is based on engine 

maximum torque, transmission multiplication and final drive ratio.

4.2.2.3.4.4 Tires

The tire component model transfers the torques and rotational inertias from upstream 

components to a force and equivalent mass that is passed to the vehicle model. This conversion 

uses the loaded tire radius and adds the tire’s rotational inertia. The force associated with the tire 

rolling resistance is also applied when the vehicle is moving. The magnitude of this force is 

determined by the coefficient of rolling resistance, vehicle static mass and current grade.

The proposed new version of GEM would make tire size a manufacturer-specified input 

rather than use a predefined value as was done for Phase 1. Manufacturers would specify tire 

size in terms of loaded radius or perhaps tire revolutions per mile. Other than this, tires are being 

modeled the same as in Phase 1.

4.2.2.4 Vehicle

The vehicle system consists of the chassis, its mass and forces associated with 

aerodynamic drag, rolling resistance, and changes in road grade. The aerodynamic force is 

calculated from the air density, vehicle speed, frontal area and drag coefficient. The vehicle 

system also contains the vehicle speed integrator that computes acceleration from the input force 

and equivalent mass which is integrated to generate vehicle speed and distance traveled.

4.2.3 Capability, Features, and Computer Resources

GEM is a flexible simulation platform that can model a wide variety of vehicles with 

conventional powertrains from Class 2b to Class 8. The key to this flexibility is the component 

description files that can be modified or adjusted to accommodate vehicle-specific information. 

Parameters such as vehicle weight, engine fuel map, transmission gear ratios, tire radius, or axle 

ratio can all be changed as inputs by the user in this fashion. The proposed Phase 2 GEM

predefines all drive cycles (the Transient mode, as defined by the California Air Resources 

Board (CARB) in their Highway Heavy-Duty Diesel Transient (HHDDT) cycle, and EPA GEM 

constant speed cycles at 65 mph and 55 mph, each with varying road grade). The agencies also 

pre-defined many key parameters, since those parameters are either hard to quantify due to lack 

of certified testing procedures or difficult to obtain due to proprietary barriers. Examples of 

these parameters include transmission shifting strategies, transmission gear mechanical 

efficiency, and transmission spin and pumping loss. The values selected for these parameters are 

a result of substantial testing found in the Southwest Research Institute Report4, as well as 

confidential discussions with engine, chassis and component manufacturers.
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During simulation the GEM tracks the status of many components and the status of all of 

the modeled losses. This information provides an energy audit to ensure the model conserves 

energy. The fuel consumed and vehicle speed traces are immediately available in the generated 

report, while the larger data set is available in a Matlab .mat file or a comma-separated values 

(CSV) file.

4.2.3.1 GEM Executable

The agencies propose to require that vehicle manufacturers use the Phase 2 GEM 

executable version, which does not require the use of Matlab or Simulink software, for 

demonstrating compliance with the proposed CO2 and fuel consumption standards. In this form, 

a precompiled executable format is used for certification. Its computational requirements are 

minimal. When using the minimum recommended 2 GHz processor and 4 GB of RAM, a single 

simulation should complete in 10 seconds and generate 100 MB output files. Inputs from the 

manufacturers are provided in a text file, and the results are available in a generated report.

4.2.3.2 GEM Matlab /Simulink Model

The Matlab/Simulink version of the GEM source code will be released for users that 

desire a more detailed look at the inner workings of the model. The system requirements for the 

Matlab /Simulink version of GEM include Matlab, Simulink and StateFlow software from 

Mathworks (version 2014a or later) and a compatible compiler.

5

 The recommended hardware 

for the Matlab release of GEM is 2+ GHz processor and 4 GB of RAM. The output data from a 

GEM simulation into Matlab is approximately 500 MB, depending on the simulation 

configuration and outputs selected. Simulations inside Matlab /Simulink using the source code 

take approximately 2 to 3 minutes. Although the source code is available to users, all of the 

component initialization files, control strategies and the underlying Matlab /Simulink/Stateflowbased models may not be used for determining compliance. Only the executable version can be 

used when producing official truck certification results. Also, it should be pointed out that EPA 

will not provide any technical support for the use of the GEM source code because it is beyond 

the scope of the agency’s responsibilities and resources.

4.2.4 Peer Review of Phase 2 GEM

The agencies conducted a peer review of Phase 2 GEM which has been submitted for 

public review in this NPRM. The peer review was conducted by an independent contractor and 

includes four reviewers. Additional details regarding the peer review and EPA’s responses to the 

peer review comments can be found in the docket2.

The agencies also met with and received comments from the Engine Manufacturers 

Association, along with other industry stakeholders, during the development of Phase 2 GEM, 

which identified some areas of concern with GEM. In response, the agencies made changes as 

necessary.
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4.3 Validation of Phase 2 GEM Simulations

This chapter presents the results of an engineering evaluation of the ability of the 

computer model in GEM to accurately simulate actual engine and vehicle performance. Note 

that this version differs from the compliance version in that it was possible to use actual values 

for vehicle parameters that are locked in the compliance version of GEM. For example, 

validations used actual vehicle curb weights. They also incorporated actual shift strategies where 

available. This is appropriate because the purpose of the validations was to evaluate the 

engineering basis of the model, rather than to evaluate whether the policy of locking certain 

parameters is appropriate. 

4.3.1 Experimental Tests for GEM Validation

Working with Southwest Research Institute (SwRI), EPA has invested significantly in

various truck tests in order to collect data to validate Phase 2 GEM. The technical research 

workshop held at SwRI, San Antonio, TX, December 10-11 details all of these tests6. The 

following truck tests were carried out by SwRI for the purpose of model validation:

• Class 6 Kenworth T270 vocational box truck with AT

• Class 6 Ford F-650 vocational tow truck with AT

• Class 8 Kenworth T700 line haul truck with AMT

• Class 8 New Flyer refuse truck with AT

The key specifications for those trucks are listed in Table 4-2.

Table 4-2 Vehicle Specifications of Heavy-Duty Trucks Tested at Southwest Research Institute 

Truck 2013 Kenworth 

T700

2012 Kenworth 

T270

2011 Ford

F-650 Tow truck

2012 New Flyer 

Refuse

Engine /Rated 

Power (hp)

Cummins ISX

455

Cummins ISB 

240

Cummins

ISB 270

Cummins

ISL 345

Transmission Eaton 

F016E310C-LAS

Allison

2100

Allison

2200 RDS

Eaton 

FO16E310C-LA

In order to fully validate the model, each truck was tested over six different driving 

cycles including regulatory cycles and non-regulatory cycles. They are the EPA GEM 55mph

(with and without grade), EPA GEM 65mph (with and without grade), the transient portion of 

the CARB Heavy-Duty Diesel Truck (HDDT) cycle, the World Harmonized Vehicle Cycle 

(WHVC), the High-efficiency Truck Users Forum (HTUF) Class 6 Parcel Delivery Cycle, and 

the National Renewable Energy Laboratory (NREL) Combined International Local and 

Commuter Cycle (CILCC) cycle (which is a utility vehicle cycle). The inclusion of driving 

cycles in addition to those used for Phase 1 certification was done to expand the range of 

operation. Some of the cycles are very aggressive (especially for Class 8 trucks), such as the 

CILCC and Parcel Delivery cycles, with many stops and rapid accelerations. EPA evaluated the 
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results from these additional cycles to improve the modeling capability and its response to highly 

transient conditions, thus providing additional confidence in model fidelity. All trucks were 

tested on a chassis dynamometer. In addition, the engine and transmission from the F-650 tow 

truck were tested in a powertrain dynamometer cell. More information on the vehicle chassis 

and powertrain dynamometer setups and tests can found in the Southwest Research Institute 

Report7.

