\75\ Mapping vehicle model/configurations in the analysis fleet
to Argonne simulations was generally straightforward, but
occasionally the mapping was complicated by factors like a vehicle
model/configuration being a great match for simulations within more
than one technology class (in which case, the model/configuration
was assigned to the technology class that it best matched), or when
technologies on the model/configuration were difficult to observe
directly (like friction reduction or parasitic loss characteristics
of a transmission, in which case the agencies relied on
manufacturer-reported data or CBI to help map the vehicle to a
simulation).
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BILLING CODE 4910-59-P

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BILLING CODE 4910-59-C
(e) Shared Vehicle Platforms, Engines, and Transmissions
    Another aspect of characterizing vehicle model/configurations in
the analysis fleet is based on whether they share a ``platform'' with
other vehicle model/configurations. A ``platform'' refers to engineered
underpinnings shared on several differentiated products. Manufacturers
share and standardize components, systems, tooling, and assembly
processes within their products (and occasionally with the products of
another manufacturer) to cost-effectively maintain vibrant
portfolios.\76\
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    \76\ The concept of platform sharing has evolved with time.
Years ago, manufacturers rebadged vehicles and offered luxury
options only on premium nameplates (and manufacturers shared some
vehicle platforms in limited cases). Today, manufacturers share
parts across highly differentiated vehicles with different body
styles, sizes, and capabilities that may share the same platform.
For instance, the Honda Civic and Honda CR-V share many parts and
are built on the same platform. Engineers design chassis platforms
with the ability to vary wheelbase, ride height, and even driveline
configuration. Assembly lines can produce hatchbacks and sedans to
cost-effectively utilize manufacturing capacity and respond to
shifts in market demand. Engines made on the same line may power
small cars or mid-size sport utility vehicles. Additionally,
although the agencies' analysis, like past CAFE analyses, considers
vehicles produced for sale in the U.S., the agency notes these
platforms are not constrained to vehicle models built for sale in
the United States; many manufacturers have developed, and use,
global platforms, and the total number of platforms is decreasing
across the industry. Several automakers (for example, General Motors
and Ford) either plan to, or already have, reduced their number of
platforms to less than 10 and account for the overwhelming majority
of their production volumes on that small number of platforms.
---------------------------------------------------------------------------

    Vehicle model/configurations derived from the same platform are so
identified in the analysis fleet. Many manufacturers' use of vehicle
platforms is well documented in the public record and widely recognized
among the vehicle engineering community. Engineering knowledge,
information from trade publications, and feedback from manufacturers
and suppliers was also used to assign vehicle platforms in the analysis
fleet.
    When the CAFE model is deciding where and how to add technology to
vehicles, if one vehicle on the platform receives new technology, other
vehicles on the platform also receive the technology as part of their
next major redesign or refresh.\77\ Similar to vehicle platforms,
manufacturers create engines that share parts.\78\ One engine family
may appear on many vehicles on a platform, and changes to that engine
may or may not carry through to all the vehicles. Some engines are
shared across a range of different vehicle platforms. Vehicle model/
configurations in the analysis fleet that share engines belonging to
the same platform are also identified as such.
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    \77\ The CAFE model assigns mass reduction technology at a
platform level, but many other technologies may be assigned and
shared at a vehicle nameplate or vehicle model level.
    \78\ For instance, manufacturers may use different piston
strokes on a common engine block or bore out common engine block
castings with different diameters to create engines with an array of
displacements. Head assemblies for different displacement engines
may share many components and manufacturing processes across the
engine family. Manufacturers may finish crankshafts with the same
tools, to similar tolerances. Engines on the same architecture may
share pistons, connecting rods, and the same engine architecture may
include both six and eight cylinder engines.
---------------------------------------------------------------------------

