compliance across all fleets. The CAFE Model calculates civil penalties
(adjusted for inflation) for CAFE shortfalls and provides means to
estimate that a manufacturer might stop adding fuel-saving technologies
once continuing to do so would effectively be more ``expensive'' (after
accounting for fuel prices and buyers' willingness to pay for fuel
economy) than paying civil penalties. The CAFE Model does not allow
civil penalty payment as an option for EPA's GHG standards or NHTSA's
HDPUV standards.\66\
---------------------------------------------------------------------------

    \66\ While civil penalties are an option in the HDPUV fleet, the
penalties for noncompliance are significantly higher, and thus
manufactures will try to avoid paying them. Setting the model to
disallow civil penalties acts to best simulate this behavior. If the
model does find no option other than ``paying a civil penalty'' in
the HDPUV fleet, this cost should be considered a proxy for credit
purchase. NHTSA seeks comment on whether and how to model civil
penalties for HDPUVs for the final rule.
---------------------------------------------------------------------------

    Dual-Fueled and Dedicated Alternative Fuel Vehicles: For purposes
of calculating passenger car and light truck CAFE levels used to
determine compliance, 49 U.S.C. 32905 and 32906 specify methods for
calculating the fuel economy levels of vehicles operating on
alternative fuels to gasoline or diesel, such as electricity. In some
cases, after MY 2020, methods for calculating AFV fuel economy are
governed by regulation. The CAFE Model is able to account for these
requirements explicitly for each vehicle model. However, 49 U.S.C.
32902 prohibits consideration of the fuel economy of dedicated AFVs,
and requires that dual-fueled AFVs' fuel economy, such as plug-in
electric vehicle (EVs), be calculated as though they ran only on
gasoline or diesel, when NHTSA determines the maximum feasible fuel
economy level that manufacturers can achieve in a given year for which
NHTSA is establishing CAFE standards. The CAFE Model therefore has an
option to be run in a manner that excludes the additional application
of dedicated AFVs and counts only the gasoline fuel economy of dual-
fueled AFVs, in MYs for which maximum feasible standards are under
consideration. As allowed under NEPA for analysis appearing in
Environmental Impact Statements (EIS) that help inform decision makers
about the environmental impacts of CAFE standards, the CAFE Model can
also be run without this analytical constraint. The CAFE Model does
account for dedicated and dual-fueled AFVs when simulating
manufacturers' potential responses to EPA's GHG standards because the
Clean Air Act (CAA), under which the EPA derives its authority to set
GHG standards for motor vehicles, contains no restrictions in using
AFVs for compliance. There are no specific statutory directions in EISA
with regard to dedicated and dual-fueled AFV fuel efficiency for
HDPUVs, so the CAFE Model reflects relevant regulatory provisions by
calculating fuel consumption directly per 49 U.S.C. 32905 and 32906
specified methods.
    ZEV Mandates: The CAFE Model can simulate manufacturers' compliance
with state-level ZEV mandates applicable in California and ``Section
177'' \67\ states. This approach involves identifying specific vehicle
model/configurations that could be replaced with BEVs and converting to
BEVs only enough vehicle models to meet the manufacturer's compliance
obligations under state-level ZEV mandates, before beginning to
consider the potential that other technologies could be applied toward
compliance with CAFE, HDPUV, or GHG standards.
---------------------------------------------------------------------------

    \67\ The term ``Section 177'' states refers to states which have
elected to adopt California's standards in lieu of Federal
requirements, as allowed under section 177 of the CAA.
---------------------------------------------------------------------------

    Creation and Use of Compliance Credits: Section 32903 of 49 U.S.C.
provides that manufacturers may earn CAFE ``credits'' by achieving a
CAFE level beyond that required of a given passenger car or light truck
fleet in a given MY and specifies how these credits may be used to
offset the amount by which a different fleet falls short of its
corresponding requirement. These provisions allow credits to be
``carried forward'' and ``carried back'' between MYs, transferred
between regulated classes (domestic passenger cars, imported passenger
cars, and light trucks), and traded between manufacturers. However,
credit use for passenger car and light truck compliance is also subject
to specific statutory limits. For example, CAFE compliance credits can
be carried

forward a maximum of five MYs and carried back a maximum of three MYs.
Also, EPCA/EISA caps the amount of credits that can be transferred
between passenger car and light truck fleets and prohibits
manufacturers from applying traded or transferred credits to offset a
failure to achieve the applicable minimum standard for domestic
passenger cars. The CAFE Model can simulate manufacturers' potential
use of CAFE credits carried forward from prior MYs or transferred from
other fleets.\68\ Section 32902 of 49 U.S.C. prohibits consideration of
manufacturers' potential application of CAFE compliance credits when
determining the maximum feasible fuel economy level that manufacturers
can achieve for their fleets of passenger cars and light trucks. The
CAFE Model can be operated in a manner that excludes the application of
CAFE credits for a given MY under consideration for standard setting,
and NHTSA operated the model with that constraint for the purpose of
determining the appropriate CAFE standard for passenger cars and light
trucks. No such statutory restrictions exist for setting HDPUV
standards. For modeling EPA's GHG standards, the CAFE Model does not
limit transfers because the CAA does not limit them. Insofar as the
CAFE Model can be exercised in a manner that simulates trading of GHG
compliance credits, such simulations treat trading as unlimited.\69\
---------------------------------------------------------------------------

    \68\ The CAFE Model does not explicitly simulate the potential
that manufacturers would carry CAFE or GHG credits back (i.e.,
borrow) from future model years, or acquire and use CAFE compliance
credits from other manufacturers. At the same time, because EPA has
elected not to limit credit trading, the CAFE Model can be exercised
(for purposes of evaluating GHG standards) in a manner that
simulates unlimited (a.k.a. ``perfect'') GHG compliance credit
trading throughout the industry (or, potentially, within discrete
trading ``blocs''). For purposes of analyzing CAFE standards, NHTSA
believes it is challenging to predict precisely how manufacturers
may choose to use these particular flexibilities in the future: for
example, while it is reasonably foreseeable that a manufacturer who
over-complies in one year may ``coast'' through several subsequent
years relying on that over-compliance rather than making further
technology improvements, it is harder to know whether manufacturers
will rely on future technology investments to offset prior-year
shortfalls, or whether/how manufacturers will trade credits with
market competitors rather than making their own technology
investments. Historically, carry-back and trading have been much
less utilized than carry-forward, for a variety of reasons including
higher risk and preference not to `pay competitors to make fuel
economy improvements we should be making' (to paraphrase one
manufacturer), although NHTSA recognizes that carry-back and trading
are used more frequently when standards increase in stringency more
rapidly. Given these dynamics, and given also the fact that the
agency has yet to resolve some of the analytical challenges
associated with simulating use of these flexibilities, the agency
has decided to support this proposal with a conservative analysis
that sets aside the potential that manufactures would depend widely
on borrowing and trading--not to mention that, for purposes of
determining maximum feasible CAFE standards, statute prohibits NHTSA
from considering the trading, transferring, or availability of
credits (see 49 U.S.C. 32902(h)). While compliance costs in real
life may be somewhat different from what is modeled in the
rulemaking record as a result of this decision, that is broadly true
no matter what, and the agency does not believe that the difference
would be so great that it would change the policy outcome.
Furthermore, a manufacturer employing a trading strategy would
presumably do so because it represents a lower-cost compliance
option. Thus, the estimates derived from this modeling approach are
likely to be conservative in this respect, with real-world
compliance costs likely being lower.
    \69\ To avoid making judgments about possible future trading
activity, the model simulates trading by combining all manufacturers
into a single entity, so that the most cost-effective choices are
made for the fleet as a whole.
---------------------------------------------------------------------------

    Statutory Basis for Stringency: Section 32902 of 49 U.S.C. requires
the Secretary of Transportation (by delegation, NHTSA) to set CAFE
standards for passenger cars and light trucks at the maximum feasible
levels that manufacturers can achieve in a given MY, considering
technological feasibility, economic practicability, the need of the
United States to conserve energy, and the impact of other motor vehicle
standards of the Government on fuel economy. For HDPUV standards, which
must also achieve the maximum feasible improvement, the similar yet
distinct factors of appropriateness, cost-effectiveness, and
technological feasibility must be considered. EPCA/EISA authorizes the
Secretary of Transportation (by delegation, NHTSA) to interpret these
factors, and as the Department's interpretation has evolved, NHTSA has
continued to expand and refine its qualitative and quantitative
analysis to account for these statutory factors. For example, one of
the ways that economic practicability considerations are incorporated
into the analysis is through the technology effectiveness
determinations: the Autonomie simulations reflect the agency's judgment
that it would not be economically practicable (nor, for HDPUVs,
appropriate) for a manufacturer to ``split'' an engine shared among
many vehicle model/configurations into myriad versions each optimized
to a single vehicle model/configuration.
    National Environmental Policy Act: NEPA requires NHTSA to consider
the environmental impacts of its actions in its decision-making
processes, including for CAFE standards. The Draft EIS accompanying
this proposal documents changes in emission inventories as estimated
using the CAFE Model, but also documents corresponding estimates--based
on the application of other models documented in the Draft EIS--of
impacts on the global climate, on air quality, and on human health.
    Other Aspects of Compliance: Beyond these statutory requirements
applicable to DOT, EPA, or both are a number of specific technical
characteristics of CAFE, HDPUV, and/or GHG regulations that are also
relevant to the construction of this analysis, like the ``off-cycle''
technologies fuel economy/emissions improvements that apply for both
CAFE and GHG compliance. Although too little information is available
to account for these provisions explicitly in the same way that NHTSA
has accounted for other technologies, the CAFE Model includes and makes
use of inputs reflecting NHTSA's expectations regarding the extent to
which manufacturers may earn such credits, along with estimates of
corresponding costs. Similarly, the CAFE Model includes and makes use
of inputs regarding credits EPA has elected to allow manufacturers to
earn toward GHG levels (not CAFE or HDPUV) based on the use of air
conditioner refrigerants with lower global warming potential, or on the
application of technologies to reduce refrigerant leakage. In addition,
the CAFE Model accounts for EPA ``multipliers'' for certain AFVs, based
on current regulatory provisions or on alternative approaches. Although
these are examples of regulatory provisions that arise from the
exercise of discretion rather than specific statutory mandate, they can
materially impact outcomes.
3. What updated assumptions does the current model reflect as compared
to the 2022 final rule?
    Besides the updates to the CAFE Model described above, any analysis
of regulatory actions that will be implemented several years in the
future, and whose benefits and costs accrue over decades, requires a
large number of assumptions. Over such time horizons, many, if not
most, of the relevant assumptions in such an analysis are inevitably
uncertain. Each successive CAFE analysis seeks to update assumptions to
better reflect the current state of the world and the best current
estimates of future conditions.
    A number of assumptions have been updated since the 2022 final
rule. As discussed below, NHTSA has updated its ``analysis fleet'' from
a MY 2020 reference to a MY 2022 reference for passenger cars and light
trucks and has built an updated HDPUV analysis fleet (the last HDPUV
analysis fleet was built in 2016). NHTSA has also updated estimates of
manufacturers' compliance credit ``holdings,'' updated fuel price
projections to reflect the U.S. EIA's 2022 Annual Energy Outlook (AEO),
updated

projections of GDP and related macroeconomic measures, and updated
projections of future highway travel. While NHTSA would have made these
updates as a matter of course, we note that the ongoing global economic
recovery and the ongoing war in Ukraine have impacted major analytical
inputs such as fuel prices, GDP, vehicle production and sales, and
highway travel. Many inputs remain uncertain, and NHTSA has conducted
sensitivity analyses around many inputs to attempt to capture some of
that uncertainty. These and other updated analytical inputs are
discussed in detail in the Draft TSD and PRIA.
    Additionally, E.O. 13990 required the formation of an Interagency
Working Group (IWG) on the Social Cost (SC) of GHGs and charged this
body with updating estimates of the SCs of carbon, nitrous oxide, and
methane (CH4). As discussed in the TSD, NHTSA has followed
DOT's determination that the values developed in the IWG's interim
guidance are the most consistent with the best available science and
economics and are the most appropriate estimates to use in the analysis
of this proposal. Those estimates of costs per ton of emissions (or
benefits per ton of emissions reductions) are considerably greater than
those applied in the analysis supporting the 2020 final rule. Even
still, the estimates NHTSA is now using are not able to fully quantify
and monetize a number of important categories of climate damages;
because of those omitted damages and other methodological limits, DOT
believes its values for SC-GHG are conservative underestimates.