Considering that procurement of trucks for model validations would be time consuming 

and expensive, EPA developed a comprehensive approach to quantify variants of vehicles in 

order to maximize testing efficiency. This was done by varying aerodynamic drag and tire 

rolling resistance, as well as using weights to simulate different trucks, affording coverage of a 

wide range of vehicles. For tractors, varying these parameters also reflects the effects of pulling 

different types of trailers, which would impact the combined drag, rolling resistance, and weight 

of the vehicle. In this sense, this simultaneously provides validation data for both tractors and 

trailers.

Three vehicles were selected for this portion of the test program and they are: the 

Kenworth T270 box truck, the Kenworth T700 truck with a 53 foot box trailer, and the F-650

tow truck. The first two trucks were tested on a chassis dynamometer, while the third one was 

tested on a powertrain system dynamometer. A total of six drive cycles were tested: EPA GEM 

55 mph, EPA GEM 65 mph, CARB HHDDT, WHVC, NREL CILCC, and HTUF Parcel 

Delivery cycle. An additional set of six tests were run for each driving cycle listed above to 

evaluate the impact of various vehicle characteristics on CO2 emissions and fuel efficiency. The 

characteristics of the six test modifications are listed below:

1. Adding 800 to 1,000 pounds to the vehicle’s tare weight depending on the vehicle class

2. Adding 15 percent to the vehicle-specific constant value representing the vehicle’s 

frictional load to simulate higher rolling resistance tires

3. Reducing the vehicle-specific constant value representing the vehicle’s frictional load by

15 percent to simulate lower rolling resistance tires

4. Increasing the vehicle-specific coefficient representing aerodynamic effects by 15 percent

to simulate a higher aerodynamic drag vehicle

5. Decreasing the vehicle-specific coefficient representing aerodynamic effects by 15

percent to simulate a lower aerodynamic drag vehicle

6. Running a new set of road load coefficients, to represent a vehicle configuration 

optimized for fuel efficiency for each vehicle that was tested, which consists of the 

lowest rolling resistance as well as the lowest aerodynamic drag coefficient

Three valid replicate tests were conducted for each vehicle and characteristic over each 

driving cycle. A valid replicate was defined as a successful test run in which all data was 

collected without regeneration of the diesel particulate filter. The following parameters were 

measured or recorded during all tests:
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 Vehicle speed as a function of time

 Engine fuel rate as a function of time

 Engine speed as a function of time

 Gear number as a function of time

 Engine load (Nm) as a function of time

 Emissions (NOX, HC, CO, CO2, N2O, CH4) as a function of time in g/s

 Measured cycle fuel economy (MPG) and emissions (NOX, HC, CO, CO2, PM, N2O, 

CH4)

 Grade as function of time for the cycle with road grade if tested

4.3.2 Results of the GEM Validations

Taking into account all of the vehicles and test configurations mentioned above, more than 

130 vehicle variants were tested, allowing GEM to be comprehensively validated against a very

well-defined and robust set of test data.

The results displayed in through Figure 4-3 through Figure 4-6 show the of comparison 

between the GEM simulations and testing data of the Class 8 Kenworth T700 truck, Class 6 Ford 

F-650 tow truck, Class 6 Kenworth T270 box truck, and New Flyer refuse truck respectively. In 

all figures shown here for 55 and 65mph cycles, road grade is not included. 
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Figure 4-3 GEM Validation against Class 8 Kenworth T700 Truck chassis tests

Figure 4-4 GEM validation against Class 6 Ford F-650 tow truck powertrain tests

2

3

4

5

6

7

8

9

10

11

2 4 6 8 10

GEM [MPG]

Chassis Dynamometer [MPG]

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC

1:1

1:1-5%

1:1+5%

6

7

8

9

10

11

12

13

14

15

16

6 8 10 12 14 16

GEM [MPG]

Powertrain Test [MPG]

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC

1:1-5%

1:1+5%

1:1
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Figure 4-5 GEM validation against Class 6 Kenworth T270 box truck chassis tests

Figure 4-6 GEM validation against New Flyer refuse truck chassis tests

5

6

7

8

9

10

11

12

13

14

5 7 9 11 13

GEM [MPG]

Chassis Dynamometer [MPG]

55 MPH

65 MPH

CARB

Parcel

Utility

WHVC

1:1

1:1-5%

1:1+5%

2

3

4

5

6

7

2 3 4 5 6 7

GEM [MPG]

Chassis Dynamometer [MPG]

55 MPH

65 MPH

CARB

Utility

WHVC

1:1

1:1-5%

1:1+5%
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A review of the data indicates that there is good agreement between the GEM simulations 

and testing data obtained over the wide range of vehicles and conditions. In general, the 

accuracy of the model simulations against the testing data is very well controlled with an error of 

less than ±5 percent, although there are a few outliers of the transient simulation cases for Class 

8 trucks due to the nature of the high variability of chassis dynamometer tests. The range of 

vehicles tested and simulated included vehicles that varied in terms of all of the proposed 

regulatory inputs. Thus, the agencies believe that the accuracy of GEM is sufficient to simulate 

the benefits of the range technologies that form the basis of the proposed standards.

Figure 4-7 shows the overall comparison between the simulation and test results when 

combining all of the testing and simulation into one figure. Overall, the simulation and test result 

correlate well.

Figure 4-7 Comparison of model simulations and chassis test results for the 130 vehicle test configurations 

While it is encouraging that GEM accurately simulates overall vehicle performance in an 

absolute sense, it is actually more important that GEM is accurate in relative comparisons. This 

is because the agencies used the same version of GEM to calculate the stringency of the 

proposed standards as was used to evaluate baseline performance for this rulemaking. The 

ultimate purpose of this new version of GEM will be to evaluate changes or additions in 

technology, and compliance is demonstrated on a relative basis to the numerical standards that 

were also derived from GEM. The importance of relative comparisons can be further explained 

with the following simplified example. 

2

4

6

8

10

12

14

16

2 4 6 8 10 12 14 16

GEM (M

P

G)

Experimental Tests (MPG)

1:1

1:1+5%

1:1-5%

Results
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Assume you have two simulation models: one that says a baseline vehicle with Bin 3 

aerodynamics and a conventional automatic transmission would be 90 g/ton-mile, and another 

that said the same vehicle would be 95 g/ton-mile. Assume also that there was a similar vehicle 

that was the basis of the new standards that had Bin 4 aerodynamics and a dual-clutch 

transmission. If both models simulated the second vehicle as being 10 g/ton-mile better than the 

baseline vehicle, then the models would work equally well for compliance as long as they were 

also used to set the standards. With the first model, we would set the standard at 80 g/tonmile. And with the second, we would set the standard at 85 g/ton-mile. In both cases, 

manufacturers adding Bin 4 aerodynamics and dual-clutch transmissions would meet the 

standard. In other words, the two models would be equivalent in terms of measuring the effect 

of the change in technology on emissions, even though the absolute values were significantly

different.