    It is important to note that manufacturers define common engines
differently. Some manufacturers consider engines as ``common'' if the
engines shared an architecture, components, or manufacturing processes.
Other manufacturers take a narrower definition, and only assume
``common'' engines if the parts in the engine assembly are the same. In
some cases, manufacturers designate each engine in each application as
a unique powertrain.\79\ Engine families for each manufacturer were
tabulated and assigned \80\ based on data-driven criteria. If engines
shared a common cylinder count and configuration, displacement,
valvetrain, and fuel type, those engines may have been considered
together. Additionally, if the compression ratio, horsepower, and
displacement of engines were only slightly different, those engines
were considered to be the same for the purposes of redesign and
sharing. Vehicles in the analysis fleet with the same engine family
will therefore adopt engine technology in a coordinated fashion.\81\ By
grouping engines together, the CAFE model controls future engine
families to retain reasonable powertrain complexity.\82\
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    \79\ For instance, a manufacturer may have listed two engines
for a pair that share designs for the engine block, the crank shaft,
and the head because the accessory drive components, oil pans, and
engine calibrations differ between the two. In practice, many
engines share parts, tooling, and assembly resources, and
manufacturers often coordinate design updates between two similar
engines.
    \80\ Engine family is referred to in the analysis as an ``engine
code.''
    \81\ Specifically, if such vehicles have different design
schedules (i.e., refresh and redesign schedules), and a subset of
vehicles using a given engine add engine technologies in the course
of a redesign or refresh that occurs in an early model year (e.g.,
2018), other vehicles using the same engine ``inherit'' these
technologies at the soonest ensuing refresh or redesign. This is
consistent with a view that, over time, most manufacturers are
likely to find it more practicable to shift production to a new
version of an engine than to indefinitely continue production of
both the new engine and a ``legacy'' engine.
    \82\ This does mean, however, that for manufacturers that
submitted highly atomized engine and transmission portfolios, there
is a practical cap on powertrain complexity and the ability of the
manufacturer to optimize the displacement of (a.k.a. ``right size'')
engines perfectly for each vehicle configuration.
---------------------------------------------------------------------------

    Like with engines, manufacturers often use transmissions that are
the same or similar on multiple vehicles.\83\ To reflect this reality,
shared transmissions were considered for manufacturers as appropriate.
To define common transmissions, the agencies considered transmission
type (manual, automatic, dual-clutch, continuously variable), number of
gears, and vehicle architecture (front-wheel-drive, rear-wheel-drive,
all-wheel-drive based on a front-wheel-drive platform, or all-wheel-
drive based on a rear-wheel-drive platform). If vehicles shared these
attributes, these transmissions were grouped for the analysis. Vehicles
in the analysis fleet with the same transmission configuration \84\
will adopt transmission technology together, as described above.\85\
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    \83\ Manufacturers may produce transmissions that have nominally
different machining to castings, or manufacturers may produce
transmissions that are internally identical, except for final output
gear ratio. In some cases, manufacturers sub-contract with suppliers
that deliver whole transmissions. In other cases, manufacturers form
joint-ventures to develop shared transmissions, and these
transmission platforms may be offered in many vehicles across
manufacturers. Manufacturers use supplier and joint-venture
transmissions to a greater extent than engines.
    \84\ Transmission configurations are referred to in the analysis
as ``transmission codes.''
    \85\ Similar to the inheritance approach outlined for engines,
if one vehicle application of a shared transmission family upgraded
the transmission, other vehicle applications also upgraded the
transmission at the next refresh or redesign year.
---------------------------------------------------------------------------

    Having all vehicles that share a platform (or engines that are part
of a family) adopt fuel economy-improving/CO2 emissions-
reducing technologies together, subject to refresh/redesign
constraints, reflects the real-world considerations described above but
also overlooks some decisions manufacturers might make in the real
world in response to market pull, meaning that even though the analysis
fleet is incredibly complex, it is also over-simplified in some
respects compared to the real world. For example, the CAFE model does
not currently attempt to simulate the potential for a manufacturer to
shift the application of technologies to improve performance rather
than fuel economy. Therefore, the model's representation of the
``inheritance'' of technology can lead to estimates a manufacturer
might eventually exceed fuel economy