B. What is NHTSA analyzing?

    NHTSA is analyzing the effects of different potential CAFE and
HDPUV standards on industry, consumers, society, and the world at
large. These different potential standards are identified as regulatory
alternatives, and amongst the regulatory alternatives, NHTSA identifies
which ones the agency is proposing. As in the past several CAFE
rulemakings and in the Phase 2 HDPUV rulemaking, NHTSA is proposing to
establish attribute-based CAFE and HDPUV standards defined by a
mathematical function of vehicle footprint (which has an observable
correlation with fuel economy) and a towing-and-hauling-based WF
respectively.\70\ EPCA, as amended by EISA, expressly requires that
CAFE standards for passenger cars and light trucks be based on one or
more vehicle attributes related to fuel economy, and be expressed in
the form of a mathematical function.\71\ The statute gives NHTSA
discretion as to how to structure standards for HDPUVs, and NHTSA
continues to believe that attribute-based standards expressed as a
mathematical function remain appropriate for those vehicles as well,
given their similarity in many ways to light trucks. Thus, the proposed
standards (and the regulatory alternatives) for passenger cars and
light trucks take the form of fuel economy targets expressed as
functions of vehicle footprint (the product of vehicle wheelbase and
average track width) that are separate for passenger cars and light
trucks, and the proposed standards and alternatives for HDPUVs take the
form of fuel consumption targets expressed as functions of vehicle WF
(which is in turn a function of towing and hauling capabilities).
---------------------------------------------------------------------------

    \70\ Vehicle footprint is the vehicle's wheelbase times average
track width (or more simply, the length and width beween the
vehicle's four wheels). The HDPUV FE towing-and-hauling-based ``WF''
metric is based on a vehicle's payload and towing capabilities, with
an added adjustment for 4-wheel drive vehicles.
    \71\ 49 U.S.C. 32902(a)(3)(A).
---------------------------------------------------------------------------

    For passenger cars and light trucks, under the footprint-based
standards, the function defines a fuel economy performance target for
each unique footprint combination within a car or truck model type.
Using the functions, each manufacturer thus will have a CAFE average
standard for each year that is almost certainly unique to each of its
fleets,\72\ based upon the footprint and production volumes of the
vehicle models produced by that manufacturer. A manufacturer will have
separate footprint-based standards for cars and for trucks, consistent
with 49 U.S.C. 32902(b)'s direction that NHTSA must set separate
standards for cars and for trucks. The functions are mostly sloped, so
that generally, larger vehicles (i.e., vehicles with larger footprints)
will be subject to lower mpg targets than smaller vehicles. This is
because smaller vehicles are generally more capable of achieving higher
levels of fuel economy, mostly because they tend not to have to work as
hard (and therefore to require as much energy) to perform their driving
task. Although a manufacturer's fleet average standard could be
estimated throughout the MY based on the projected production volume of
its vehicle fleet (and are estimated as part of EPA's certification
process), the standards with which the manufacturer must comply are
determined by its final model year (FMY) production figures. A
manufacturer's calculation of its fleet average standards, as well as
its fleets' average performance at the end of the MY, will thus be
based on the production-weighted average target and performance of each
model in its fleet.\73\
---------------------------------------------------------------------------

    \72\ EPCA/EISA requires NHTSA and EPA to separate passenger cars
into domestic and import passenger car fleets for CAFE compliance
purposes (49 U.S.C. 32904(b)), whereas EPA combines all passenger
cars into one fleet for GHG compliance purposes.
    \73\ As discussed in prior rulemakings, a manufacturer may have
some vehicle models that exceed their target and some that are below
their target. Compliance with a fleet average standard is determined
by comparing the fleet average standard (based on the production-
weighted average of the target levels for each model) with fleet
average performance (based on the production-weighted average of the
performance of each model). This is inherent in the statutory
structure of CAFE, which requires NHTSA to set corporate average
standards.
---------------------------------------------------------------------------

    For passenger cars, consistent with prior rulemakings, NHTSA is
proposing to define fuel economy targets as shown in Equation II-1.
BILLING CODE 4910-59-P
[GRAPHIC] [TIFF OMITTED] TP17AU23.005

Where:

TARGETFE is the fuel economy target (in mpg) applicable
to a specific vehicle model type with a unique footprint
combination,
a is a minimum fuel economy target (in mpg),
b is a maximum fuel economy target (in mpg),
c is the slope (in gallons per mile (or gpm) per square foot) of a
line relating fuel

consumption (the inverse of fuel economy) to footprint, and
d is an intercept (in gpm) of the same line.

    Here, MIN and MAX are functions that take the minimum and maximum
values, respectively, of the set of included values. For example,
MIN[40, 35] = 35 and MAX(40, 25) = 40, such that MIN[MAX(40, 25), 35] =
35.
    For the Preferred Alternative, this equation is represented
graphically as the curves in Figure II-2.
[GRAPHIC] [TIFF OMITTED] TP17AU23.006

    For light trucks, also consistent with prior rulemakings, NHTSA is
proposing to define fuel economy targets as shown in Equation II-2.
[GRAPHIC] [TIFF OMITTED] TP17AU23.007

Where:

TARGETFE is the fuel economy target (in mpg) applicable
to a specific vehicle model type with a unique footprint
combination,
a, b, c, and d are as for passenger cars, but taking values specific
to light trucks,
e is a second minimum fuel economy target (in mpg),
f is a second maximum fuel economy target (in mpg),
g is the slope (in gpm per square foot) of a second line relating
fuel consumption (the inverse of fuel economy) to footprint, and
h is an intercept (in gpm) of the same second line.

    For the Preferred Alternative, this equation is represented
graphically as the curves in Figure II-3.

[GRAPHIC] [TIFF OMITTED] TP17AU23.008

    Although the general model of the target function equation is the
same for passenger cars and light trucks, and the same for each MY, the
parameters of the function equation differ for cars and trucks. The
actual parameters for both the Preferred Alternative and the other
regulatory alternatives are presented in Section III.
    The required CAFE level applicable to a passenger car (either
domestic or import) or light truck fleet in a given MY is determined by
calculating the production-weighted harmonic average of fuel economy
targets applicable to specific vehicle model configurations in the
fleet, as shown in Equation II-3.
[GRAPHIC] [TIFF OMITTED] TP17AU23.009

Where:

CAFErequired is the CAFE level the fleet is required to
achieve,
i refers to specific vehicle model/configurations in the fleet,
PRODUCTIONi is the number of model configuration i
produced for sale in the U.S., and
TARGETFE, i is the fuel economy target (as defined above)
for model configuration i.

    For HDPUVs, NHTSA has previously set attribute-based standards, but
used a work-based metric as the attribute rather than footprint. Work-
based measurements such as payload and towing capability are key among
the parameters that characterize differences in the design of these
vehicles, as well as differences in how the vehicles will be used.
Since NHTSA has been regulating HDPUVs, these standards have been based
on a WF attribute that combines the vehicle's payload and towing
capabilities, with an added adjustment for 4-wheel drive vehicles.
Again, while NHTSA is not required by statute to set HDPUV standards
that are attribute-based and that are described by a mathematical
function, NHTSA continues to believe that doing so is reasonable and
appropriate for this segment of vehicles, consistent with prior HDPUV
standard-setting rulemakings. NHTSA proposes to continue using the
work-based attribute and gradually increasing stringency (which for
HDPUVs means that standards appear to decline, as compared to passenger
car and light truck standards where increasing stringency means that
standards appear to increase. This is because HDPUV standards are based
on fuel consumption, which is the inverse of fuel economy,\74\ the
metric that NHTSA

is statutorily required to use when setting standards for light-duty
vehicle (LDV) fuel use). NHTSA proposes to define HDPUV fuel efficiency
targets as shown in Equation II-4.
---------------------------------------------------------------------------

    \74\ For additional information, see the National Academies of
Sciences, Engineering, and Medicine. 2011. Assessment of Fuel
Economy Technologies for Light-Duty Vehicles. Washington, DC. The
National Academies Press. Available at: https://nap.nationalacademies.org/catalog/12924/assessment-of-fuel-economy-technologies-for-light-duty-vehicles. (Accessed: May 31, 2023). Fuel
economy is a measure of how far a vehicle will travel with a gallon
(or unit) of fuel and is expressed in mpg. Fuel consumption is the
inverse of fuel economy. It is the amount of fuel consumed in
driving a given distance. Fuel consumption is a fundamental
engineering measure that is directly related to fuel consumed per
100 miles and is useful because it can be employed as a direct
measure of volumetric fuel savings.
[GRAPHIC] [TIFF OMITTED] TP17AU23.010

---------------------------------------------------------------------------
Where:

WF = Work Factor = [0.75 x (Payload Capacity + Xwd)] + [0.25 x
Towing Capacity]
Where:
Xwd = 4wd adjustment = 500 lbs. if the vehicle group is equipped
with 4WD and all-wheel drive, otherwise equals 0 lbs. for 2wd
Payload Capacity = GVWR (lbs.) - Curb Weight (lbs.) (for each
vehicle group)
Towing Capacity = GCWR \75\ (lbs.) - GVWR (lbs.) (for each vehicle
group)
---------------------------------------------------------------------------

    \75\ Gross Combined Weight Rating.

    For the Preferred Alternative, this equation is represented
graphically as the curves in Figure II-4 and Figure II-5.
[GRAPHIC] [TIFF OMITTED] TP17AU23.011

[GRAPHIC] [TIFF OMITTED] TP17AU23.012

    Similar to the standards for passenger cars and light trucks, NHTSA
(and EPA) have historically set HDPUV standards such that each
manufacturer's fleet average standard is based on production volume-
weighting of target standards for all vehicles, which are based on each
vehicle's WF as explained above. Thus, for HDPUVs, the required fuel
efficiency level applicable in a given MY is determined by calculating
the production-weighted harmonic average of subconfiguration targets
applicable to specific vehicle model configurations in the fleet, as
shown in Equation II-5.
[GRAPHIC] [TIFF OMITTED] TP17AU23.013

BILLING CODE 4910-59-C
Where:

Subconfiguration Target Standardi = fuel consumption standard for
each group of vehicles with the same payload, towing capacity, and
drive configuration (gallons per 100 miles), and
Volumei = production volume of each unique subconfiguration of a
model type based upon payload, towing capacity, and drive
configuration.

    Chapter 1 of the Draft TSD contains a detailed description of the
use of attribute-based standards, generally, for passenger cars, light
trucks, and HDPUVs, and explains the specific decision, in past rules
and for the current proposal, to continue to use vehicle footprint as
the attribute over which to vary passenger car and light truck
stringency, and WF as the attribute over which to vary HDPUV
stringency. That chapter also discusses the policy and approach in
selecting the specific mathematical functions. NHTSA refers readers to
the Draft TSD for a full discussion of these topics and seeks comment
on that discussion.

C. What inputs does the compliance analysis require?

    The first step in our analysis of the effects of different levels
of fuel economy standards is the compliance simulation. When we say,
``compliance simulation'' throughout this rulemaking, we mean the CAFE
Model's simulation of how vehicle manufacturers could comply with
different levels of CAFE standards by adding fuel-economy-improving
technology to an existing fleet of vehicles.\76\ At the most basic
level, a model is a set of equations, algorithms,\77\ or other
calculations that are used to make predictions about a

complex system, such as the environmental impact of a particular
industry or activity. A model may consider various inputs, such as
emissions data, technology costs, or other relevant factors, and use
those inputs to generate output predictions.
---------------------------------------------------------------------------

    \76\ When we use the phase ``the model'' throughout this
section, we are referring to the CAFE Model. Any other model will be
specifically named.
    \77\ See Merriam-websiter, ``algorithm.'' Broadly, an algorithm
is a step-by-step procedure for solving a problem or accomplishing
some end. More specifically, an algorithm is a procedure for solving
a mathematical problem (as of finding the greatest common divisor)
in a finite number of steps that frequently involves repetition of
an operation.
---------------------------------------------------------------------------

    One important note about models is that a model is only as good as
the data and assumptions that go into it. We attempt to ensure that the
technology inputs and assumptions that go into the CAFE Model to
project the effects of different levels of CAFE standards are based on
sound science and reliable data, and that our reasons for using those
inputs and assumptions are transparent and understandable to
stakeholders. This section and the following section discuss at a high
level how we generate the technology inputs and assumptions that the
CAFE Model uses for the compliance simulation.\78\ The Draft Technical
Support Document, CAFE Model Documentation, CAFE Analysis Autonomie
Model Documentation,\79\ and other technical reports supporting this
proposal discuss our technology inputs and assumptions in more detail.
---------------------------------------------------------------------------

    \78\ As explained throughout this section, our inputs are a
specific number or datapoint used by the model, and our assumptions
are based on judgment after careful consideration of available
evidence. An assumption can be an underlying reason for the use of a
specific datapoint, function, or modeling process. For example, an
input might be the fuel economy value of the Ford Mustang, whereas
the assumption is that the Ford Mustang's fuel economy value
reported in Ford's CAFE compliance data should be used in our
modeling.
    \79\ The ANL report is titled ``Vehicle Simulation Process to
Support the Analysis for MY 2027 and Beyond CAFE and MY 2030 and
Beyond HDPUV FE Standards;'' however, for ease of use and
consistency with the Draft TSD, it is referred to as ``CAFE Analysis
Autonomie Documentation.''
---------------------------------------------------------------------------