As is shown below, GEM indeed performs better in this relative sense. The results from 

the T700 and T270 trucks, and powertrain tests for the F-650 tow truck shown in Figure 4-3

through Figure 4-5, can also be presented in a format to evaluate GEM’s ability to measure the 

relative impact of a technology. Table 4-3 shows an example of relative comparisons to 

illustrate how the relative comparison is done with the T700 truck. For simplicity, only the 

results from the Class 8 T700 tractor on the 65mph cycle are shown in this table. The column 

labeled as Chassis Test Fuel Economy Result (MPG) shows the testing results, while the column 

with GEM Fuel Economy Result (MPG) shows the GEM simulation results. Each row 

represents a single change to the vehicle configuration, relative to the baseline case. The “Delta” 

in the last column is the difference between the impact of the vehicle configuration change as 

measured on the chassis dynamometer and simulated in GEM (which sometimes differs from the 

apparent delta due to rounding). For example, the row with the “+15 percent Crr” variable 

compares GEM results to chassis test results for a vehicle that is the same as the baseline vehicle 

except that it has tires with a coefficient of rolling resistance 15 percent higher than the baseline 

vehicle. For this example, chassis testing indicates the change in rolling resistance increases fuel 

consumption for this cycle by 3.9 percent, while GEM predicts it would increase by 4.9 percent, 

but the delta difference is only 1.0 percent as shown in the last column.

Table 4-3 Sample of Relative Comparisons for T700 Truck

Drive 

Cycle

Vehicle 

Attribute 

Variables

Chassis 

Test Fuel 

Economy 

Result 

(MPG)

GEM Fuel 

Economy 

Result 

(MPG)

Impact of 

Variable 

on Chassis 

Test Result

Impact of 

Variable on 

GEM 

Simulation 

Result Delta

65 mph Baseline 6.84 6.61 0.0% 0.0% 0.0%

65 mph +907 kg 6.86 6.55 -0.3% 0.9% -1.2%

65 mph +15% Crr 6.57 6.28 3.9% 4.9% -1.0%

65 mph -15% Crr 7.27 6.96 -6.3% -5.3% -1.0%

65 mph +15% Cd 6.31 6.05 7.7% 8.4% -0.7%

65 mph -15% Cd 7.63 7.25 -11.5% -9.8% -1.8%

65 mph

Optimized

Package 8.08 7.65 -18.1% -15.8% -2.3%
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The same methodology was applied to all other cases including three different trucks, and 

six driving cycles and six vehicle variables. The differences between the chassis test and GEM 

results from all of these comparisons are plotted in Figure 4-8.

Figure 4-8 Relative comparisons between tests and GEM results

In Figure 4-8, the horizontal axis represents the test number of each truck. It can be seen 

that the majority of cases have less than ±2-3 percent difference. Excellent correlation was 

obtained between the F-650 tow truck powertrain test data and GEM results, where all of the 

comparisons had an error less than ±2 percent. However, a few outliers with an error greater 

than 3 percent can be found in the Class 8 T700 and Class 6 T270 data. This is not unexpected 

since all the tests for both T700 and T270 trucks were conducted on the chassis dynamometer, 

while the tests for F-650 tow truck were conducted in the powertrain dynamometer cell8. The 

recent findings from the SwRI program sponsored by EPA show that chassis dynamometer tests 

have higher variability than powertrain tests, as discussed below9.

The driver behavior in the chassis dynamometer is one of the biggest contributors to the 

variability. This becomes even more an issue when a driver drives a very heavy vehicle like a 

Class 8 truck to follow a targeted vehicle speed trace in the chassis dynamometer cell, 

specifically for those highly transient cycles, such as CARB HHDDT, NREL CILCC, and HTUF 

Class 6 Parcel Delivery cycles. In contrast, a robot driver is used in the powertrain test for F-650

truck tests, thus removing this major source of variability. In addition, many other testing 

conditions, such as air temperature and coolant temperature, can be more stably controlled 

during powertrain than in chassis dynamometer tests. The findings also include many other 

sources of variability in the chassis dynamometer tests, such as tire temperature, thermal 

management during idle, and transmission oil temperature.9

,10 Because of the many 

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

0 10 20 30 40 50

R

elativ

e

Error b

etw

e

e

n

G

E

M

a

n

d

Test

T700

T270

F650
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4-19

uncertainties due to the variability of chassis dynamometer testing, it has been very challenging 

to match GEM results with chassis dynamometer test results in the same range of accuracy as the 

comparisons against the powertrain tests, specifically for those highly transient cycles. In some 

cases, it is hard to quantify which method, vehicle simulation or chassis dynamometer test, is 

more accurate. Therefore, considering the favorable comparison between the powertrain tests 

and GEM simulation results, it is fair to say that the overall accuracy of the GEM to represent the 

relative changes in fuel economy of a real world vehicle should be in the range of ±2-3 percent.

Figure 4-7 and Figure 4-8, respectively, show the GEM accuracy against over 130 

vehicle variants on an absolute and a relative basis. All are done in a total vehicle configuration, 

which includes all vehicle components, such as engine, transmission, and driveline. Since 

certification would be done in a total vehicle form for CO2 emissions and fuel efficiency, these 

types of comparisons are the most important because they demonstrate that GEM is capable of 

capturing the impact on the total vehicle CO2 emissions and fuel consumption due to technology

improvement of individual components. In order to show the fidelity of GEM in modeling 

individual components in a more detailed level, the comparisons for the key components must be 

demonstrated as well. Displayed in Figure 4-9 through Figure 4-11 are the comparisons of 

engine speed, fuel rate, and transmission gear numbers as function of time over the CARB 

HHDDT cycle for Class 8 T700 truck.

Figure 4-9 Engine speed comparisons over the WHVC for a Class 8 T700 truck

0 100 200 300 400 500 600 700

400

600

800

1000

1200

1400

1600

1800

2000

2200

Time (s)

Engine Speed (R

P

M)

GEM

Test Data
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4-20

Figure 4-10 Engine fuel rate comparisons over the WHVC for a Class 8 T700 truck

Figure 4-11 Transmission gear comparisons over the WHVC for a Class 8 T700 truck

0 100 200 300 400 500 600 700

0

5

10

15

20

25

Time (s)

Fuel Rate (g/s)

GEM

Test Data

0 100 200 300 400 500 600 700

0

1

2

3

4

5

6

7

8

9

10

Time (s)

Transmission Gear

GEM

Test Data
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As shown in Figure 4-9 through Figure 4-11, reasonably good comparisons between 

GEM simulations and tests are obtained. GEM basically can capture detailed behaviors of the 

engine and transmission. To further provide more complete picture on the GEM validations, 

Figure 4-12 and Figure 4-13 show another set of examples for an F-650 tow truck. Shown in 

these two figures are the comparisons of engine speed and transmission output shaft torque over 

the World Harmonized Vehicle Cycle (WHVC) between powertrain dynamometer tests and 

GEM results. As can be seen from these two figures, reasonable comparisons are again obtained 

between GEM and actual test results.

Figure 4-12 Engine speed comparisons over the WHVC for an F-650 tow truck 

0

500

1000

1500

2000

2500

0 500 1000 1500 2000

Engine Speed (RPM)

Time (s)

Test

GEM
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4-22

Figure 4-13 Transmission output torque comparisons over the WHVC for an F-650 tow truck 

4.4 EPA and NHTSA HD Vehicle Compliance Model

As described earlier, GEM is a computer model that simulates vehicle operation to 

predict CO2 emissions and fuel consumption for a wide variety of heavy-duty vehicles. This 

section describes how that computer model is used as a compliance tool to evaluate vehicle 

performance relative to the applicable standards. The engineering evaluation of GEM discussed 

in Chapter 4.3 was not limited by computing time and presumes all inputs to be accurate. 