standards as technology continues to propagate across shared platforms
and engines. In the past, there were some examples of extended periods
during which some manufacturers exceeded one or both of the CAFE and/or
GHG standards, but in plenty of other examples, manufacturers chose to
introduce (or even reintroduce) technological complexity into their
vehicle lineups in response to buyer preferences. Going forward, and
recognizing the recent trend for consolidating platforms, it seems
likely manufacturers will be more likely to choose efficiency over
complexity in this regard; therefore, the potential should be lower
that today's analysis turns out to be over-simplified compared to the
real world.
    Options will be considered to further refine the representation of
sharing and inheritance of technology, possibly including model
revisions to account for tradeoffs between fuel economy and performance
when applying technology. Please provide comments on the sharing and
inheritance-related aspects of the analysis fleet and the CAFE model
along with information that would support refinement of the current
approach or development and implementation of alternative approaches.
(f) Estimated Product Design Cycles
    Another aspect of characterizing vehicle model/configurations in
the analysis fleet is based on when they can next be refreshed or
redesigned. Redesign schedules play an important role in determining
when new technologies may be applied. Many technologies that improve
fuel economy and reduce CO2 emissions may be difficult to
incorporate without a major product redesign. Therefore, each vehicle
model in the analysis fleet has an associated redesign schedule, and
the CAFE model uses that schedule to restrict significant advances in
some technologies (like major mass reduction) to redesign years, while
allowing manufacturers to include minor advances (such as improved tire
rolling resistance) during a vehicle ``refresh,'' or a smaller update
made to a vehicle, which can happen between redesigns. In addition to
refresh and redesign schedules associated with vehicle model/
configurations, vehicles that share a platform subsequently have
platform-wide refresh and redesign schedules for mass reduction
technologies.
    To develop the refresh/redesign cycles used for the MY 2016
vehicles in the analysis fleet, information from commercially available
sources was used to project redesign cycles through MY 2022, as for
NHTSA's analysis for the Draft TAR published in 2016.\86\ Commercially
available sources' estimates through MY 2022 are generally supported by
detailed consideration of public announcements plus related
intelligence from suppliers and other sources, and recognize that
uncertainty increases considerably as the forecasting horizon is
extended. For MYs 2023-2035, in recognition of that uncertainty,
redesign schedules were extended considering past pacing for each
product, estimated schedules through MY 2022, and schedules for other
products in the same technology classes. As mentioned above,
potentially confidential forward-looking information was not requested
from manufacturers; nevertheless, all manufacturers had an opportunity
to review the estimates of product-specific redesign schedules, a few
manufacturers provided related forecasts and, for the most part, that
information corroborated the estimates.
---------------------------------------------------------------------------

    \86\ In some cases, data from commercially available sources was
found to be incomplete or inconsistent with other available
information. For instance, commercially available sources identified
some newly imported vehicles as new platforms, but the international
platform was midway through the product lifecycle. While new to the
U.S. market, treating these vehicles as new entrants would have
resulted in artificially short redesign cycles if carried forward,
in some cases. Similarly, commercially available sources labeled
some product refreshes as redesigns, and vice versa. In these
limited cases, the data was revised to be consistent with other
available information or typical redesign and refresh schedules, for
the purpose of the CAFE modeling. In these limited cases, the
forecast time between redesigns and refreshes was updated to match
the observed past product timing.
---------------------------------------------------------------------------

    Some commenters suggested supplanting these estimated redesign
schedules with estimates applying faster cycles (e.g., four to five
years), and this approach was considered for the analysis.\87\ Some
manufacturers tend to operate with faster redesign cycles and may
continue to do so, and manufacturers tend to redesign some products
more frequently than others. However, especially considering that
information presented by manufacturers largely supports estimates
discussed above, applying a ``one size fits all'' acceleration of
redesign cycles would likely not improve the analysis; instead, doing
so would likely reduce consistency with the real world, especially for
light trucks. Moreover, if some manufacturers accelerate redesigns in
response to new standards, doing so would likely involve costs (greater
levels of stranded capital, reduced opportunity to benefit from
``learning''-related cost reductions) greater than reflected in other
inputs to the analysis. However, a wider range of technologies can
practicably be applied during mid-cycle ``freshenings'' than has been
represented by past analyses, and this part of the analysis has been
expanded, as discussed below in Section II.D.\88\ Also, in the
sensitivity analysis supporting today's proposal and presented in
Chapter 13 of the PRIA, one case involving faster redesign schedules
and one involving slower redesign schedules has been analyzed.
---------------------------------------------------------------------------

    \87\ In response to the EPA's August 21, 2017, Request for
Comments (docket numbers EPA-HQ-OAR-2015-0827 and NHTSA-2016-0068),
see, e.g., CARB at 28 (Docket ID. EPA-HQ-OAR-2015-0827-9197), EDF at
12 (Docket ID. EPA-HQ-OAR-2015-0827-9203), and NRDC, et. al. at 29-
33 (Docket ID. EPA-HQ-OAR-2015-0827-9826).
    \88\ NRDC, et al., at 32.
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    Manufacturers use diverse strategies with respect to when, and how
often they update vehicle designs. While most vehicles have been
redesigned sometime in the last five years, many vehicles have not. In
particular, vehicles with lower annual sales volumes tend to be
redesigned less frequently, perhaps giving manufacturers more time to
amortize the investment needed to bring the product to market. In some
cases, manufacturers continue to produce and sell vehicles designed
more than a decade ago.