    We incorporate technology inputs and assumptions either directly in
the CAFE Model or in the CAFE Model's various input files. The heart of
the CAFE Model's decisions about how to apply technologies to
manufacturer's vehicles to project how the manufacturer could meet CAFE
standards is the compliance simulation algorithm. The compliance
simulation algorithm is several equations that direct the model to
apply fuel economy improving technologies to vehicles in a way that
estimates how manufacturers might apply those technologies to their
vehicles in the real world. The compliance simulation algorithm
projects a cost-effective pathway for manufacturers to comply with
different levels of CAFE standards, considering the technology present
on manufacturer's vehicles now, and what technology could be applied to
their vehicles in the future. Embedded directly in the CAFE Model is
the universe of technology options that the model can consider and some
rules about the order in which it can consider those options and
estimates of how effective fuel economy improving technology is on
different types of vehicles, like on a sedan or a pickup truck.
    Technology inputs and assumptions are also located in all four of
the CAFE Model's input files. The Market Data Input file is a Microsoft
Excel file that characterizes the baseline automotive fleet used as the
starting point for the analysis. There is one Excel row describing each
vehicle model and model configuration manufactured in the United States
in a MY (or years), and input and assumption data that links that
vehicle to technology, economic, environmental, and safety effects.
Next, the Technologies Input File identifies approximately six dozen
technologies we use in the analysis, uses phase-in caps to identify
when and how widely each technology can be applied to specific types of
vehicles, provides most of the technology costs (only battery costs for
electrified vehicles are provided in a separate file), and provides
some of the inputs involved in estimating impacts on vehicle fuel
consumption and weight. The Scenarios Input File provides the
coefficient values defining the standards for each regulatory
alternative,\80\ and other relevant information applicable to modeling
each regulatory scenario. This information includes, for example, the
estimated value of select tax credits from the IRA, which provide
Federal technology incentives for electrified vehicles, and the PEF,
which is a value that the Secretary of Energy determines under EPCA
that applies to EV fuel economy values.\81\ Finally, the Parameters
Input File contains mainly economic and environmental data, as well as
data about how fuel economy credits and California's Zero Emissions
Vehicle program credits are simulated in the model.
---------------------------------------------------------------------------

    \80\ The coefficient values are defined in Draft TSD Chapter
1.2.1 for both the CAFE and HDPUV FE standards.
    \81\ See 49 U.S.C. 32904(a)(2), 88 FR 21525 (April 11, 2023).
---------------------------------------------------------------------------

    We generate these technology inputs and assumptions in several
ways, including by and through evaluating data submitted by vehicle
manufacturers pursuant to their CAFE reporting obligations;
consolidating public data on vehicle models from manufacturer websites,
press materials, marketing brochures, and other publicly available
information; collaborative research, testing, and modeling with other
Federal agencies, like the DOE's ANL; research, testing, and modeling
with independent organizations, like IAV GmbH Ingenieurgesellschaft
Auto und Verkehr (IAV), Southwest Research Institute (SwRI), NAS and
FEV North America; determining that work done for prior rules is still
relevant and applicable; considering feedback from stakeholders on
prior rules and in meetings conducted before the commencement of this
rule; and using our own engineering judgment. When we say,
``engineering judgment'' throughout this rulemaking, we are referring
to decisions made by a team of engineers and analysts. This judgment is
based on their experience working in the automotive industry and other
relevant fields, and assessment of all the data sources described
above. Most importantly, we use engineering judgment to assess how best
to represent vehicle manufacturer's potential responses to different
levels of CAFE standards within the boundaries of our modeling tools,
as ``a model is meant to simplify reality in order to make it
tractable.'' \82\ In other words, we use engineering judgment to
concentrate potential technology inputs and assumptions from millions
of discrete data points from hundreds of sources to three datasets
integrated in the CAFE Model and four input files. How the CAFE Model
decides to apply technology, i.e., the compliance simulation algorithm,
has also been developed using engineering judgment, considering some of
the same factors that manufacturers consider when they add technology
to vehicles in the real world.
---------------------------------------------------------------------------

    \82\ Chem. Mfrs. Ass'n v. E.P.A., 28 F.3d 1259, 1264-65 (D.C.
Cir. 1994) (citing Milton Friedman, The Methodology of Positive
Economics, in Essays in Positive Economics 3, 14-15 (1953)).
---------------------------------------------------------------------------

    While upon first read this discussion may seem oversimplified, we
believe that there is value in all stakeholders being able to
understand how the analysis uses different sets of technology inputs
and assumptions and how those inputs and assumptions are based on real-
world factors. This is so that all stakeholders have the appropriate
context to better comment on the specific technology inputs and
assumptions discussed later and in detail in all of the associated
technical documentation.
1. Technology Options and Pathways
    We begin the compliance analysis by defining the range of fuel
economy improving technologies that the CAFE Model could add to a
manufacturer's vehicles in the United States

market.83 84 85 These are technologies that we believe are
representative of what vehicle manufacturers currently use on their
vehicles, and that vehicle manufacturers could use on their vehicles in
the timeframe of the standards (MYs 2027 and beyond for the LD analysis
and MYs 2030 and beyond for the HDPUV analysis). The technology options
include basic and advanced engines, transmissions, electrification, and
road load technologies, which include mass reduction (MR), aerodynamic
improvement (AERO), and tire rolling resistance (ROLL) reduction
technologies. Note that while EPCA/EISA constrains our ability to
consider the possibility that manufacturers would comply with CAFE
standards by implementing some electrification technologies when making
decisions about the level of CAFE standards that is maximum feasible,
there are several reasons why we must accurately model the range of
available electrification technologies. These are discussed in more
detail in Section II.D and in Section V.
---------------------------------------------------------------------------

    \83\ 40 CFR 86.1806-17--Onboard diagnostics.
    \84\ 40 CFR 86.1818-12--Greenhouse gas emission standards for
light-duty vehicles, light-duty trucks, and medium-duty passenger
vehicles.
    \85\ Commission Directive 2001/116/EC--European Union emission
regulations for new LDVs--including passenger cars and light
commercial vehicles (LCV).
---------------------------------------------------------------------------

    We require several data elements to add a technology to the range
of options that the CAFE Model can consider; those elements include a
broadly applicable technology definition, estimates of how effective
that technology is at improving a vehicle's fuel economy value on a
range of vehicles (e.g., sedan through pickup truck, or HD pickup truck
and HD van), and the cost to apply that technology on a range of
vehicles. Each technology we select is designed to be representative of
a wide range of specific technology applications used in the automotive
industry. For example, in MY 2022, eleven vehicle brands under five
vehicle manufacturers \86\ used what we call a ``downsized turbocharged
engine with cylinder deactivation.'' While we might expect brands owned
by the same manufacturer to use similar technology on their engines,
among those five manufacturers, the engine systems will be very
different. Some manufacturers may also have been making those engines
longer than others, meaning that they have had more time to make the
system more efficient while also making it cheaper, as they make gains
learning the development improvement and production process. If we
chose to model the best performing, cheapest engine and applied that
technology across vehicles made by all automotive manufacturers, we
would likely be underestimating the cost and underestimating the
technology required for the entire automotive industry to achieve
higher levels of CAFE standards. The reverse would be true if we
selected a system that was less efficient and more expensive. So, in
reality, some vehicle manufacturers' systems will perform better and
cost less than our modeled systems and some will perform worse and cost
more. However, selecting representative technology definitions for our
analysis will ensure that, on balance, we capture a reasonable level of
costs and benefits that would result from any manufacturer applying the
technology.
---------------------------------------------------------------------------

    \86\ Ford, General Motors (GM), Honda, Stellantis, and VWA
represent the following 11 brands: Acura, Alfa Romeo, Audi, Bentley,
Buick, Cadillac, Chevrolet, Ford, GMC, Lamborghini, and Porsche.
---------------------------------------------------------------------------

    We have been refining the LD technology options since first
developing the CAFE Model in the early 2000s. ``Refining'' means both
adding and removing technology options depending on technology
availability now and projected future availability in the United States
market, while balancing a reasonable amount of modeling and analysis
complexity. Since the last analysis we have reduced the number of LD
ICE technology options but have refined the options, so they better
reflect the diversity of engines in the current fleet. Our technology
options also reflect an increase in diversity for hybridization and
electrification options, though we utilize these options in a manner
that is consistent with statutory constraints. In addition to better
representing the current fleet, this reflects consistent feedback from
vehicle manufacturers who have told us that they will reduce investment
in ICEs while increasing investment in hybrid and plug-in BEV
options.\87\
---------------------------------------------------------------------------

    \87\ 87 FR 25781 (May 2, 2022); Docket Submission of Ex Parte
Meetings Prior to Publication of the Corporate Average Fuel Economy
Standards for Passenger Cars and Light Trucks for Model Years 2027-
2032 and Fuel Efficiency Standards for Heavy-Duty Pickup Trucks and
Vans for Model Years 2030-2035 Notice of Proposed Rulemaking
memorandum, which can be found under References and Supporting
Material in the rulemaking Docket No. NHTSA-2023-0022.
---------------------------------------------------------------------------

    Feedback on the past several CAFE rules has also centered
thematically on the expected scope of future electrified vehicle
technologies. We have received feedback that we cannot consider BEV
options and even so, our costs underestimate BEV costs when we do
consider them in, for example, the baseline. We have also received
comments that we should consider more electrified vehicle options and
our costs overestimate future costs. Consistent with our interpretation
of EPCA/EISA, discussed further in Section V.D.1, we include several LD
electrified technologies to appropriately represent the diversity of
current and anticipated future technology options while ensuring our
analysis remains consistent with statutory limitations. In addition,
this ensures that our analysis can appropriately capture manufacturer
decision making about their vehicle fleets for reasons other than CAFE
standards (e.g., other regulatory programs and manufacturing
decisions).
    The technology options also include our judgment about which
technologies will not be available in the rulemaking timeframe. There
are several reasons why we may have concluded that it was reasonable to
exclude a technology from the options we consider. As with past
analyses, we did not include technologies unlikely to be feasible in
the rulemaking timeframe, engines technologies designed for markets
other than the United States market that are required to use unique
gasoline,\88\ or technologies where there were not appropriate data
available for the range of vehicles that we model in the analysis
(i.e., technologies that are still in the research and development
phase but are not ready for mass market production). Each technology
section below and in chapter 3 of the Draft TSD discusses these
decisions in detail.
---------------------------------------------------------------------------

    \88\ In general, most vehicles produced for sale in the United
States have been designed to use ``Regular'' gasoline, or 87 octane.
See EIA. What is Octane. Available at: https://www.eia.gov/energyexplained/gasoline/octane-in-depth.php. (Accessed: May 31,
2023), for more information.
---------------------------------------------------------------------------

    The HDPUV technology options also represent a diverse range of both
internal combustion and electrified powertrain technologies. We last
used the CAFE Model for analyzing HDPUV standards in the Phase 2
Medium- and Heavy-Duty Greenhouse Gas and Fuel Efficiency joint rules
with EPA in 2016.\89\ Since issuing that rule, we refined the ICE
technology options based on trends on vehicles in the fleet and updated
technology cost and effectiveness data. The HDPUV options also reflect
more electrification and hybridization options in that real-world
fleet. However, the HDPUV technology options are also less diverse than
the LD technology options, for several reasons.

The HDPUV fleet is significantly smaller than the LD fleet, with five
manufacturers building a little over 30 nameplates in one thousand
vehicle model configurations,\90\ compared with the almost 20 LDV
manufacturers building 369 nameplates in the range of over two thousand
configurations. Also, by definition, the HDPUV fleet only includes two
vehicle types: HD pickup trucks and work vans.\91\ These vehicle types
have focused applications, which includes transporting people and
moving equipment and supplies. As discussed in more detail below, these
vehicles are built with specific technology application, reliability,
and durability requirements in order to do work.\92\ We believe the
range of HDPUV technology options appropriately and reasonably
represents the smaller range of technology options available currently
and for application in future MYs for the United States market.
---------------------------------------------------------------------------

    \89\ 81 FR 73478 (Oct. 25, 2016); CAFE Compliance and Effects
Modeling System. 2016 Final Rule for Model Years 2021-2027 Heavy-
Duty Pickups and Vans. Available at: https://www.nhtsa.gov/corporate-average-fuel-economy/cafe-compliance-and-effects-modeling-system. (Accessed: May 31, 2023).
    \90\ In this example, a HDPUV ``nameplate'' could be the
``Sprinter 2500'', as in the Mercedes-Benz Sprinter 2500. The
vehicle model configurations are each unique variants of the
Sprinter 2500 that have an individual row in our Market Data Input
File, which are divided generally based on compliance fuel
consumption value and WF.
    \91\ For this proposal, vehicles were divided between the LD and
HDPUV fleets solely on their gross vehicle weight rating (GVWR)
being above or below 8,500 lbs. We will revisit the distribution of
vehicles in the final rule to include the the distinction for MDPVs.
    \92\ ``Work'' includes hauling, towing, carrying cargo, or
transporting people, animals, or equipment.
---------------------------------------------------------------------------

    Note, however, that for both the LD and HDPUV analyses, the CAFE
Model does not dictate or predict the technologies manufacturers must
use to comply; rather, the CAFE Model outlines a technology pathway
that manufacturers could use to meet the standards cost-effectively.
While we estimate the costs and benefits for different levels of CAFE
standards estimating technology applications that manufacturers could
use in the rulemaking timeframe, it is entirely possible and reasonable
that a vehicle manufacturer will use different technology options to
meet our standards than the CAFE Model estimates and may even use
technologies that we do not include in our analysis. This is because
our standards do not mandate the application of any particular
technology. Rather, our standards are performance based: manufacturers
can and do use a range of compliance solutions that include technology
application, shifting sales from one vehicle model or trim level to
another,\93\ and even paying civil penalties. That said, we are
confident that the 75 LD technology options and 30 HDPUV technology
options included in the analysis (in particular considering that for
each technology option, the analysis includes distinct technology cost
and effectiveness values for fourteen different types of vehicles,
resulting in about a million different technology effectiveness and
cost data points) strike a reasonable balance between the diversity of
technology used by an entire industry and simplifying reality in order
to make modeling tractable.
---------------------------------------------------------------------------

    \93\ Manufacturers could increase their production of one type
of vehicle that has higher fuel economy level, like the hybrid
version of a conventional vehicle model, to meet the standards. For
example, Ford has conventional, hybrid, and electric versions of its
F-150 pickup truck, and Toyota has conventional, hybrid, and plug-in
hybrid versions of its RAV4 sport utility vehicle.
    \94\ A detailed discussion of all the technologies listed in the
table can be found in TSD Chapter 3.
---------------------------------------------------------------------------

    Table II-1 and Table II-2 below list most of the technologies that
we used for the LD and HDPUV analyses. Each technology has a name that
loosely corresponds to its real-world technology equivalent. We
abbreviate the name to a short easy signifier for the CAFE Model to
read. We organize those technologies into groups based on technology
type: basic and advanced engines, transmissions, electrification, and
road load technologies, which include MR, aerodynamic improvement, and
low rolling resistance tire technologies.