However, using GEM as a compliance tool requires some simplification of the model. It also 

requires the elimination of user inputs that cannot be verified by the agencies. As discussed 

below, such simplifications are being proposed for Phase 2, but to a lesser degree than was done 

for Phase 1.

The Phase 2 GEM of EPA and NHTSA’s vehicle compliance simulation model is similar 

to Phase 1 GEM in many respects. However, it differs from the Phase 1 version in two major 

aspects. The first involves the significant improvements described in Chapter of 4.2.1. Second, 

Phase 2 GEM provides users the opportunity to enter additional vehicle and engine parameters

for the actual vehicle being simulated. As noted above, Phase 1 GEM only allows a maximum 

of five user defined inputs for tractors. These are: the aerodynamic drag coefficient, tire rolling 

resistance, vehicle speed limiter, weight reduction, and idle reduction. For vocational vehicles 

there is only one user defined input: tire rolling resistance. In contrast, the proposed Phase 2 

GEM allows the user to input many more engine and vehicle parameters, including most of those 

that have the biggest impact on emissions. In particular, it allows vehicle manufacturers to input 

their own engine fuel maps. Key driveline parameters, such as transmission gear number versus 

gear ratio, axle ratio, and tire rolling radius, are also part of the manufacturer inputs. 

-1000

-500

0

500

1000

1500

2000

0 500 1000 1500 2000

Transmission Ouput Torque (N

M)

Time (s)

Test

GEM
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There are still some GEM input parameters that are proposed to be pre-defined by the 

agencies. For some, such as shifting strategy, this is due to the fact that the parameters are hard 

to measure and quantify due to the lack of well-defined test procedures. For others, the 

manufacturers consider the parameter values to be proprietary and are reluctant to share the 

information with other parties. Examples of those items include the transmission gear shifting 

strategy table and gear mechanical efficiency. The modeling parameters associated with torque 

converters for automatic transmission would be also pre-defined by the agencies. The inertias of 

all rotational parts, vehicle weights and accessory power losses are also default parameters 

defined by the agencies. Finally, in order to have a consistent basis for the standards, the vehicle 

weights and payloads are predefined by vehicle class and duty cycle.

Table 4-6 and Table 4-7 list all of the proposed GEM input parameters for tractors and 

Table 4-8 through Table 4-10 list the predefined parameters for vocational vehicles. These 

tables also include weighting factors for each driving cycle for the determination of composite 

CO2 in g/ton-mile. 

It is important to note that, for many of these parameters, publicly available information 

on the values for current and future vehicles is limited. Manufacturers have provided values to 

the agencies, but have generally identified them as confidential business information. 

Nevertheless, we have used this information to inform our estimation of appropriate default 

values.

4.4.1 Predefined GEM Values

4.4.1.1 Transmissions

One of the major changes in Phase 2 GEM is to allow manufacturers to enter their 

transmission gear ratio versus gear number. When entering this information, manufacturers also 

have an option to select the type of transmission, which is either manual, automated manual or 

automatic with a torque converter. Mechanical efficiency for each gear is pre-defined by the 

agencies as shown in Table 4-5 through Table 4-10. Pre-specification was required due to the 

lack of a reliable, repeatable, and cost-effective test procedure.

One of the areas that required significant development work was the transmission shift 

strategy for use in the compliance tool. This was required because transmission suppliers have 

been reluctant to provide their shifting strategies to vehicle manufacturers for vehicle 

certification due to their concern over protecting intellectual property. The shifting strategy in 

the proposed Phase 2 GEM includes the agencies’ internally developed automatic shift

algorithm3. The impact of the use of the agencies’ default transmission shifting as opposed to 

using manufacturers’ shifting strategies has been evaluated and the results are presented in Table 

4-4.
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Table 4-4 Impact of the Agencies’ Default Shifting Strategy Compared to Transmission Manufacturers’ 

Strategies

Truck Cycle

Manufacturer

Shift Strategy 

Fuel Economy 

(MPG)

EPA Defined 

Shift Strategy 

Fuel Economy 

(MPG)

Difference (%)

T270 Box Truck GEM 55 mph 11.36 11.36 0

T270 Box Truck GEM 65 mph 9.24 9.24 0

T270 Box Truck CARB HHDDT 8.39 8.44 0.60

F-650 Tow Truck GEM 55 mph 14.49 14.57 0.55

F-650 Tow Truck GEM 65 mph 12.03 12.14 0.91

F-650 Tow Truck CARB HHDDT 10.32 10.72 3.88

T700 Class 8 Truck GEM 55 mph 7.96 7.96 0

T700 Class 8 Truck GEM 65 mph 6.59 6.59 0

T700 Class 8 Truck CARB HHDDT 3.9 3.94 1.02

The Manufacturer column in Table 4-4 represents the simulation results using the shifting 

tables and strategies provided by the transmission manufacturer, while the EPA column 

represents the results using EPA’s default shift algorithm. The transmission manufacturer and 

EPA fuel economy results are essentially the same despite the different shifting strategies. There 

is a noticeable difference in fuel economy results for the CARB HHDDT cycle, but it is still 

relatively small. It should be pointed out that in the case of 55 and 65 mph cruise speed cycles, 

there are few, if any, shifts.

Phase 2 GEM includes three types of transmissions as discussed in Chapter 4.2.2.3.3. 

They are manual transmissions (MT), automated manual transmissions (AMT) and automatic 

transmissions (AT). Due to lack of test data for other types of transmissions, GEM was not able 

to be validated in time against these three cases:

1. Dual clutch transmission (DCT)

2. Dual clutch transmission with a torque converter

3. Allison TC-10 automatic transmission

The agencies are proposing use of AMT to model case 1; use of AT to model case 2, and 

use of AT to model case 3. The manufacturers would still have the option to use powertrain 

dynamometer tests to quantify the benefits of these or any other special transmissions, rather 

than use the pre-defined values. The detailed test procedure of the powertrain dynamometer tests 

are described in Chapter 3 of the draft RIA

4.4.1.2 Axles

Axle ratios for all model sub-categories would be user defined. Default axle mechanical 

efficiency is pre-defined by the agencies. Based on comments that the agencies receive related 

to this NPRM, we may adopt provisions in the final rule that would allow manufacturers to 

override the default mechanical efficiency and input their own values; however, the inputs would 

be determined by using the prescribed test procedure described by Chapter 3.8 of the draft RIA. 
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4-25

Typical base Class 8 tractors have one steer and two drive axles, while typical base Class 

7 tractors have one steer and one drive axle. The trailer used for both Class 7 and Class 8 

tractors in simulation modeling has two axles. All HHD vocational vehicle categories have 3 

axles, while all others only have two.

4.4.1.3 Weights

It is assumed that the vehicle unloaded weight will vary by vehicle subcategory. Taking 

tractors as an example, the total weight ranges from 65,500 to 70,500 lbs, while for Class 7 

tractors weight ranges from 46,500 to 50,000 lbs. The payload capacity varies as shown in Table 

4-5 through Table 4-10. The development of these weights is discussed in Chapter 3 of the draft 

RIA.