[GRAPHIC] [TIFF OMITTED] TP24AU18.012

BILLING CODE 4910-59-P
    Each manufacturer may use different strategies throughout their
product portfolio, and a component of each strategy may include the
timing of refresh and redesign cycles. Table II-3 below summarizes the
average time between redesigns, by manufacturer, by vehicle technology
class.\89\ Dashes mean the manufacturer has no volume in that vehicle
technology class in the MY 2016 analysis fleet. Across the industry,
manufacturers average 6.5 years between product redesigns.
---------------------------------------------------------------------------

    \89\ Technology class, or tech class, refers to a group of fuel-
economy simulations of similar vehicles. As explained, each vehicle
is assigned to a representative simulation to estimate technology
effectiveness for purposes of the analysis.

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    There are a few notable observations from this table. Pick-up
trucks have much longer redesign schedules (7.8 years on average) than
small cars (5.5 years on average). Some manufacturers redesign vehicles
often (every 5.2 years in the case of Hyundai), while other
manufacturers redesign vehicles less often (FCA waits on average 8.6
years between vehicle redesigns). Across the industry, light-duty
vehicle designs last for about 6.5 years.
    Even if two manufacturers have similar redesign cadence, the model
years in which the redesigns occur may still be different and dependent
on where each of the manufacturer's products are in their life cycle.
    Table II-4 summarizes the average age of manufacturers' offering by
vehicle technology class. A value of ``0.0'' means that every vehicle
for a manufacturer in that vehicle technology class, represented in the
MY 2016 analysis fleet was new in MY 2016. Across the industry,
manufacturers redesigned MY 2016 vehicles an average of 3.2 years
earlier.

[GRAPHIC] [TIFF OMITTED] TP24AU18.014

BILLING CODE 4910-59-C
    Based on historical observations and refresh/redesign schedule
forecasts, careful consideration to redesign cycles for each
manufacturer and each vehicle is important. Simply assuming every
vehicle is redesigned by 2021 and by 2025 is not appropriate, as this
would misrepresent both the likely timing of redesigns and the likely
time between redesigns in most cases.
C. Development of Footprint-Based Curve Shapes
    As in the past four CAFE rulemakings, the most recent two of which
included related standards for CO2 emissions, NHTSA and EPA
are proposing to set attribute-based CAFE standards that are defined by
a mathematical function of vehicle footprint, which has observable
correlation with fuel economy and vehicle emissions. 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.\90\
While the CAA includes no specific requirements regarding GHG
regulation, EPA has chosen to adopt standards consistent with the EPCA/
EISA requirements in the interest of simplifying compliance for the
industry since 2010.\91\ Section II.C.1 describes the advantages of
attribute standards, generally. Section II.C.2 explains the agencies'
specific decision to use vehicle footprint as the attribute over which
to vary stringency for past and current rules. Section II.C.3 discusses
the policy considerations in selecting the specific mathematical
function. Section II.C.4 discusses the methodologies used to develop
current attribute-based standards, and the agencies' current proposal
to continue to do so for MYs 2022-2026. Section II.C.5 discusses the
methodologies used to reconsider the mathematical function for the
proposed standards.
---------------------------------------------------------------------------

    \90\ 49 U.S.C. 32902(a)(3)(A).
    \91\ Such an approach is permissible under section 202(a) of the
CAA, and EPA has used the attribute-based approach in issuing
standards under analogous provisions of the CAA.
---------------------------------------------------------------------------

1. Why attribute-based standards, and what are the benefits?
    Under attribute-based standards, every vehicle model has fuel
economy and CO2 targets, the levels of which depend on the
level of that vehicle's determining attribute (for this proposed rule,
footprint is the determining attribute, as discussed below). The
manufacturer's fleet average performance is calculated by the harmonic
production-weighted average of those targets, as defined below:

[GRAPHIC] [TIFF OMITTED] TP24AU18.015

    Here, i represents a given model \92\ in a manufacturer's fleet,
Productioni represents the U.S. production of that model, and Targeti
represents the target as defined by the attribute-based standards. This
means no vehicle is required to meet its target; instead, manufacturers
are free to balance improvements however they deem best within (and,
given credit transfers, at least partially across) their fleets.
---------------------------------------------------------------------------