         Table II-1--Light Duty Vehicle Technology Options \94\
------------------------------------------------------------------------
         Technology name              Abbreviation      Technology group
------------------------------------------------------------------------
Single Overhead Camshaft Engine    SOHC..............  Basic Engines.
 with VVT.
Double Overhead Camshaft Engine    DOHC..............  Basic Engines.
 with VVT.
Variable Valve Lift..............  VVL...............  Basic Engines.
Stoichiometric Gasoline Direct     SGDI..............  Basic Engines.
 Injection.
Cylinder Deactivation............  DEAC..............  Basic Engines.
Turbocharged Engine..............  TURBO0............  Advanced Engines.
Turbocharged Engine with Cooled    TURBOE............  Advanced Engines.
 Exhaust Gas Recirculation.
Turbocharged Engine with Cylinder  TURBOD............  Advanced Engines.
 Deactivation.
Advanced Turbocharged Engine,      TURBO1............  Advanced Engines.
 Level 1.
Advanced Turbocharged Engine,      TURBO2............  Advanced Engines.
 Level 2.
DOHC Engine with Advanced          ADEACD............  Advanced Engines.
 Cylinder Deactivation.
SOHC Engine with Advanced          ADEACS............  Advanced Engines.
 Cylinder Deactivation.
High Compression Ratio Engine....  HCR...............  Advanced Engines.
High Compression Ratio Engine      HCRE..............  Advanced Engines.
 with Cooled Exhaust Gas
 Recirculation.
High Compression Ratio Engine      HCRD..............  Advanced Engines.
 with Cylinder Deactivation.
Variable Compression Ratio Engine  VCR...............  Advanced Engines.
Variable Turbo Geometry Engine...  VTG...............  Advanced Engines.
Variable Turbo Geometry Engine     VTGE..............  Advanced Engines.
 with eBoost.
Turbocharged Engine with Advanced  TURBOAD...........  Advanced Engines.
 Cylinder Deactivation.
Advanced Diesel Engine...........  ADSL..............  Advanced Engines.
Advanced Diesel Engine with        DSLI..............  Advanced Engines.
 Cylinder Deactivation.
Compressed Natural Gas Engine....  CNG...............  Advanced Engines.
5-Speed Automatic Transmission...  AT5...............  Transmissions.
6-Speed Automatic Transmission...  AT6...............  Transmissions.
7-Speed Automatic Transmission     AT7L2.............  Transmissions.
 with Level 2 high efficiency
 gearbox (HEG).
8-Speed Automatic Transmission...  AT8...............  Transmissions.
8-Speed Automatic Transmission     AT8L2.............  Transmissions.
 with Level 2 HEG.
8-Speed Automatic Transmission     AT8L3.............  Transmissions.
 with Level 3 HEG.
9-Speed Automatic Transmission     AT9L2.............  Transmissions.
 with Level 2 HEG.
10-Speed Automatic Transmission    AT10L2............  Transmissions.
 with Level 2 HEG.
10-Speed Automatic Transmission    AT10L3............  Transmissions.
 with Level 3 HEG.
6-Speed Dual Clutch Transmission.  DCT6..............  Transmissions.

8-Speed Dual Clutch Transmission.  DCT8..............  Transmissions.
Continuously Variable              CVT...............  Transmissions.
 Transmission.
Continuously Variable              CVTL2.............  Transmissions.
 Transmission with Level 2 HEG.
Conventional Powertrain (Non-      CONV..............  Electrification.
 Electric).
12V Micro-Hybrid Start-Stop        SS12V.............  Electrification.
 System.
48V Belt Mounted Integrated        BISG..............  Electrification.
 Starter/Generator.
Parallel Strong Hybrid/Electric    P2D...............  Electrification.
 Vehicle with DOHC Engine.
Parallel Strong Hybrid/Electric    P2SGDID...........  Electrification.
 Vehicle with DOHC+SGDI Engine.
Parallel Strong Hybrid/Electric    P2S...............  Electrification.
 Vehicle with SOHC Engine.
Parallel Strong Hybrid/Electric    P2SGDIS...........  Electrification.
 Vehicle with SOHC+SGDI Engine.
Parallel Strong Hybrid Electric    P2TRB0............  Electrification.
 Vehicle with TURBO0 Engine.
Parallel Strong Hybrid Electric    P2TRBE............  Electrification.
 Vehicle with TURBOE Engine.
Parallel Strong Hybrid Electric    P2TRB1............  Electrification.
 Vehicle with TURBO1 Engine.
Parallel Strong Hybrid Electric    P2TRB2............  Electrification.
 Vehicle with TURBO2 Engine.
Parallel Strong Hybrid Electric    P2HCR.............  Electrification.
 Vehicle with HCR Engine.
Parallel Strong Hybrid Electric    P2HCRE............  Electrification.
 Vehicle with HCRE Engine.
Power Split Strong Hybrid/         SHEVPS............  Electrification.
 Electric Vehicle with Full Time
 Atkinson Engine.
Plug-in Hybrid Vehicle with        PHEV20T...........  Electrification.
 TURBO1 Engine and 20 miles of
 electric range.
Plug-in Hybrid Vehicle with        PHEV50T...........  Electrification.
 TURBO1 Engine and 50 miles of
 electric range.
Plug-in Hybrid Vehicle with HCR    PHEV20H...........  Electrification.
 Engine and 20 miles of electric
 range.
Plug-in Hybrid Vehicle with HCR    PHEV50H...........  Electrification.
 Engine and 50 miles of electric
 range.
Plug-in Hybrid Vehicle with Full   PHEV20PS..........  Electrification.
 Time Atkinson Engine and 20
 miles of electric range.
Plug-in Hybrid Vehicle with Full   PHEV50PS..........  Electrification.
 Time Atkinson Engine and 50
 miles of electric range.
Battery Electric Vehicle with 200  BEV1..............  Electrification.
 miles of range.
Battery Electric Vehicle with 250  BEV2..............  Electrification.
 miles of range.
Battery Electric Vehicle with 300  BEV3..............  Electrification.
 miles of range.
Battery Electric Vehicle with 350  BEV4..............  Electrification.
 miles of range.
Fuel Cell Vehicle................  FCV...............  Electrification.
Baseline Tire Rolling Resistance.  ROLL0.............  Rolling
                                                        Resistance.
Tire Rolling Resistance, 10%       ROLL10............  Rolling
 Improvement.                                           Resistance.
Tire Rolling Resistance, 20%       ROLL20............  Rolling
 Improvement.                                           Resistance.
Tire Rolling Resistance, 30%       ROLL30............  Rolling
 Improvement.                                           Resistance.
Baseline Aerodynamic Drag          AERO0.............  Aerodynamic Drag.
 Technology.
Aerodynamic Drag, 5% Drag          AERO5.............  Aerodynamic Drag.
 Coefficient Reduction.
Aerodynamic Drag, 10% Drag         AERO10............  Aerodynamic Drag.
 Coefficient Reduction.
Aerodynamic Drag, 15% Drag         AERO15............  Aerodynamic Drag.
 Coefficient Reduction.
Aerodynamic Drag, 20% Drag         AERO20............  Aerodynamic Drag.
 Coefficient Reduction.
Baseline Mass Reduction            MR0...............  Mass Reduction.
 Technology.
Mass Reduction--5.0% of Glider...  MR1...............  Mass Reduction.
Mass Reduction--7.5% of Glider...  MR2...............  Mass Reduction.
Mass Reduction--10.0% of Glider..  MR3...............  Mass Reduction.
Mass Reduction--15.0% of Glider..  MR4...............  Mass Reduction.
Mass Reduction--20.0% of Glider..  MR5...............  Mass Reduction.
------------------------------------------------------------------------

   Table II-2--Heavy-Duty Pickup Truck and Van Technology Options \95\
------------------------------------------------------------------------
         Technology name              Abbreviation      Technology group
------------------------------------------------------------------------
Single Overhead Camshaft Engine    SOHC..............  Basic Engines.
 with VVT.
Double Overhead Camshaft Engine    DOHC..............  Basic Engines.
 with VVT.
Stoichiometric Gasoline Direct     SGDI..............  Basic Engines.
 Injection.
Cylinder Deactivation............  DEAC..............  Basic Engines.
Turbocharged Engine..............  TURBO0............  Advanced Engines.
Advanced Diesel Engine...........  ADSL..............  Advanced Engines.
Advanced Diesel Engine with        DSLI..............  Advanced Engines.
 Improvements.
5-Speed Automatic Transmission...  AT5...............  Transmissions.
6-Speed Automatic Transmission...  AT6...............  Transmissions.
8-Speed Automatic Transmission...  AT8...............  Transmissions.
9-Speed Automatic Transmission     AT9L2.............  Transmissions.
 with Level 2 HEG.
10-Speed Automatic Transmission    AT10L2............  Transmissions.
 with Level 2 HEG.
Conventional Powertrain (Non-      CONV..............  Electrification.
 Electric).
12V Micro-Hybrid Start-Stop        SS12V.............  Electrification.
 System.
Belt Mounted Integrated Starter/   BISG..............  Electrification.
 Generator.
Parallel Strong Hybrid/Electric    P2S...............  Electrification.
 Vehicle with SOHC Engine.         (P2D, P2TRB0).....
Plug-in Hybrid Vehicle with Basic  PHEV50H...........  Electrification.
 Engine and 50 miles of electric   (PHEV50T).........
 range.
Battery Electric Vehicle with 150  BEV1..............  Electrification.
 miles of range (for van classes)
 or 200 miles of range (for
 pickup classes).
Battery Electric Vehicle with 250  BEV2..............  Electrification.
 miles of range (for van classes)
 or 300 miles of range (for
 pickup classes).