4.4.1.4 Inertia

All of the inertias for rotational parts, including engine, transmission and axle, are predefined based on a combination of the agencies’ engineering judgment and confidential business 

information from OEMs. The default inertia values were used during GEM validation against 

respective trucks and they will be used as the default values for all of the vehicles certified using 

GEM. Thus, the vehicle OEM will not have flexibility to enter their own inertias. 

4.4.1.5 Accessory Load

The agencies are assuming that all trucks, including tractors and vocational vehicles, 

carry a constant electrical load as well as mechanical load when operated over the agencies’ 

certification drive cycles. Those agency derived values were used when GEM validations were 

carried out against experimentally derived data from SwRI. All of the default, pre-specified 

values are shown in Table 4-5 through Table 4-10, and would be used as the default values for 

all vehicle certification.

4.4.1.6 Tires

Tire radius is a user defined input; however, the agencies do provide default values for 

all vehicle sub-categories. Static loaded tire radius is used in GEM for all simulations for every

combination tractor and the default value can be overridden by the vehicle OEM. 

The trailer tire coefficient of rolling resistance (Crr, trailer tires) assumes a constant value

for all trailer tires. This value was developed through tire testing performed by the SmartWay

Transport Partnership.11 

4.4.1.7 Idle Cycle and Its Modeling

As described in Chapter 3.4.2 of this draft RIA, we are proposing the addition of an idleonly cycle to determine both fuel consumption and CO2 emissions when a vocational vehicle is 

idling, and to recognize technologies that either reduce the fuel consumption rate or shut the 

engine off (and restart) during short-term idle events during the workday. Based on user inputs, 

GEM would calculate CO2 emissions and fuel consumption at both zero torque (neutral idle) and 
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4-26

with torque set to Curb-Idle Transmission Torque (as defined in 40 CFR 1065.510(f)(4) for 

variable speed engines) for use in the CO2 emission calculation in 40 CFR 1037.510(b). We are 

also proposing that GEM would calculate reduced CO2 and fueling for stop-start systems, based 

on an assumption that the effectiveness would represent a 90 percent reduction of the emissions 

that would occur if the vehicle had operated at Curb-Idle Transmission Torque over this cycle. 

This cycle is proposed to be applicable for all subcategories of vocational vehicles (HHD, MHD 

and LHD) using any of the proposed composite duty cycles (Regional, Multi-Purpose, or Urban 

composite duty cycles). More can be seen in Chapter 3.4.2.3 about the idle cycle. Chapter 4.5

discusses how these idle technologies are modeled as part of technology improvements that are 

recognized in GEM.

4.4.1.8 Transient Adjustment Factor

As described in Chapter 2, fuel consumption during transient engine operation typically 

is higher than during steady-state operation. The difference can vary significantly, but the trend 

is generally consistent. Because the GEM simulation relies on steady-state fuel maps to predict 

emissions for all the cycles, including the transient cycle, the agencies are proposing to apply a 

transient adjustment to GEM results for the transient cycle.

4.4.1.8.1 Transient Engine Testing

To evaluate the need for a transient adjustment factor, we compared the results from 28 

individual engine dynamometer tests. Three different engines were used to generate this data, 

and these engines were produced by two different engine manufacturers. One engine was tested 

at three different power ratings (13 liters at 410, 450 & 475 hp) and the other engines ranged 

from medium heavy-duty (6.7 liters, 300 hp) to heavy heavy-duty (15 liters, 455 hp) service 

classes. For each engine and rating our proposed steady-state engine dynamometer test 

procedure was conducted to generate the data table to represent that particular engine in GEM. 

Next, GEM simulated various vehicles in which the engine could be installed. For each of the 

GEM duty cycles we are proposing, namely the urban local (CARB HHDDT), urban highway

with road grade (GEM 55 mph), and rural highway with road grade (GEM 65 mph) duty cycles, 

we determined the GEM result for each vehicle configuration, and we saved the engine output 

shaft speed and torque information that GEM utilized to interpolate the steady-state engine data 

table for each vehicle configuration We then had this same engine output shaft speed and torque 

information programmed into the engine dynamometer controller, and we had each engine 

perform the same duty cycles that GEM demanded of the simulated version of the engine. We 

then compared the GEM interpolated results to the measured engine dynamometer results. We 

concluded that for the 55 mph and 65 mph duty cycles, GEM’s interpolation of the steady-state 

data tables was sufficiently accurate versus the measured results. This is reasonable because 

even with changes in road grade, the 55 mph and 65 mph duty cycles do not demand rapid 

changes in engine speed or load. They are nearly steady-state, just like the data tables 

themselves. However, for the CARB HHDDT cycle, we observed a consistent bias, where GEM 

consistently under-predicted fuel consumption and CO2 emissions. This low bias over the 28 

engine tests ranged from 4.2 percent low to 7.8 percent low. When we aggregated these results 

by engine, the results were between 5 and 6 percent. We understand that use of an engine 

dynamometer test with GEM inputs of torque and speed to quantify the impact of using a steady 

state engine fuel map would not be perfect, since this approach would not consider the 
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4-27

interaction of the driver model of GEM in response to individual vehicle component dynamics. 

Also, the input torque and speed values calculated in GEM and input into the engine 

dynamometer are only targeted values and they are not the real measured engine torque and 

speed. Since the engine may not be able to follow the targeted speed trace, there may be some

discrepancy compared to the vehicle performance obtained from GEM. Accessory loads 

between the engine test and GEM simulation are also different. In spite of these differences, it is 

a common fact that steady state operation is different from transient operation, specifically in a 

diesel engine.

The most significant difference between steady state and transient behavior is the smoke 

control during acceleration. Diesel engines must limit the fueling in order to prevent smoke 

during rapid acceleration. In contrast, there is no such issue during steady state mapping. 

Furthermore, all modern diesel engines use a fueling cut-off technique to shut off the fueling 

during deceleration based on a manufacturer-defined set of conditions. In addition, thermal 

management is another major factor to that may create a difference between steady state and 

transient fueling. The aftertreatment system is very sensitive to exhaust temperature in order to 

maintain optimal performance of selective catalytic reduction devices. Post fueling must be 

injected into the exhaust stream once the exhaust temperature is below certain criteria, typically 

in the range of 200 degrees Celsius. In steady state fueling mapping, the engine always follows

the defined testing procedure; thus, the engine runs hotter even at light loads than in a transient 

condition at the same speed and torque, thus thermal management may not even kick in. 

Because of these differences, engine manufacturers typically include at least two distinguished 

engine calibrations into the engine control unit – one for typically on-highway operation, and the 

other for transient operation, such as urban and mountainous areas.

We are confident that this low bias in GEM results using a steady state fuel map would 

continue to exist well into the future if we were to test additional engines. However, with the 

range of the results that we have generated so far we are somewhat less confident in proposing a 

single numerical value to correct for this bias over the CARB HHDDT drive cycle. The 

procedure used to derive this proposed correction adjustment factor, although reasonable, may

still need more refinement. 

4.4.1.8.2 Proposed Value for the Transient Adjustment Factor

Based on the limited testing that was performed for this analysis, the agencies are 

proposing a transient adjustment factor of 1.05. This means that the simulated transient cycle 

GEM results (that are generated based on steady-state fuel maps) would be multiplied by 1.05 

before being output from the GEM compliance tool. The higher output value would be the 

official GEM result.