    \92\ If a model has more than one footprint variant, here each
of those variants is treated as a unique model, i, since each
footprint variant will have a unique target.
---------------------------------------------------------------------------

    The idea is to select the shape of the mathematical function
relating the standard to the fuel economy-related attribute to reflect
the trade-offs manufacturers face in producing more of that attribute
over fuel efficiency (due to technological limits of production and
relative demand of each attribute). If the shape captures these trade-
offs, every manufacturer is more likely to continue adding fuel
efficient technology across the distribution of the attribute within
their fleet, instead of potentially changing the attribute--and other
correlated attributes, including fuel economy--as a part of their
compliance strategy. Attribute-based standards that achieve this have
several advantages.
    First, assuming the attribute is a measurement of vehicle size,
attribute-based standards reduce the incentive for manufacturers to
respond to CAFE standards by reducing vehicle size in ways harmful to
safety.\93\ Larger vehicles, in terms of mass and/or crush space,
generally consume more fuel, but are also generally better able to
protect occupants in a crash.\94\ Because each vehicle model has its
own target (determined by a size-related attribute), properly fitted
attribute-based standards provide little, if any, incentive to build
smaller vehicles simply to meet a fleet-wide average, because smaller
vehicles are subject to more stringent compliance targets.
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    \93\ The 2002 NAS Report described at length and quantified the
potential safety problem with average fuel economy standards that
specify a single numerical requirement for the entire industry. See
Transportation Research Board and National Research Council. 2002.
Effectiveness and Impact of Corporate Average Fuel Economy (CAFE)
Standards, Washington, DC: The National Academies Press (``2002 NAS
Report'') at 5, finding 12, available at https://www.nap.edu/catalog/10172/effectiveness-and-impact-of-corporate-average-fuel-economy-cafe-standards (last accessed June 15, 2018). Ensuing
analyses, including by NHTSA, support the fundamental conclusion
that standards structured to minimize incentives to downsize all but
the largest vehicles will tend to produce better safety outcomes
than flat standards.
    \94\ Bento, A., Gillingham, K., & Roth, K. (2017). The Effect of
Fuel Economy Standards on Vehicle Weight Dispersion and Accident
Fatalities. NBER Working Paper No. 23340. Available at http://www.nber.org/papers/w23340 (last accessed June 15, 2018).
---------------------------------------------------------------------------

    Second, attribute-based standards, if properly fitted, better
respect heterogeneous consumer preferences than do single-valued
standards. As discussed above, a single-valued standard encourages a
fleet mix with a larger share of smaller vehicles by creating
incentives for manufacturers to use downsizing the average vehicle in
their fleet (possibly through fleet mixing) as a compliance strategy,
which may result in manufacturers building vehicles for compliance
reasons that consumers do not want. Under a size-related, attribute-
based standard, reducing the size of the vehicle is a less viable
compliance strategy because smaller vehicles have more stringent
regulatory targets. As a result, the fleet mix under such standards is
more likely to reflect aggregate consumer demand for the size-related
attribute used to determine vehicle targets.
    Third, attribute-based standards provide a more equitable
regulatory framework across heterogeneous manufacturers who may each
produce different shares of vehicles along attributes correlated with
fuel economy.\95\ A single, industry-wide CAFE standard imposes
disproportionate cost burden and compliance challenges on manufacturers
who produce more vehicles with attributes inherently correlated with
lower fuel economy--i.e. manufacturers who produce, on average, larger
vehicles. As discussed above, retaining the ability for manufacturers
to produce vehicles which respect heterogeneous market preferences is
an important consideration. Since manufacturers may target different
markets as a part of their business strategy, ensuring that these
manufacturers do not incur a disproportionate share of the regulatory
cost burden is an important part of conserving consumer choices within
the market.
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    \95\ 2002 NAS Report at 4-5, finding 10.
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2. Why footprint as the attribute?
    It is important that the CAFE and CO2 standards be set
in a way that does not encourage manufacturers to respond by selling
vehicles that are less safe. Vehicle size is highly correlated with
vehicle safety--for this reason, it is important to choose an attribute
correlated with vehicle size (mass or some dimensional measure). Given
this consideration, there are several policy and technical reasons why
footprint is considered to be the most appropriate attribute upon which
to base the standards, even though other vehicle size attributes
(notably, curb weight) are more strongly correlated with fuel economy
and emissions.
    First, mass is strongly correlated with fuel economy; it takes a
certain amount of energy to move a certain amount of mass. Footprint
has some positive correlation with frontal surface area, likely a
negative correlation with aerodynamics, and therefore fuel economy, but
the relationship is less deterministic. Mass and crush space
(correlated with footprint) are both important safety considerations.
As discussed below and in the accompanying PRIA, NHTSA's research of
historical crash data indicates that holding footprint constant, and
decreasing the mass of the largest vehicles, will have a net positive
safety impact to drivers overall, while holding footprint constant and
decreasing the mass of the smallest vehicles will have a net decrease
in fleetwide safety. Properly fitted footprint-based standards provide
little, if any, incentive to build smaller vehicles to meet CAFE and
CO2 standards, and therefore help minimize the impact of
standards on overall fleet safety.
    Second, it is important that the attribute not be easily
manipulated in a manner that does not achieve the goals of EPCA or
other goals, such as safety. Although weight is more strongly
correlated with fuel economy than footprint, there is less risk of
manipulation (changing the attribute(s) to achieve a more favorable
target) by increasing footprint under footprint-based standards than
there would be by increasing vehicle mass under weight-based standards.
It is relatively easy for a manufacturer to add enough weight to a
vehicle to decrease its applicable fuel economy target a significant
amount, as compared to increasing vehicle