Fuel Cell Vehicle................  FCV...............  Electrification.
Baseline Tire Rolling Resistance.  ROLL0.............  Rolling
                                                        Resistance.
Tire Rolling Resistance, 10%       ROLL10............  Rolling
 Improvement.                                           Resistance.
Tire Rolling Resistance, 20%       ROLL20............  Rolling
 Improvement.                                           Resistance.
Baseline Aerodynamic Drag          AERO0.............  Aerodynamic Drag.
 Technology.
Aerodynamic Drag, 10% Drag         AERO10............  Aerodynamic Drag.
 Coefficient Reduction.
Aerodynamic Drag, 20% Drag         AERO20............  Aerodynamic Drag.
 Coefficient Reduction.
Baseline Mass Reduction            MR0...............  Mass Reduction.
 Technology.
Mass Reduction--1.4% of Glider...  MR1...............  Mass Reduction.
Mass Reduction--13.0% of Glider..  MR2...............  Mass Reduction.
------------------------------------------------------------------------

    We then organize the groups into pathways. The pathways instruct
the CAFE Model how and in what order to apply technology. In other
words, the pathways define technologies that are mutually exclusive
(i.e., that cannot be applied at the same time), and define the
direction in which vehicles can advance as the model evaluates which
technologies to apply. Figure II-6 shows the LD and HDPUV technology
pathways used in this analysis. In general, the paths are tied to ease
of implementation of additional technology and how closely related the
technologies are.
---------------------------------------------------------------------------

    \95\ A detailed discussion of all the technologies listed in the
table can be found in TSD Chapter 3.
---------------------------------------------------------------------------

BILLING CODE 4910-59-P

[GRAPHIC] [TIFF OMITTED] TP17AU23.014

BILLING CODE 4910-59-C
    As an example, our ``Turbo Engine Path'' consists of five different
engine technologies that employ different levels of turbocharging
technology. A

turbocharger is essentially a small turbine that is driven by exhaust
gases produced by the engine. As these gases flow through the
turbocharger, they spin the turbine, which in turn spins a compressor
that pushes more air into an engine's cylinder. Having more air in the
engine's cylinder allows the engine to burn more fuel, which then
creates more power, without needing a physically larger engine. In our
analysis, an engine that uses a turbocharger ``downsizes,'' or becomes
smaller. The smaller engine can use less fuel to do the same amount of
work as the engine did before it used a turbocharger and was downsized.
Allowing basic engines to be downsized and turbocharged instead of just
turbocharged keeps the vehicle's utility and performance constant so
that we can measure the costs and benefits of different levels of fuel
economy improvements, rather than the change in different vehicle
attributes. This concept is discussed further, below.
    Grouping technologies on pathways also tells the model how to
evaluate technologies; continuing this example, a vehicle can only have
one engine, so if a vehicle has one of the Turbo engines the model will
evaluate which more advanced Turbo technology to apply. Or, if it is
more cost-effective to go beyond the Turbo pathway, the model will
evaluate whether to apply more advanced engine technologies and
hybridization path technology.
    Then, the arrows between technologies instruct the model on the
order in which to evaluate technologies on a pathway. This ensures that
a vehicle that uses a more fuel-efficient technology cannot downgrade
to a less efficient option or that a vehicle would switch to technology
that was significantly technically different. As an example, if a
vehicle in the compliance simulation begins with a TURBOD engine--a
turbocharged engine with cylinder deactivation--it cannot adopt a
TURBO0 engine. Similarly, this vehicle with a TURBOD engine cannot
adopt an ADEACD engine.\96\ The model follows instructions pursuant to
the direction of arrows between technology groups and between
technologies on the same pathway.
---------------------------------------------------------------------------

    \96\ An engine could potentially be changed from TURBO0 to
TURBO2 without redesigning the engine block or requiring
significantly different expertise to design and implement. A change
to ADEACD would likely require a different engine block that might
not be possible to fit in the engine bay of the vehicle without a
complete redesign and different technical expertise requiring years
of research and development. This consideration which would strand
capital and break parts sharing is why the advanced engine paths
restrict most movement between them.
---------------------------------------------------------------------------

    We also consider two categories of technology that we could not
simulate as part of the CAFE Model's technology pathways. ``Off-cycle''
and air conditioning (AC) efficiency technologies improve vehicle fuel
economy, but the benefit of those technologies cannot be captured using
the fuel economy test methods that we must use under EPCA/EISA.\97\ As
an example, manufacturers can claim a benefit for technology like
active seat ventilation and solar reflective surface coatings that make
the cabin of a vehicle more comfortable for the occupants, who then do
not have to use other less efficient accessories like heat or AC.
Instead of including off-cycle and AC efficiency technologies in the
technology pathways, we include the improvement as a defined benefit
that gets applied to a manufacturer's entire fleet instead of to
individual vehicles. The defined benefit that each manufacturer
receives in the analysis for using off-cycle and AC efficiency
technology on their vehicles is located in the Market Data Input file.
See Chapter 3.7 of the Draft TSD for more discussion in how off-cycle
and AC efficiency technologies are developed and modeled.
---------------------------------------------------------------------------

    \97\ See 49 U.S.C. 32904(c) (``Testing and calculation
procedures. . . . the Administrator shall use the same procedures
for passenger automobiles the Administrator used for model year 1975
(weighted 55 percent urban cycle and 45 percent highway cycle), or
procedures that give comparable results.'').
---------------------------------------------------------------------------

    To illustrate, throughout this section we will follow the
hypothetical vehicle mentioned above that begins the compliance
simulation with a TURBOD engine. Our hypothetical vehicle, Generic
Motors' Ravine Runner F Series, is a roomy, top of the line sport
utility vehicle (SUV). The Ravine Runner F Series starts the compliance
simulation with technologies from most technology pathways;
specifically, after looking at Generic Motors' website and marketing
materials, we determined that it has technology that loosely fits
within the following technologies that we consider in the CAFE Model:
it has a turbocharged engine with cylinder deactivation, a fairly
advanced 10-speed automatic transmission, a 12V start-stop system, the
least advanced tire technology, a fairly aerodynamic vehicle body, and
it employs a fairly advanced level of MR. We track the technologies on
each vehicle using a ``technology key'', which is the string of
technology abbreviations for each vehicle. Again, the vehicle
technologies and their abbreviations that we consider in this analysis
are shown in Table II-1 and Table II-2 above. The technology key for
the Ravine Runner F Series is ``TURBOD; AT10L2, SS12V; ROLL0; AERO5;
MR3.''
2. Defining the Technology Baseline
    The Market Data Input File is one of four Excel input files that
the CAFE Model uses for compliance and effects simulation. The Market
Data Input file's ``Vehicles'' tab (or worksheet) houses one of the
most significant compilations of technology inputs and assumptions in
the analysis, which is a characterization of a baseline fleet of
vehicles to which the CAFE Model adds fuel-economy-improving
technology. We call this fleet the ``baseline fleet'' or the ``analysis
fleet.'' The baseline fleet includes a number of inputs necessary for
the model to add fuel economy improving technology to each vehicle for
the compliance analysis and to calculate the resulting impacts for the
effects analysis.
    There is one Microsoft Excel file row for each vehicle model, for
LD with the same certification fuel economy value and vehicle
footprint, and for HDPUV with the same certification fuel consumption
and WF. This means that vehicle models with different configurations
that affect the vehicle's certification fuel economy or fuel
consumption value--for example, our Ravine Runner example vehicle comes
in three different configurations, the Ravine Runner FWD, Ravine Runner
AWD, and Ravine Runner F Series--will be separated into three rows in
the Vehicles tab. In each row we also designate a vehicle's engine,
transmission, and platform codes.\98\ Vehicles that have the same
engine, transmission, or platform code are deemed to ``share'' that
component in the CAFE Model. Parts sharing helps manufacturers achieve
economies of scale, deploy capital efficiently, and make the most of
shared research and development expenses, while still presenting a wide
array of consumer choices to the market. The CAFE Model was developed
to treat vehicles, platforms, engines, and transmissions as separate
entities, which allows the modeling system to concurrently evaluate
technology improvements on multiple vehicles that may share a

common component. Sharing also enables realistic propagation, or
``inheriting,'' of previously applied technologies from an upgraded
component down to the vehicle ``users'' of that component that have not
yet realized the benefits of the upgrade. For additional information
about the initial state of the fleet and technology evaluation and
inheriting within the CAFE Model, please see Section 2.1 and Section
4.4 of the Draft CAFE Model Documentation.
---------------------------------------------------------------------------

    \98\ Each numeric engine, transmission, or platform code
designates important information about that vehicle's technology;
for example, a vehicle's six-digit Transmission Code includes
information about the manufacturer, the vehicle's drive
configuration (i.e., front-wheel drive, all-wheel drive, four-wheel
drive, or rear-wheel drive), transmission type, number of gears
(e.g., a 6-speed transmission has six gears), and the transmission
variant.
---------------------------------------------------------------------------

    Figure II-7 below shows how we separate the different
configurations of the Ravine Runner. We can see by the Platform Codes
that these Ravine Runners all share the same platform, but only the
Ravine Runner FWD and Ravine Runner AWD share an engine. Even so, all
three certification fuel economy values are different, which is common
of vehicles that differ in drive type (drive type meaning whether the
vehicle has all-wheel drive (AWD), four-wheel drive (4WD), front-wheel
drive (FWD), or rear-wheel drive). While it would certainly be easier
to aggregate vehicles by model, ensuring that we capture model variants
with different fuel economy values improves the accuracy of our
analysis and the potential that our estimated costs and benefits from
different levels of standards are appropriate. We include information
about other vehicle technologies at the farthest right side of the
Vehicles tab, and in the ``Engines'', ``Transmissions'', and
``Platforms'' worksheets, as discussed further below.
[GRAPHIC] [TIFF OMITTED] TP17AU23.015

    Moving from left to right on the Vehicles tab, after including
general information about vehicles and their compliance fuel economy
value, we include sales and manufacturer's suggested retail price
(MSRP) data, regulatory class information (i.e., domestic passenger
car, import passenger car, light truck, MDPV, HD pickup truck, or HD
van), and information about how we classify vehicles for the
effectiveness and safety analyses. Each of these data points is
important to different parts of the compliance and effects analysis, so
that the CAFE Model can accurately average the technologies required
across a manufacturer's regulatory class for each class to meet its
CAFE standard, or the impacts of higher fuel economy standards on
vehicle sales. In addition, we include columns indicating if a vehicle
is a ``ZEV Candidate,'' which means that the vehicle could be made into
a zero emissions vehicle (ZEV) at its first redesign opportunity in
order to simulate a manufacturer's compliance with California's ACC,
ACC II, or ACT program, which is discussed further below. Next, we
include vehicle information necessary for applying different types of
technology; for example, designating a vehicle's body style means that
we can appropriately apply aerodynamic technology, and designating
starting curb weight values means that we can more accurately apply MR
technology. Importantly, this section also includes vehicle footprint
data (because we set footprint-based standards).
---------------------------------------------------------------------------

    \99\ Note that not all data columns are shown in this example
for brevity.
---------------------------------------------------------------------------

    We also set product design cycles, which are the years when the
CAFE Model can apply different technologies

to vehicles. Manufacturers often introduce fuel saving technologies at
a ``redesign'' of their product or adopt technologies at ``refreshes''
in between product redesigns. As an example, the redesigned third
generation Chevrolet Silverado was released for the 2019 MY, and
featured a new platform, updated drivetrain, increased towing capacity,
reduced weight, improved safety and expanded trim levels, to name a few
improvements. For MY 2022, the Chevrolet Silverado received a refresh
(or facelift as it is commonly called), with an updated interior,
infotainment, and front-end appearance.\100\
---------------------------------------------------------------------------

    \100\ GM Authority. 2022 Chevy Silverado. Available at: https://gmauthority.com/blog/gm/chevrolet/silverado/2022-chevrolet-silverado/. (Accessed May 31, 2023).
---------------------------------------------------------------------------

    During modeling, all improvements from technology application are
initially realized on a component and then propagated (or inherited)
down to the vehicles that share that component. As such, new component-
level technologies are initially evaluated and applied to a platform,
engine, or transmission during their respective redesign or refresh
years. Any vehicles that share the same redesign and/or refresh
schedule as the component apply these technology improvements during
the same MY. The rest of the vehicles inherit technologies from the
component during their refresh or redesign year (for engine- and
transmission-level technologies), or during a redesign year only (for
platform-level technologies). Please see Section 4.4 of the Draft CAFE
Model Documentation for additional information about technology
evaluation and inheriting within the CAFE Model.
    The CAFE Model also considers the potential safety effect of MR
technologies and crash compatibility of different vehicle types. MR
technologies lower the vehicle's curb weight, which may change crash
compatibility and safety, depending on the type of vehicle. We assign
each vehicle in the Market Data Input File a ``safety class'' that best
aligns with the CAFE Model's analysis of vehicle mass, size, and
safety, and include the vehicle's baseline curb weight.\101\
---------------------------------------------------------------------------

    \101\ Vehicle curb weight is the weight of the vehicle with all
fluids and components but without the drivers, passengers, and
cargo.
---------------------------------------------------------------------------

    The CAFE Model includes procedures to consider the direct labor
impacts of manufacturers' response to CAFE regulations, considering the
assembly location of vehicles, engines, and transmissions, the percent
U.S. content (that reflects percent U.S. and Canada content), and the
dealership employment associated with new vehicle sales. Baseline labor
information, by vehicle, is included in the Market Data Input File.
Sales volumes included in and adapted from the market data also
influence total estimated direct labor projected in the analysis. See
Chapter 6.2.5 of the Draft TSD for further discussion of the labor
utilization analysis.
    Then we assign the CAFE Model's range of technologies to individual
vehicles. This initial linkage of vehicle technologies is how the CAFE
Model knows how to advance a vehicle down each technology pathway.
Assigning CAFE Model technologies to individual vehicles is dependent
on the mix of information we have about any particular vehicle and
trends about how a manufacturer has added technology to that vehicle in
the past, equations and models that translate real-world technologies
to their counterparts in our analysis, and our engineering judgment.
    As discussed further below, we use information directly from
manufacturers to populate some fields in the Market Data Input file,
like vehicle horsepower ratings and vehicle weight. We also use
manufacturer data as an input to various other models that calculate
how a manufacturer's real-world technology equates to a technology
level in our model. For example, we calculate MR, aerodynamic drag
reduction, and ROLL baseline levels by looking at industry-wide trends
and calculating--through models or equations--levels of improvement for
each technology. The models and algorithms that we use are described
further below and in detail in Chapter 3 of the Draft TSD. Other
fields, like vehicle refresh and redesign years, are projected forward
based on historic trends.
    Let us return to the Ravine Runner F Series with the technology key
``TURBOD; AT10L2, SS12V; ROLL0; AERO5; MR3.'' Generic Motor's publicly
available spec sheet for the Ravine Runner F Series says that the
Ravine Runner F Series uses Generic Motor's Turbo V6 engine with
proprietary Adaptive Cylinder Management Engine (ACME) technology. ACME
improves fuel economy and lowers emissions by operating the engine
using only three of the engine's cylinders in most conditions and using
all six engine cylinders when more power is required. Generic Motors
uses this engine in several of their vehicles, and the specifications
of the engine can be found in the Engines Tab of the Market Data Input
File, under a six-digit engine code.\102\
---------------------------------------------------------------------------