This 1.05 factor reflects the engine at the lower end of the range for the three engines, 

with the other two engines indicating values of 1.06 or 1.07 might be appropriate. The agencies 

are proposing the lower value because we do not want to allow manufacturers to gain an 

advantage from powertrain testing without making some improvement to the powertrain. The 

test results indicate that an adjustment of 1.07 would allow the ISX engine (which showed a 

lower transient difference) to show a 2 percent improvement without making any improvements. 

The agencies recognize that there is significant uncertainty in the proposed value and will 
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4-28

continue to evaluate the adjustment. The agencies would likely consider values in the range of 

1.00 to 1.07.

4.4.1.9 Tractor Tables

Table 4-5 through Table 4-7 display the predefined GEM parameters proposed for the 

Phase 2 tractor compliance model. The predefined parameters were developed using the same 

methodology used in Phase 1. Many of the parameters are based on the vehicles EPA selected to 

test at SwRI and are considered to reasonably represent the fleet in their respective categories.

For example, the transmission gear ratio, axle ratio, tire diameters, and all accessory losses used 

for all tractors shown in these three tables are from the Kenworth T700 truck tested at SwRI. All 

of the other predefined parameters, such as the engine power rating, vehicle weight, payload, 

follow the Phase 1 structure. The gear mechanical efficiency as well as axle mechanical 

efficiency was developed based on several verbal communications from stakeholders. For 

further detail regarding how these parameters are chosen and used in GEM see Chapters 4.4.3 to 

4.4.9. 

Table 4-5 Class 8 Combination Tractor Sleeper Cab Predefined Modeling Parameters

Regulatory Class

Class 8 Combination Class 8 Combination Class 8 Combination

Sleeper Cab - High 

Roof

Sleeper Cab - Mid 

Roof Sleeper Cab - Low Roof

Gearbox Efficiency

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other 

gears

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears

98% for the gear with a 

1:1 gear ratio, and 96% 

for all other gears

Axle Mechanical Efficiency 95.5% 95.5% 95.5%

Total weight (kg) 31978 30277 30390

Number of Axles 5 5 5

Default Axle Configuration 6x4 6x4 6x4

Electrical Accessory Power (W) 300 300 300

Mechanical Accessory Power (W) 1000 1000 1000

Environmental Air Temperature (°C) 25 25 25

Payload (tons) 19 19 19

Weight Reduction (lbs)

Add 1/3*weight 

reduction to Payload 

tons

Add 1/3*weight 

reduction to Payload 

tons

Add 1/3*weight 

reduction to Payload 

tons

Tire Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

Drive Cycles & Weightings:

CARB HHDDT 0.05 0.05 0.05

GEM 55 mph 0.09 0.09 0.09

GEM 65 mph 0.86 0.86 0.86
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4-29

Table 4-6 Class 8 Combination Tractor Day Cab Predefined Modeling Parameters

Regulatory Subcategory

Class 8 Combination Class 8 Combination Class 8 Combination

Day Cab - High Roof Day Cab - Mid Roof Day Cab - Low Roof

Gearbox Efficiency

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears

98% for the gear with a 

1:1 gear ratio, and 96% 

for all other gears

Axle Mechanical Efficiency 95.5% 95.5% 95.5%

Total weight (kg) 31297 29529 29710

Number of Axles 5 5 5

Default Axle Configuration 6x4 6x4 6x4

Electrical Accessory Power (W) 300 300 300

Mechanical Accessory Power (W) 1000 1000 1000

Environmental air temperature (°C) 25 25 25

Payload (tons) 19 19 19

Weight Reduction (lbs)

Add 1/3*weight 

reduction to Payload 

tons

Add 1/3*weight 

reduction to Payload 

tons

Add 1/3*weight 

reduction to Payload 

tons

Tire Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

Drive Cycles & Weightings:

CARB HHDDT 0.19 0.19 0.19

GEM 55 mph 0.17 0.17 0.17

GEM 65 mph 0.64 0.64 0.64
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Table 4-7 Class 7 Combination Tractor Predefined Modeling Parameters

Regulatory Subcategory

Class 7 Combination Class 7 Combination Class 7 Combination

Day Cab - High Roof Day Cab - Mid Roof Day Cab - Low Roof

Gearbox Efficiency

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other gears

98% for the gear with a 

1:1 gear ratio, and 96% 

for all other gears

Axle Mechanical Efficiency 95.5% 95.5% 95.5%

Total weight (kg) 22679 20910 21091

Axle Base 4 4 4

Default Axle Configuration 4x2 4x2 4x2

Electrical Accessory Power (W) 300 300 300

Mechanical Accessory Power (W) 1000 1000 1000

Environmental air temperature (°C) 25 25 25

Payload (tons) 12.5 12.5 12.5

Weight Reduction (lbs)

Add 1/3*weight 

reduction to Payload 

tons

Add 1/3*weight 

reduction to Payload 

tons

Add 1/3*weight 

reduction to Payload 

tons

Tire Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

=0.425*Trailer 

Crr+0.425*Drive 

Crr+0.15*Steer Crr

Drive Cycles & Weightings:

CARB HHDDT 0.19 0.19 0.19

GEM 55 mph 0.17 0.17 0.17

GEM 65 mph 0.64 0.64 0.64

4.4.1.10 Vocational Tables

Table 4-8 through Table 4-10 display the predefined GEM parameters proposed for use 

for the vocational vehicle compliance model. Many of the parameters are based on the vehicles 

EPA selected to test at SwRI and are considered to reasonably represent the fleet in their

respective categories. For example, the Kenworth T270 truck and Ford F-650 tow truck are used 

as vehicles to represent the MHD and LHD vocational vehicle fleet, while the Kenworth T700 

and New Flyer refuse trucks are used to represent the fleet of HHD vocational vehicles. With 

those vehicles as reference, it helps to determine the type of transmission and its gear ratio, tire 

diameters, and all accessory losses used for all vocational vehicles shown in these three tables. 

Tire radius and axle ratios were selected, using good engineering judgment and stakeholder 

input, to reflect reasonable final drive ratios to match with our modeled transmissions. With the 

exception of the Multi-purpose and Urban HHD vehicles, the engine power rating is the same as 

in Phase 1. For these two subcategories, the agencies selected 11L-345 hp engines because this 

is a more typical power rating for vehicles that are not long haul. Other parameters, such as the 

engine power rating, vehicle weight, payload, weight reduction, tire rolling resistance, frontal

area, and axle base, etc. follow the Phase 1 structure. The gear mechanical efficiency as well as 

axle mechanical efficiency is selected based on the inputs from stakeholders. The weighting of 

steer tire Crr and drive tire Crr is different than in Phase 1 to better reflect the weight distribution 

over the steer and drive axles. The assignment of 50 percent of reduced weight back to payload 
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is different than in Phase 1 and not the same as for tractors. See the draft RIA Chapter 2 for 

details. Chapter 4.4.3 to 4.4.9 explains how these parameters are used in GEM.

The agencies propose to expand the number of vocational subcategories from three (in 

Phase 1) to nine (in Phase 2). It can be seen from Table 4-8 through Table 4-10, the agencies are 

also proposing to add an idle cycle for vocational vehicles to the duty cycles used in Phase 1 

certification.

Table 4-8 Vocational HHD Vehicle Predefined Modeling Parameters

Regulatory Subcategory

HHD HHD HHD

Regional Duty Cycle Multi-Purpose Duty 

Cycle Urban Duty Cycle 

Gearbox Efficiency

98% for the gear with 

a 1:1 gear ratio, and 

96% for all other 

gears

98% for all gears 98% for all gears

Axle Mechanical Efficiency 95.5% 95.5% 95.5%

Total weight (kg) 19051 19051 19051

Number of Axles 3 3 3

Electrical Accessory Power (W) 300 300 300

Mechanical Accessory Power (W) 1000 1000 1000

Environmental Air Temperature (°C) 25 25 25

CdA (m2) 6.86 6.86 6.86

Tire Crr =0.7*Drive Crr + 

0.3*Steer Crr

0.7*Drive Crr + 

0.3*Steer Crr

=0.7*Drive Crr + 

0.3*Steer Crr

Payload (tons) 7.50 7.50 7.50

Weight Reduction (lbs)

Add 0.5*weight 

reduction to Payload 

tons

Add 0.5*weight 

reduction to Payload 

tons

Add 0.5*weight 

reduction to Payload 

tons

Drive Cycles & Weightings:

CARB HHDDT 0.50 0.82 0.94

GEM 55 mph 0.28 0.15 0.06

GEM 65 mph 0.22 0.03 0.00

Idle cycle 0.10 0.15 0.20
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Table 4-9 Vocational MHD Vehicle Predefined Modeling Parameters

Regulatory Subcategory

MHD MHD MHD

Regional Duty Cycle Multi-Purpose Duty 

Cycle Urban Duty Cycle 

Gearbox Efficiency 98% for all gears 98% for all gears 98% for all gears

Axle Mechanical Efficiency 95.5% 95.5% 95.5%

Total weight (kg) 11408 11408 11408

Number of Axles 2 2 2

Electrical Accessory Power (W) 300 300 300

Mechanical Accessory Power (W) 1000 1000 1000

Environmental Air Temperature (°C) 25 25 25

CdA (m2) 5.40 5.40 5.40

Tire Crr =0.7*Drive Crr + 

0.3*Steer Crr

0.7*Drive Crr + 

0.3*Steer Crr

=0.7*Drive Crr + 

0.3*Steer Crr

Payload (tons) 5.60 5.60 5.60

Weight Reduction (lbs)

Add 0.5*weight 

reduction to Payload 

tons

Add 0.5*weight 

reduction to Payload 

tons

Add 0.5*weight 

reduction to Payload 

tons

Drive Cycles & Weightings:

CARB HHDDT 0.50 0.82 0.94

GEM 55 mph 0.28 0.15 0.06

GEM 65 mph 0.22 0.03 0.00

Idle cycle 0.10 0.15 0.20
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Table 4-10 Vocational LHD Vehicle Predefined Modeling Parameters

Regulatory Subcategory

LHD LHD LHD

Regional Duty Cycle Multi-Purpose Duty 

Cycle Urban Duty Cycle 

Gearbox Efficiency 98% for all gears 98% for all gears 98% for all gears

Axle Mechanical Efficiency 95.5% 95.5% 95.5%

Total weight (kg) 7257 7257 7257

Number of Axles 2 2 2

Electrical Accessory Power (W) 300 300 300

Mechanical Accessory Power (W) 1000 1000 1000

Environmental Air Temperature (°C) 25 25 25

CdA (m2) 5.40 5.40 5.40

Tire Crr =0.7*Drive Crr + 

0.3*Steer Crr

0.7*Drive Crr + 

0.3*Steer Crr

=0.7*Drive Crr + 

0.3*Steer Crr

Payload (tons) 2.85 2.85 2.85

Weight Reduction (lbs)

Add 0.5*weight 

reduction to Payload 

tons

Add 0.5*weight 

reduction to Payload 

tons

Add 0.5*weight 

reduction to Payload 

tons

Drive Cycles & Weightings:

CARB HHDDT 0.50 0.82 0.94

GEM 55 mph 0.28 0.15 0.06

GEM 65 mph 0.22 0.03 0.00

Idle cycle 0.10 0.15 0.20

4.4.1.11 Trailer Tables

Trailers are simulated using the same GEM models as the tractor program. There are 

only minor differences between the trailer and tractor modeling parameters and inputs. Table 

4-11 lists all of the predefined vehicle parameters of trailer baseline models. The predefined 

modeling parameters for the long box dry van trailer subcategory are identical to the Class 8 

high-roof sleeper cab tractor subcategory. The other trailer subcategories differ in tractor cab 

type, total weight, aerodynamic characteristics, number of axles, payload, and drive cycle. For 

example, the refrigerated trailers include a refrigeration unit which adds weight and slightly 

improves aerodynamic performance (reduces CdA). Short box vans are half the length, have a 

single axle, and are pulled by a day cab tractor which reduces total weight and the total payload 

carrying capacity. The drive cycle weightings are consistent with the tractor program. Long box 

trailers are simulated as being pulled by sleeper cabs, and therefore have the long-haul drive 

cycle weightings. The short box trailers are pulled by day cabs and have the short-haul 

weightings. 

Similar to the tractor program, trailer manufacturers can provide aerodynamic drag, tire 

rolling resistance and weight reduction inputs to the model. The key differences between the

trailer and tractor options are that aerodynamic drag is submitted as a change in drag (delta CdA)

for trailers, which is compared to the baseline CdA values shown in Table 4-11 within GEM, and 
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only adjustments to trailer tire rolling resistance are allowed. A list of weight reduction options 

is available in 40 CFR 1037.520 and manufacturers have the option to indicate that their trailers 

use Automatic Tire Inflation Systems (ATIS) for a predefined additional performance 

improvement. Additional information about each trailer subcategory is found in Chapter 2.10 of 

this draft RIA.

Table 4-11 Predefined Modeling Parameters for Box Trailers

Regulatory Subcategory Long Box

Dry Van

Long Box

Refrigerated Van

Short Box

Dry Van

Short Box 

Refrigerated Van

Tractor Type C8 Sleeper Cab - High Roof C8 Day Cab - High Roof

Engine Fuel Map 15L - 455 HP

Gear ratio 12.8, 9.25, 6.76, 4.9, 3.58, 2.61, 1.89, 1.38, 1, 0.73

Gearbox Efficiency 98% for 1:1 gear ratio, and 96 for all other gears

Axle Mechanical Efficiency 95.5%

Total weight (kg) 31978 33778 15191 16991

Baseline CdA Values (m2) 6.2 6.1 6.1 6.0

Number of Axles 5 4

Payload (tons) 19 10

Default Axle Configuration 6x4

Electrical Accessory Power (W) 300

Mechanical Accessory Power (W) 1000

Steer Tire RR 6.54

Drive Tire RR 6.92

Tire Radius (m) 0.5

Axle Drive Ratio 3.7

Tire Crr =0.425*Trailer Crr+0.425*Drive Crr+0.15*Steer Crr

Weight Reduction (lbs) Add 1/3*weight reduction to Payload tons

Drive Cycles & Weightings:

CARB HHDDT 0.05 0.19

GEM 55 mph 0.09 0.17

GEM 65 mph 0.86 0.64

Payload (tons) 19 7

4.5 Technology Improvements that Are Recognized in GEM without 

Simulation

The development of GEM as a compliance tool has required the agencies to balance the 

need for simplicity against the rigor of the model. As part of that process, the agencies have 

identified several technologies and technological improvements that would be difficult to 

accurately simulate, but that should be recognized during certification. These would be 

recognized in the proposed Phase 2 GEM through post-simulation adjustments to the results. 