    \102\ Like the Transmission Codes discussed above, the Engine
Codes include information identifying the manufacturer, engine
displacement (i.e., how many liters the engine is), whether the
engine is naturally aspirated or force inducted (e.g.,
turbocharged), and whether the engine has any other unique
attributes.
---------------------------------------------------------------------------

    This is a relatively easy engine to assign based on publicly
available specification sheets, but some technologies are much more
difficult to assign. Manufacturers use different trade names or terms
for different technology, and the way that we assign the technology in
our analysis may not necessarily line up with how a manufacturer
describes the technology. We must use some engineering judgment to
determine how discrete technologies in the market best fit the
technology options that we consider in our analysis. We discuss factors
that we use to assign each vehicle technology in the individual
technology subsections below.
    In addition to the Vehicles Tab that houses the baseline fleet, the
Market Data input file includes information that affects how the CAFE
Model might apply technology to vehicles in the compliance simulation.
Specifically, the Market Data Input file's ``Manufacturers'' tab
includes a list of vehicle manufacturers considered in the analysis and
several pieces of information about their economic and compliance
behavior. First, we determine if a manufacturer ``prefers fines,''
meaning that historically in the LD fleet, we have observed this
manufacturer paying civil penalties for failure to meet CAFE
standards.\103\ We might designate a manufacturer as not preferring
fines if, for example, they have told us that paying civil penalties
would be a violation of provisions in their corporate charter. For this
analysis, we assume that all manufacturers are willing to pay fines in
MYs 2022-2026, and that in MY 2027 and beyond, only the manufacturers
that have historically paid fines would continue to pay fines. We seek
comment on these fine payment preference assumptions. Note however
that, as further discussed below in regard to the CAFE Model's
compliance simulation algorithm, the model will still apply
technologies for these manufacturers if it is cost-effective to do so,
defined by several variables discussed below in Section II.C.6.
---------------------------------------------------------------------------

    \103\ See 49 U.S.C. 32912.
---------------------------------------------------------------------------

    Next, we designate a ``payback period'' for each manufacturer. The
payback period represents an assumption that consumers are willing to
buy vehicles with more fuel economy

technology because the fuel economy technology will save them money on
gas in the long run. For the past several CAFE Model analyses we have
assumed that in the absence of CAFE or other regulatory standards,
manufacturers would apply technology that ``pays for itself''--by
saving the consumer money on fuel--in 2.5 years. While the amount of
technology that consumers are willing to pay for is subject to much
debate, we assume a 2.5-year payback period based on what manufacturers
have told us they do, and on estimates in the available literature.
This is discussed in detail in Section II.E below, and in the Draft TSD
and PRIA.
    We also designate in the Market Data Input file the percentage of
each manufacturer's sales that must meet CAA section 177 requirements
in certain states. Section 209(a) of the CAA generally preempts states
from adopting emission control standards for new motor vehicles;
however, Congress created an exemption program in section 209(b) that
allows the State of California to seek a waiver of preemption. EPA must
grant the waiver unless the Agency makes one of three statutory
findings.\104\ Under CAA section 177, other States can adopt and
enforce standards identical those approved under California's section
209(b) waiver.
---------------------------------------------------------------------------

    \104\ See 87 FR 14332 (March 14, 2022). (``The CAA section
209(b) waiver is limited ``to any State which has adopted standards
. . . for the control of emissions from new motor vehicles or new
motor vehicle engines prior to March 30, 1966,'' and California is
the only State that had standards in place before that date.'').
---------------------------------------------------------------------------

    Finally, we include estimated CAFE compliance credit banks for each
manufacturer in several years through 2021, which is the year before
the compliance simulation begins. The CAFE Model does not explicitly
simulate credit trading between and among vehicle manufacturers, but we
estimate how manufacturers might use compliance credits in early MYs.
This reflects manufacturers' tendency to use regulatory credits rather
than to apply technology.\105\
---------------------------------------------------------------------------

    \105\ Note, this is just an observation about manufacturers'
tendency to use regulatory credits rather than to apply technology;
in accordance with 49 U.S.C. 32902(h), the CAFE Model does not
simulate a manufacturer's potential credit use during the years for
which we are setting new CAFE standards.
---------------------------------------------------------------------------

    Before we begin building the Market Data Input file for any
analysis, we must consider what MY vehicles will comprise the baseline
fleet. There is an inherent time delay in the data we can use for any
particular analysis because we must set LD CAFE standards at least 18
months in advance of a MY if the CAFE standards increase,\106\ and
HDPUV fuel efficiency standards at least 4 full MYs in advance if the
standards increase.\107\ In addition to the requirement to set
standards at least 18 months in advance of a MY, we must propose
standards with enough time to allow the public to comment on the
proposed standards and meaningfully evaluate that feedback and
incorporate it into the final rule in accordance with the APA.\108\
This means that the most recent data we have available to generate the
baseline fleet necessarily falls behind the MY fleets of vehicles for
which we generate standards. We have historically and intend again to
update the data we use for the baseline fleet for the final rule if we
receive more recent, high-quality data in time to use it for the final
rule.
---------------------------------------------------------------------------

    \106\ 49 U.S.C. 32902(a).
    \107\ 49 U.S.C. 32902(k)(3)(A).
    \108\ 5 U.S.C. 553.
---------------------------------------------------------------------------

    Using recent data for the baseline is more likely to reflect the
current vehicle fleet than older data. Recent data will inherently
include manufacturer's decisions on what fuel-economy-improving
technology to apply, mix shifts in response to consumer preferences
(e.g., more recent data reflects manufacturer and consumer preference
towards larger vehicles),\109\ and industry sales volumes that
incorporate substantive macroeconomic events (e.g., the impact of the
Coronavirus disease of 2019 (COVID) or microchip shortages). We
considered that using a baseline fleet year that has been impacted by
these transitory shocks may not represent trends in future years;
however, on balance, we believe that updating to using the most
complete set of available fleet data provides the most accurate
baseline for the CAFE Model to calculate compliance and effects of
different levels of future fuel economy standards. Also, using recent
data decreases the likelihood that the CAFE Model selects compliance
pathways for future standards that affect vehicles already built-in
previous MYs.\110\
---------------------------------------------------------------------------

    \109\ See the 2022 EPA Automotive Trends Report at pg. 14-19.
    \110\ For example, in this analysis the CAFE Model must apply
technology to the MY 2022 fleet from MYs 2023-2026 for the
compliance simulation that begins in MY 2027 (for the light-duty
fleet), and from MYs 2023-2029 for the compliance simulation that
begins in MY 2030 (for the HDPUV fleet). While manufacturers have
already built MY 2022 and later vehicles, the most current, complete
dataset with regulatory fuel economy test results to build the
analysis fleet at the time of writing remains MY 2022 data for the
light-duty fleet, and a range of MYs between 2014 and 2022 for the
HDPUV fleet.
---------------------------------------------------------------------------

    At the time we start building the baseline fleet, data that we
receive from vehicle manufacturers in accordance with EPCA/EISA,\111\
and our CAFE compliance regulations \112\ in advance of or during an
ongoing MY, offers the best snapshot of vehicles for sale in the US in
a MY. These pre-model year (PMY) and mid-model year (MMY) reports
include information about individual vehicles at the vehicle
configuration level. We use the vehicle configuration, certification
fuel economy, sales, regulatory class, and some additional technology
data from these reports as the starting point to build a ``row'' (i.e.,
a vehicle configuration, with all necessary information about the
vehicle) in the Market Data Input File's Vehicle's Tab. Additional
technology data come from publicly available information, including
vehicle specification sheets, manufacturer press releases, owner's
manuals, and websites. We also generate some assumptions in the Market
Data Input file for data fields where there is limited data, like
refresh and redesign cycles for future MYs, and technology levels for
certain road load reduction technologies like MR and aerodynamic drag
reduction.
---------------------------------------------------------------------------

    \111\ 49 U.S.C. 32907(a)(2).
    \112\ 49 CFR part 537.
---------------------------------------------------------------------------

    For this analysis, the LD baseline fleet consists of every vehicle
model in MY 2022 in mostly every configuration that has a different
compliance fuel economy value, which results in a little over 2,000
individual rows in the Vehicles Tab of the Market Data Input file. The
HDPUV fleet consists of vehicles produced in between MYs 2014 and 2022,
which results in a little over 1100 individual rows in the HDPUV Market
Data Input file. We used a combination of MY data for that fleet
because of data availability, but the resulting dataset is a robust
amalgamation that provides a reasonable starting point for the much
smaller fleet.
    The next section discusses how our analysis evaluates how adding
additional fuel-economy-improving technology to a vehicle in the
baseline fleet will improve that vehicle's fuel economy value. Put
another way, the next section answers the question, how do we estimate
how effective any given technology is at improving a vehicle's fuel
economy value?
3. Technology Effectiveness Values
    How does the CAFE Model know how effective any particular
technology is at improving a vehicle's fuel economy value? Accurate
technology effectiveness estimates require information about: (1) the
vehicle type and size; (2) the other technologies on the vehicle and/or
being added to the

vehicle at the same time; and (3) and how the vehicle is driven. Any
oversimplification of these complex factors could make the
effectiveness estimates less accurate.
    To build a database of technology effectiveness estimates that
includes these factors, we partner with the DOE's ANL. ANL has
developed and maintains a physics-based full-vehicle modeling and
simulation tool called Autonomie that generates technology
effectiveness estimates for the CAFE Model.
    What is physics-based full-vehicle modeling and simulation? A model
is a mathematical representation of a system, and simulation is the
behavior of that mathematical representation over time. In Autonomie,
the model is a mathematical representation of an entire vehicle,
including its individual technologies such as the engine and
transmission, overall vehicle characteristics such as mass and
aerodynamic drag, and the environmental conditions, such as ambient
temperature and barometric pressure.
    We simulate a vehicle model's behavior over the ``two-cycle'' tests
that are used to measure vehicle fuel economy.\113\ For readers
unfamiliar with this process, measuring a vehicle's fuel economy on the
two-cycle tests is like running a car on a treadmill following a
program--or more specifically, two programs. The ``programs'' are the
``urban cycle,'' or Federal Test Procedure (abbreviated as ``FTP''),
and the ``highway cycle,'' or Highway Fuel Economy Test (abbreviated as
``HFET''). Figure II-8 below shows the FTP ``program''; the vehicle
meets certain speeds at certain times during the test, or in technical
terms, the vehicle must follow the designated ``speed trace.'' The FTP
is meant roughly to simulate stop and go city driving, and the HFET is
meant roughly to simulate steady flowing highway driving at about 50
miles per hour (mph). We also use the Society of Automotive Engineers
(SAE) recommended practices to simulate hybridized and EV drive
cycles,\114\ which involves the test cycles mentioned above and
additional test cycles to measure battery energy consumption and range.
---------------------------------------------------------------------------

    \113\ We are statutorily required to use the two-cycle tests to
measure vehicle fuel economy in the CAFE program. See 49 U.S.C.
32904(c) (``Testing and calculation procedures. . . . the
Administrator shall use the same procedures for passenger
automobiles the Administrator used for model year 1975 (weighted 55
percent urban cycle and 45 percent highway cycle), or procedures
that give comparable results.'').
    \114\ SAE. Recommended Practice for Measuring the Exhaust
Emissions and Fuel Economy of Hybrid-Electric Vehicles, Including
Plug-in Hybrid Vehicles. SAE Standard J1711. Rev. Feb 2023.; and
SAE. Battery Electric Vehicle Energy Consumption and Range Test
Procedure. SAE Standard J1634. Rev. April 2021.
[GRAPHIC] [TIFF OMITTED] TP17AU23.016

    Measuring every vehicle's fuel economy values using the same test
cycles (and in the real world, using sophisticated test and measurement
equipment including dynamometers, carefully controlled environmental
conditions, and precise procedures) ensures that the fuel economy
certification results are repeatable for

each vehicle model, and comparable among all of the different vehicle
models.
---------------------------------------------------------------------------