This is similar to what was done in Phase 1, where the GEM interface included pull-down menus 

for manufacturers to select these adjustments. For this reason, these adjustments have come to 

be known as pull-down technologies.
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Phase 2 GEM would continue to recognize those technologies that would be difficult to 

model accurately. In addition to those recognized in Phase 1, the technology list is expanded to a 

much wider range as discussed in the next few paragraphs of this Chapter. In contrast to the 

Phase 1 approach, Phase 2 GEM uses a different approach in recognizing these technologies. 

First of all, all technology improvements are built into GEM. Default improvement values for 

each of these technologies, developed by the agencies after consulting various stakeholders and 

searching for literature values, are implemented into GEM. The user can only select either “Y” 

or “N” in the GEM input file, where Y means that the technology is included, while N means the 

vehicle does not have this technology. This means that users have no flexibility to enter their 

own values.

For some of these technologies, such as predictive cruise control and Automatic Tire 

Inflation Systems (ATIS), the actual benefit is dependent on how operators behave in the real 

world. For example, ATIS would be of very little benefit where a driver made sure on a daily 

basis that the tires were properly inflated, but would have large benefits where a driver never 

checked the tires. For other technologies, the benefits of the technology are small relative to the 

difficulty of rigorously simulating it. The agencies believe the technology improvement

approach is an appropriate compromise that will achieve the regulatory goal of incentivizing the 

use of the technology. 

In this proposed approach, the GEM software would adjust the simulation results to 

decrease the g/ton-mile results that are output by the model. For example, with a technology that 

is assigned a 1 percent benefit, the official result for a vehicle that was simulated as having 500 

g/ton-mile CO2 emissions would be reported as having an emission rate of 495 g/ton-mile.

The technology improvement values used for tractors are shown in Table 4-12. These 

values represent the agencies’ best judgment about the appropriate value for each of these 

technologies. We are generally assigning minimum values to be conservative and not 

overestimate the actual in-use benefits. These values were developed based on all available 

information, including information from stakeholders.
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Table 4-12 Tractor Technology Improvement Values

Technology Improvement Class 8 Sleeper 

Cabs

Class 8 Day 

Cabs

Class 7 Day 

Cabs

Class 8 Heavy Haul 

Tractors

Single Drive Axle (6x2 or 4x2)

Configuration 

2.5% 2.5% N/A 2.5%

Part-time Single Drive Axle 6x2

Configurationa

2.5% 2.5% N/A 2.5%

Low Friction Axle Lubricant 0.5% 0.5% 0.5% 0.5%

Automated Manual, Automatic, 

and Dual Clutch Transmissions

2% 2% 2% 2%

Predictive Cruise control 2% 2% 2% 2%

High Efficiency Air 

Conditioning Compressor

0.5% 0.5% 0.5% 0.5%

Electric Accessories 1% 1% 1% 1%

Extended Idle Reduction 5% N/A N/A N/A

Automatic Tire Inflation System

(ATIS)

1% 1% 1% 1%

Note:

a A 2.5% reduction over the 55 mph and 65 mph cycles and no reduction over the CARB HHDDT cycle

For vocational vehicles, the technologies in Table 4-13 would be considered.

Table 4-13 Vocational Vehicle Technology Improvement Values

Technology Improvement Regional Duty Cycle Multi-Purpose Duty 

Cycle Urban Duty Cycle 

HHD Single Drive Axle (6x2 or 

4x2) Configuration 

2.5% 2.5% 2.5%

HHD Part-time Single Drive Axle 

6x2 Configurationa

1.0% 0.3% 0.1%

Low Friction Axle Lubricant 0.5% 0.5% 0.5%

HHD Automated Manual or Dual 

Clutch Transmissions

2.3% N/A N/A

PTO Delta Fuel (g/ton-mile) 0 to 30 0 to 30 0 to 30

Neutral Idle Emissions during idle cycle calculated using torque and speed values 

from idle fuel map with the transmission in drive and neutral, 10% and 

90% of the cycle time, respectivelyb

Stop-Start Idle Reduction 90 percent reduction of idle-cycle emissions calculated using torque and 

speed values from idle fuel map with the transmission in driveb

Notes:

a Based on 2.5% reduction over the 55 mph and 65 mph cycles and no reduction over the CARB HHDDT cycle

b

See idle fuel consumption test procedure at 40 CFR 1036.535(d).

For trailers, the following technologies in Table 4-14 would be considered.

Table 4-14 Trailer Technology Improvement Values

Technology Improvement Effectiveness

Automatic Tire Inflation Systems 1.5%
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If a manufacturer believes that the CO2 reduction benefits assigned by the agencies are an 

underestimate, they have the option to perform powertrain testing or request (and demonstrate) 

credit in the off-cycle technology process.
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References

1

 http://www.epa.gov/otaq/climate/gem.htm

2

“Peer Review of the Greenhouse gas Emissions Model (GEM) and EPA's Response to Comments,” found in the 

docket of this rulemaking, EPA–420-R-15-009

3 K. Newman, J. Kargul, and D. Barba, “Development and Testing of an Automatic Transmission Shift Schedule 

Algorithm for Vehicle Simulation, “SAE Int. J. Engines 8(3):2015, doi:10.4271/2015-01-1142

4 Reinhart, T.E. (2015, June). Commercial Medium- and Heavy-Duty Truck Fuel Efficiency Technology Study –

Report #1. (Report No. DOT HS 812 146). Washington, DC: National Highway Traffic Safety Administration.

5 MatLab information (© 1994-2010 The MathWorks, Inc.) can be found at 

http://www.mathworks.com/products/matlab. Simulink information (© 1994-2010 The MathWorks, Inc.) can be 

found at http://www.mathworks.com/products/simulink. StateFlow information (© 1994-2010 The MathWorks, 

Inc.) can be found at http://www.mathworks.com/products/stateflow

6 US EPA, “Technical Research Workshop supporting EPA and NHTSA Phase 2 Standards for MD/HD Greenhouse 

Gas and Fuel Efficiency — December 10 and 11, 2014,” http://www.epa.gov/otaq/climate/regs-heavy-duty.htm

7

J. W. Anthony, J.V. Sarlashkar, A. Phan, C. E. Roberts, Jr., H. Zhang, J. Sanchez, M. Spears, A. Cullen, 

Powertrain test cell and its test procedure development for greenhouse gas emission measurements, ASME Int. J. , 

[in press]

8

SwRI, “GEM Validation”, Technical Research Workshop supporting EPA and NHTSA Phase 2 Standards for 

MD/HD Greenhouse Gas and Fuel Efficiency — December 10 and 11, 2014, http://www.epa.gov/otaq/climate/regsheavy-duty.htm

9

J. Anthony, J. Sarlashkar, H. Zhang, J. Sanchez, M. Spears, A. Cullen, Chassis testing versus powertrain testing 

for heavy-duty vehicle greenhouse gas emission measurements, SAE 2014 Commercial Vehicle Engineering 

Congress, Oct. 7-9, 2014

10 H. Zhang, M. Spears, and A. Cullen, Technology recognition for the next phase heavy-duty greenhouse gas 

emission and fuel efficiency standards, SAE 2014 Commercial Vehicle Engineering Congress, Oct. 7-9, 2014.

11 United States Environmental Protection Agency. SmartWay Transport Partnership July 2010 e-update accessed 

July 16, 2010, from http://www.epa.gov/smartwaylogistics/newsroom/documents/e-update-july-10.pdf