    \115\ EPA. Emissions Standards Reference Guide. EPA FTP.
Available at: https://www.epa.gov/emission-standards-reference-guide/epa-federal-test-procedure-ftp. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    Finally, ``physics-based'' simply refers to the mathematical
equations underlying the modeling and simulation--the simulated vehicle
models and all of the sub-models that make up specific vehicle
components and the calculated fuel used on simulated test cycles are
calculated mathematical equations that conform to the laws of physics.
    Full-vehicle modeling and simulation was initially developed to
avoid the costs of designing and testing prototype parts for every new
type of technology. For example, Generic Motors can use physics-based
computer modeling to determine the fuel economy penalty for adding a
4WD, rugged off-road tire trim level of the Ravine Runner to its
lineup. The Ravine Runner, modeled with its new drivetrain and off-road
tires, can be simulated on a defined test route and under defined test
conditions and compared against the baseline Ravine Runner simulated
without the change. Full-vehicle modeling and simulation allows Generic
Motors to consider and evaluate different designs and concepts before
building a single prototype for any potential technology change.
    Full vehicle modeling and simulation is also essential to measuring
how all technologies on a vehicle interact. An analysis using single or
limited point estimates may assume that, for example, one technology
may improve the vehicle's fuel economy by 5% and a second technology
may improve the vehicle's fuel economy by 10%, but when both
technologies are added to the vehicle together, they achieve a 15%
improvement. Single point estimates generally do not provide accurate
effectiveness values because they do not capture complex relationships
among technologies. Technology effectiveness often differs
significantly depending on the vehicle type (e.g., sedan versus pickup
truck) and the way in which the technology interacts with other
technologies on the vehicle, as different technologies may provide
different incremental levels of fuel economy improvement if implemented
alone or in combination with other technologies. As stated above, any
oversimplification of these complex factors could lead to less accurate
technology effectiveness estimates.
    In addition, because manufacturers often add several fuel-saving
technologies simultaneously when redesigning a vehicle, it is difficult
to isolate the effect of adding any one individual technology to the
full vehicle system. Modeling and simulation offer the opportunity to
isolate the effects of individual technologies by using a single or
small number of baseline vehicle configurations and incrementally
adding technologies to those baseline configurations. This provides a
consistent reference point for the incremental effectiveness estimates
for each technology and for combinations of technologies for each
vehicle type. Vehicle modeling also reduces the potential for
overcounting or undercounting technology effectiveness.
    ANL does not build an individual vehicle model for every single
vehicle configuration in our LD and HDPUV Market Data Input files. This
would be nearly impossible, because Autonomie requires very detailed
data on hundreds of different vehicle attributes (like the weight of
the vehicle's fuel tank, the weight of the vehicle's transmission
housing, the weight of the engine, the vehicle's 0-60 mph time, and so
on) to build a vehicle model, and for practical reasons we cannot
acquire 4000 vehicles and obtain these measurements every time we
promulgate a new rule (and we cannot acquire vehicles that have not yet
been built). Rather, ANL builds a discrete number of vehicle models
that are representative of large portions of vehicles in the real
world. We refer to the vehicle model's type and performance level as
the vehicle's ``technology class.'' By assigning each vehicle in the
Market Data Input file a ``technology class,'' we can connect it to the
Autonomie effectiveness estimate that best represents how effective the
technology would be on the vehicle, taking into account vehicle
characteristics like type and performance metrics. Because each vehicle
technology class has unique characteristics, the effectiveness of
technologies and combinations of technologies is different for each
technology class.
    There are ten technology classes for the LD analysis: small car
(SmallCar), small performance car (SmallCarPerf), medium car (MedCar),
medium performance car (MedCarPerf), small SUV (SmallSUV), small
performance SUV (SmallSUVPerf), medium SUV (MedSUV), medium performance
SUV (MedSUVPerf), pickup truck (Pickup), and high towing pickup truck
(PickupHT). There are four technology classes for the HDPUV analysis,
based on the vehicle's ``weight class.'' An HDPUV that weighs between
8,501 and 10,000 pounds is in ``Class 2b,'' and an HDPUV that weighs
between 10,001 and 14,000 pounds is in ``Class 3.'' Our four HDPUV
technology classes are Pickup2b, Pickup3, Van2b, and Van3.
    We use a two-step process that involves two algorithms to give
vehicles a ``fit score'' that determines which vehicles best fit into
each technology class. At the first step we determine the vehicle's
size, and at the second step we determine the vehicle's performance
level. Both algorithms consider several metrics about the individual
vehicle and compare that vehicle to other vehicles in the baseline
fleet. This process is discussed in detail in Draft TSD Chapter 2.2.
    Consider our Ravine Runner F Series, which is a medium-sized
performance SUV. The exact same combination of technologies on the
Ravine Runner F Series, which is a medium-sized SUV, will operate
differently in a compact car or pickup truck, two different vehicle
sizes. Our Ravine Runner F Series also achieves slightly better
performance metrics than other medium-sized SUVs in the baseline fleet.
When we say, ``performance metrics,'' we mean power, acceleration,
handing, braking, and so on, but for the performance fit score
algorithm, we consider the vehicle's estimated 0-60 mph time compared
to a baseline 0-60 mph time for the vehicle's technology class.
Accordingly, the ``technology class'' for the Ravine Runner F Series in
our analysis is ``MedSUVPerf''.
    Table II-3 shows how vehicles in different technology classes that
use the exact same fuel economy technology have very different absolute
fuel economy values. Note that, as discussed further below, the
Autonomie absolute fuel economy values are not used directly in the
CAFE Model; we calculate the ratio between two Autonomie absolute fuel
economy values (one for each technology key for a specific technology
class) and apply that ratio to a baseline fleet vehicle's starting fuel
economy value.

          Table II-3--Examples of Technology Class Differences
------------------------------------------------------------------------
                                                             Autonomie
                                                          absolute  fuel
           Technology class and technology key             economy value
                                                               (mpg)
------------------------------------------------------------------------
MedSUVPerf TURBOD; AT10L2, SS12V; ROLL0; AERO5; MR3.....            30.8
MedSUV TURBOD; AT10L2, SS12V; ROLL0; AERO5; MR3.........            34.9
CompactPerf TURBOD; AT10L2, SS12V; ROLL0; AERO5; MR3....            42.2
Pickup TURBOD; AT10L2, SS12V; ROLL0; AERO5; MR3.........            29.7
------------------------------------------------------------------------

    Let us also return to the concept of what we call technology
synergies. Again, depending on the technology, when two technologies
are added to the vehicle together, they may not result in an additive
fuel economy improvement. This is an important concept to understand
because in Section II.D, below, we present technology effectiveness
estimates for every single combination of technology that could be
applied to a vehicle. In some cases, technology effectiveness estimates
show that a combined technology has a different effectiveness estimate
than if the individual technologies were added together individually.
However, this is expected and not an error. Continuing our example from
above, turbocharging technology and DEAC technology both improve fuel
economy by reducing the engine displacement, and accordingly burning
less fuel. Turbocharging allows a larger naturally aspirated engine to
be reduced in size or displacement while still doing the same amount of
work, and its fuel efficiency improvements are in part due to the
reduced displacement. DEAC effectively makes a larger engine smaller by
essentially turning off cylinders, but the engine is able to perform
the same amount of work when needed. Therefore, a manufacturer
upgrading to an engine that uses both a turbocharger and DEAC
technology, like the TURBOD engine in our example above, may not see a
significant fuel economy improvement from that specific combination of
technologies. Table II-4 shows a vehicle's fuel economy value when
using the baseline DEAC technology and when using the baseline
turbocharging technology, compared to our vehicle that uses both of
those technologies combined with a TURBOD engine.

               Table II-4--Example of Technology Synergies
------------------------------------------------------------------------
                                                             Autonomie
                                                           absolute fuel
                MedSUVPerf technology key                  economy value
                                                               (mpg)
------------------------------------------------------------------------
DOHC; SGDI; AT10L2; SS12V; ROLL0; AERO5; MR3............            28.6
DOHC; SGDI; DEAC; AT10L2; SS12V; ROLL0; AERO5; MR3......            29.1
TURBO0; AT10L2; SS12V; ROLL0; AERO5; MR3................            30.7
TURBOD; AT10L2; SS12V; ROLL0; AERO5; MR3................            30.8
------------------------------------------------------------------------

    As expected, the percent improvement in Table II-4 between the
first and second rows is 1.7% and between the third and fourth rows is
0.3%, even though the only difference within the two sets of technology
keys is the DEAC technology (note that we only compare technology keys
within the same technology class). This is because there are complex
interactions between all fuel economy improving technologies. We model
these individual technologies and groups of technologies to reduce the
uncertainty and improve the accuracy of the CAFE Model outputs.
    Some technology synergies that we will discuss in Section II.D
include advanced engine and hybrid powertrain technology synergies. As
an example, we do not see a particularly high effectiveness improvement
from applying advanced engines to existing parallel strong hybrid
(i.e., P2) architectures.\116\ In this instance, the P2 powertrain
improves fuel economy, in part, by allowing the engine to spend more
time operating at efficient engine speed and load conditions. This
reduces the advantage of adding advanced engine technologies, which
also improve fuel economy, by broadening the range of speed and load
conditions for the engine to operate at high efficiency. This
redundancy in fuel savings mechanism results in a lower effectiveness
when the technologies are added to each other. Again, we intend and
expect that different combinations of technologies will provide
different effectiveness improvements on different vehicle types. This
is something we can only see using full vehicle modeling and
simulation.
---------------------------------------------------------------------------

    \116\ A parallel strong hybrid powertrain is fundamentally
similar to a conventional powertrain but adds one electric motor to
improve efficiency. Section II.C.1, Technology Options and Pathways,
shows all of the parallel strong hybrid powertrain options we model
in this analysis.
---------------------------------------------------------------------------

    Just as our CAFE Model analysis requires a large set of technology
inputs and assumptions, the Autonomie modeling uses a large set of
technology inputs and assumptions. Figure II-9 below shows the suite of
fuel consumption input data used in the Autonomie modeling to generate
the fuel consumption input data we use in the CAFE Model.

[GRAPHIC] [TIFF OMITTED] TP17AU23.017

    What are each of these inputs? For full vehicle benchmarking,
vehicles are instrumented with sensors and tested both on the road and
on chassis dynamometers (i.e., the car treadmills used to calculate
vehicle's fuel economy values) under different conditions and duty
cycles. Some examples of full vehicle benchmarking we did in
conjunction with our partners at ANL in anticipation of this rule
include benchmarking a 2019 Chevy Silverado, a 2021 Toyota Rav4 Prime,
a 2022 Hyundai Sonata Hybrid, a 2020 Tesla Model 3, and a 2020 Chevy
Bolt. We produced a report for each vehicle benchmarked, and those are
available in the docket and on our website. As discussed further below,
that full vehicle benchmarking data is used as inputs to the engine
modeling and Autonomie full vehicle simulation modeling. Component
benchmarking is like full vehicle benchmarking, but instead of testing
a full vehicle, we instrument a single production component or
prototype component with sensors and test it on a similar duty cycle as
a full vehicle. Examples of components we benchmark are engines,
transmissions, axles, electric motors, and batteries. Component
benchmarking data are used as an input to component modeling, where a
production or prototype component is changed in fit, form and/or
function and modeled in the same scenario. As an example, we might
model a decrease in the size of holes in fuel injectors to see the fuel
atomization impact or see how it affects the fuel spray angle.
    We use a range of models to do the component modeling for our
analysis. As shown in Figure II-9, battery pack modeling using ANL's
BatPaC Model and engine modeling are two of the most significant
component models used to generate data for the Autonomie modeling. We
discuss BatPaC in detail in Section II.D, but briefly, BatPaC is the
battery pack modeling tool we use to estimate the cost of vehicle
battery packs, based on the materials chemistry, battery design, and
manufacturing design of the plants manufacturing the battery packs.
    Engine modeling is used to generate engine fuel map models that
define the fuel consumption rate for an engine equipped with specific
technologies when operating over a variety of engine load and engine
speed conditions. Some performance metrics we capture in engine
modeling include power, torque, airflow, volumetric efficiency, fuel
consumption, turbocharger performance and matching, pumping losses, and
more. Each engine map model has been developed ensuring the engine will
still operate under real-world constraints using a suite of other
models. Some examples of these models that ensure the engine map models
capture real-world operating constraints include simulating heat
release through a predictive combustion model, knock characteristics
through a kinetic fit knock model,\117\ and using physics-based heat
flow and friction models, among others. We simulate these constraints
using data gathered from component benchmarking, engineering, and
physics.
---------------------------------------------------------------------------

    \117\ Engine knock occurs when combustion of some of the air/
fuel mixture in the cylinder does not result from propagation of the
flame front ignited by the spark plug, but one or more pockets of
air/fuel mixture explodes outside of the envelope of the normal
combustion front. Engine knock can result in unsteady operation and
damage to the engine.
---------------------------------------------------------------------------

    The engine map models are developed by creating a base, or root,
engine map and then modifying that root map, incrementally, to isolate
the effects of the added technologies. The LD engine maps, developed by
IAV using their GT-Power modeling tool and the HDPUV engine maps,
developed by SwRI using their GT-Power modeling tool, are based on
real-world engine designs. One important feature of both the LD and
HDPUV engine maps is that they were both developed using a knock model.
As noted above, a knock model ensures that any engine size or
specification that we model in the analysis does not result in engine
knock, which could damage engine components in a real-world vehicle.
Although the same engine map models are used for all vehicle technology
classes, the effectiveness varies based on the characteristics of each
class. For example, as discussed above, a compact car with a
turbocharged engine will

have a different effectiveness value than a pickup truck with the same
engine technology type. The engine map model development and
specifications are discussed further in Chapter 3 of the Draft TSD.
    ANL also compiles a database of vehicle attributes and
characteristics that are reasonably representative of the vehicles in
that technology class to build the vehicle models. Relevant vehicle
attributes may include a vehicle's fuel efficiency, emissions,
horsepower, 0-60 mph acceleration time, and stopping distance, among
others, while vehicle characteristics may include whether the vehicle
has all-wheel-drive, 18-inch wheels, summer tires, and so on. ANL
identified representative vehicle attributes and characteristics for
both the LD and HDPUV fleets from publicly available information and
automotive benchmarking databases such as A2Mac1,\118\ ANL's
Downloadable Dynamometer Database (D\3\),\119\ EPA compliance and fuel
economy data,\120\ EPA's guidance on the cold start penalty on 2-cycle
tests,\121\ the 21st Century Truck Partnership,122 123 124
and industry partnerships.\125\ The resulting vehicle technology class
baseline assumptions and characteristics database consists of over 100
different attributes like vehicle height and width and weights for
individual vehicle parts.
---------------------------------------------------------------------------

    \118\ A2Mac1: Automotive Benchmarking. (Proprietary data).
Available at: https://www.a2mac1.com. (Accessed: May 31, 2023).
A2Mac1 is subscription-based benchmarking service that conducts
vehicle and component teardown analyses. Annually, A2Mac1 removes
individual components from production vehicles such as oil pans,
electric machines, engines, transmissions, among the many other
components. These components are weighed and documented for key
specifications which is then available to their subscribers.
    \119\ Downloadable Dynamometer Database (D\3\). Argonne National
Laboratory, Energy Systems Division. Available at: https://www.anl.gov/es/downloadable-dynamometer-database. (Accessed: May 31,
2023).
    \120\ Data on Cars used for Testing Fuel Economy. EPA Compliance
and Fuel Economy Data. Available at: https://www.epa.gov/compliance-and-fuel-economy-data/data-cars-used-testing-fuel-economy.
(Accessed: May 31, 2023).
    \121\ EPA PD TSD at 2-265-2-266.
    \122\ DOE. 2019. 21st Century Truck Partnership Research
Blueprint. Available at: https://www.energy.gov/sites/default/files/2019/02/f59/21CTPResearchBlueprint2019_FINAL.pdf. (Accessed: May,
31, 2023).
    \123\ Office of Energy Efficiency & Renewable Energy. 2023. 21st
Century Truck Partnership. Available at: https://www.energy.gov/eere/vehicles/21st-century-truck-partnership. (Accessed: May 31,
2023).
    \124\ National Academies of Sciences, Engineering, and Medicine.
2015. Review of the 21st Century Truck Partnership, Third Report.
Washington, DC: The National Academies Press. Available at: https://nap.nationalacademies.org/21784/. (Accessed: May 31, 2023).
    \125\ North American Council for Freight Efficiency. Research
and analysis. https://www.nacfe.org/research/overview/. (Accessed:
May 31, 2023).
---------------------------------------------------------------------------

    ANL then assigns ``reference'' technologies to each vehicle model.
The reference technologies are the technologies on the first step of
each CAFE Model technology pathway, and they closely (but do not
exactly) correlate to the technology abbreviations that we use in the
CAFE Model. As an example, the first Autonomie vehicle model in the
``MedSUVPerf'' technology class starts out with the least advanced
engine, which is ``DOHC'' (a dual overhead cam engine) in the CAFE
Model, or ``eng01'' in the Autonomie modeling. The vehicle has the
least advanced transmission, AT5, the least advanced MR level, MR0, the
least advanced aerodynamic body style, AERO0, and the least advanced
ROLL level, ROLL0. The first vehicle model is also defined by initial
vehicle attributes and characteristics that consist of data from the
suite of sources mentioned above. Again, these attributes are meant to
reasonably represent the average of vehicle attributes found on
vehicles in a certain technology class.
    Then, just as a vehicle manufacturer tests its vehicles to ensure
they meet specific performance metrics, Autonomie ensures that the
built vehicle model meets its performance metrics. We include
quantitative performance metrics in our Autonomie modeling to ensure
that the vehicle models can meet real-world performance metrics that
consumers observe and that are important for vehicle utility and
customer satisfaction. The four performance metrics that we use in the
Autonomie modeling for light duty vehicles are low-speed acceleration
(the time required to accelerate from 0-60 mph), high-speed passing
acceleration (the time required to accelerate from 50-80 mph),
gradeability (the ability of the vehicle to maintain constant 65 mph
speed on a six percent upgrade), and towing capacity for light duty
pickup trucks. We have been using these performance metrics for the
last several CAFE Model analyses, and vehicle manufacturers have
repeatedly agreed that these performance metrics are representative of
the metrics considered in the automotive industry.\126\ ANL simulates
the vehicle model driving the two-cycle tests (i.e., running its
treadmill ``programs'') to ensure that it meets its applicable
performance metrics (e.g., our MedSUVPerf does not have to meet the
towing capacity performance metric because it is not a pickup truck).
For HDPUVs, Autonomie examines sustainable maximum speed at 6 percent
grade, start/launch capability on grade, and maximum sustainable grade
at highway cruising speed, before examining towing capability to look
for the maximum possible vehicle weight over 40 mph in gradeability.
This process ensures that the vehicle can satisfy the gradeability
requirement (over 40 mph) with additional payload mass to the curb
weight. These metrics are based on commonly used metrics in the
automotive industry, including SAE J2807 tow requirements.\127\
Additional details about how we size light duty and HDPUV powertrains
in Autonomie to meet defined performance metrics can be found in the
CAFE Analysis Autonomie Documentation.
---------------------------------------------------------------------------

    \126\ See, e.g., NHTSA-2021-0053-1492, at 134 (``Vehicle design
parameters are never static. With each new generation of a vehicle,
manufacturers seek to improve vehicle utility, performance, and
other characteristics based on research of customer expectations and
desires, and to add innovative features that improve the customer
experience. The Agencies have historically sought to maintain the
performance characteristics of vehicles modeled with fuel economy-
improving technologies. Auto Innovators encourages the Agencies to
maintain a performance-neutral approach to the analysis, to the
extent possible. Auto Innovators appreciates that the Agencies
continue to consider highspeed acceleration, gradeability, towing,
range, traction, and interior room (including headroom) in the
analysis when sizing powertrains and evaluating pathways for road-
load reductions. All of these parameters should be considered
separately, not just in combination. (For example, we do not support
an approach where various acceleration times are added together to
create a single ``performance'' statistic. Manufacturers must
provide all types of performance, not just one or two to the
detriment of others.)'').
    \127\ See SAE J2807, Performance Requirements for Determining
Tow-Vehicle Gross Combination Weight Rating and Trailer Weight
Rating, available at https://www.sae.org/standards/content/j2807_202002/.
---------------------------------------------------------------------------

    If the vehicle model does not initially meet one of the performance
metrics, then Autonomie's powertrain sizing algorithm increases the
vehicle's engine power. The increase in power is achieved by increasing
engine displacement (which is the measure of the volume of all
cylinders in an engine), which might involve an increase in the number
of engine cylinders, which may lead to an increase in the engine
weight. This iterative process then determines if the baseline vehicle
with increased engine power and corresponding updated engine weight
meets the required performance metrics. The powertrain sizing algorithm
stops once all the baseline vehicle's performance requirements are met.
    Some technologies require extra steps for performance optimization
before the

vehicle models are ready for simulation. Specifically, the sizing and
optimization process is more complex for the electrified vehicles
(e.g., hybrid electric vehicle (HEVs) and plug-in hybrid electric
vehicles (PHEVs) compared to vehicles with only ICEs, as discussed
further in the Draft TSD. As an example, a PHEV powertrain that can
travel a certain number of miles on its battery energy alone (referred
to as all-electric range (AER), or as performing in electric-only mode)
is also sized to ensure that it can meet the performance requirements
of the SAE standardized drive cycles mentioned above in electric-only
mode.
    Every time a vehicle model in Autonomie adopts a new technology,
the vehicle weight is updated to reflect the weight of the new
technology. For some technologies, the direct weight change is easy to
assess. For example, when a vehicle is updated to a higher geared
transmission, the weight of the original transmission is replaced with
the corresponding transmission weight (e.g., the weight of a vehicle
moving from a 6-speed automatic (AT6) to an 8-speed automatic (AT8)
transmission is updated based on the 8-speed transmission weight). For
other technologies, like engine technologies, calculating the updated
vehicle weight is more complex. As discussed earlier, modeling a change
in engine technology involves both the new technology adoption and a
change in power (because the reduction in vehicle weight leads to lower
engine loads, and a resized engine). When a vehicle adopts new engine
technology, the associated weight change to the vehicle is accounted
for based on a regression analysis of engine weight versus power.\128\
---------------------------------------------------------------------------

    \128\ See Merriam-Webster, ``regression analysis'' is the use of
mathematical and statistical techniques to estimate one variable
from another especially by the application of regression
coefficients, regression curves, regression equations, or regression
lines to empirical data. In this case, we are estimating engine
weight by looking at the relationship between engine weight and
engine power.
---------------------------------------------------------------------------

    In addition to using performance metrics that are commonly used by
automotive manufacturers, we instruct Autonomie to mimic real-world
manufacturer decisions by only resizing engines at specific periods in
the analysis and in specific ways. When a vehicle manufacturer is
making decisions about how to change a vehicle model to add fuel
economy improving technology, the manufacturer could entirely
``redesign'' the vehicle, or the manufacturer could ``refresh'' the
vehicle with relatively more minor technology changes. We discuss how
our modeling captures vehicle refreshes and redesigns in more detail
below, but for now there are some simple yet important concepts to
understand. First, most changes to a vehicle's engine happen when the
vehicle is redesigned and not refreshed, as incorporating a new engine
in a vehicle is a 10- to 15-year endeavor at a cost of $750 million to
$1 billion.\129\ But, manufacturers will use that same basic engine,
with only minor changes, across multiple vehicle models. We model
engine ``inheriting'' from one vehicle to another in both the Autonomie
modeling and the CAFE Model. During a vehicle ``refresh'', one vehicle
may inherit an already redesigned engine from another vehicle that
shares the same platform. In the Autonomie modeling, when a new vehicle
adopts fuel saving technologies that are inherited, the engine is not
resized (i.e., the properties from the reference vehicle are used
directly). While this may result in a small change in vehicle
performance, manufacturers have repeatedly and consistently told us
that the high costs for redesign and the increased manufacturing
complexity that would result from resizing engines for small technology
changes preclude them from doing so. In addition, when a manufacturer
applies MR technology (i.e., makes the vehicle lighter), the vehicle
can use a less powerful engine because there is less weight to move.
However, Autonomie will only use a resized engine at certain MR
application levels, as a representation of how manufacturers update
their engine technologies. Again, this is intended to reflect
manufacturer's comments that it would be unreasonable and unaffordable
to resize powertrains for every unique combination of technologies. We
have determined that our rules about performance neutrality and
technology inheritance result in a fleet that is essentially
performance neutral.
---------------------------------------------------------------------------

    \129\ 2015 NAS Report, at 256. It's likely that manufacturers
have made improvements in the product lifetime and development
cycles for engines since this NAS report and the report that the NAS
relied on, but we do not have data on how much. We believe that it
is still reasonable to conclude that generating an all new engine or
transmission design with little to no carryover from the previous
generation would be a notable investment.
---------------------------------------------------------------------------

    Why is it important to ensure that the vehicle models in our
analysis maintain consistent performance levels? The answer involves
how we measure the costs and benefits of different levels of fuel
economy standards. In our analysis, we want to capture the costs and
benefits of vehicle manufacturers applying fuel-economy-improving
technologies to their vehicles. If we modeled increases or decreases in
performance because of fuel economy improving technology--for example,
say a manufacturer that adds a turbocharger to their engine without
downsizing the engine, and then directs all of the additional engine
work to additional vehicle horsepower instead of vehicle fuel economy
improvements--that increase in performance has a monetized benefit
attached to it that is not specifically due to our fuel economy
standards. By ensuring that our vehicle modeling remains performance
neutral, we can better ensure that we are reasonably capturing the
costs and benefits due only to potential changes in the fuel economy
standards.
    As with past rules, we have analyzed the change in low speed
acceleration (0-60 mph) time for four scenarios: (1) MY 2022 under the
no action scenario (i.e., No-Action Alternative), (2) MY 2022 under the
Preferred Alternative, (3) MY 2032 under the no action scenario, and
(4) MY 2032 under the Preferred Alternative.\130\ Using the MY 2022
analysis fleet sales volumes as weights, we calculated the weighted
average 0-60 mph acceleration time for the analysis fleet in each of
the four above scenarios. We identified that the analysis fleet under
no action standards in MY 2032 had a 0.5002 percent worse 0-60 mph
acceleration time than under the Preferred Alternative, indicating
there is minimal difference in performance between the alternatives.
---------------------------------------------------------------------------

    \130\ The baseline reference for both the No-Action Alternative
and the Preferred Alternative is MY 2022 fleet performance.
---------------------------------------------------------------------------

    Autonomie then adopts one single fuel saving technology to the
baseline vehicle model, keeping everything else the same except for
that one technology and the attributes associated with it. Once one
technology is assigned to the vehicle model and the new vehicle model
meets its performance metrics, the vehicle model is used as an input to
the full vehicle simulation. This means that Autonomie simulates the