footprint, which is a much more complicated change that typically takes
place only with a vehicle redesign.
    Further, some commenters on the MY 2011 CAFE rulemaking were
concerned that there would be greater potential for gaming under multi-
attribute standards, such as those that also depend on weight, torque,
power, towing capability, and/or off-road capability. As discussed in
NHTSA's MY 2011 CAFE final rule,\96\ it is anticipated that the
possibility of gaming is lowest with footprint-based standards, as
opposed to weight-based or multi-attribute-based standards.
Specifically, standards that incorporate weight, torque, power, towing
capability, and/or off-road capability in addition to footprint would
not only be more complex, but by providing degrees of freedom with
respect to more easily-adjusted attributes, they could make it less
certain that the future fleet would actually achieve the projected
average fuel economy and CO2 levels. This is not to say that
a footprint-based system will eliminate gaming, or that a footprint-
based system will eliminate the possibility that manufacturers will
change vehicles in ways that compromise occupant protection, but
footprint-based standards achieve the best balance among affected
considerations. Please provide comments on whether vehicular footprint
is the most suitable attribute upon which to base standards.
---------------------------------------------------------------------------

    \96\ See 74 FR at 14359 (Mar. 30, 2009).
---------------------------------------------------------------------------

3. What mathematical function should be used to specify footprint-based
standards?
    In requiring NHTSA to ``prescribe by regulation separate average
fuel economy standards for passenger and non-passenger automobiles
based on 1 or more vehicle attributes related to fuel economy and
express each standard in the form of a mathematical function'', EPCA/
EISA provides ample discretion regarding not only the selection of the
attribute(s), but also regarding the nature of the function. The CAA
provides no specific direction regarding CO2 regulation, and
EPA has continued to harmonize this aspect of its CO2
regulations with NHTSA's CAFE regulations. The relationship between
fuel economy (and GHG emissions) and footprint, though directionally
clear (i.e., fuel economy tends to decrease and CO2 emissions tend to
increase with increasing footprint), is theoretically vague, and
quantitatively uncertain; in other words, not so precise as to a priori
yield only a single possible curve.
    The decision of how to specify this mathematical function therefore
reflects some amount of judgment. The function can be specified with a
view toward achieving different environmental and petroleum reduction
goals, encouraging different levels of application of fuel-saving
technologies, avoiding any adverse effects on overall highway safety,
reducing disparities of manufacturers' compliance burdens, and
preserving consumer choice, among other aims. The following are among
the specific technical concerns and resultant policy tradeoffs the
agencies have considered in selecting the details of specific past and
future curve shapes:
     Flatter standards (i.e., curves) increase the risk that
both the size of vehicles will be reduced, potentially compromising
highway safety, and reducing any utility consumers would have gained
from a larger vehicle.
     Steeper footprint-based standards may create incentives to
upsize vehicles, potentially oversupplying vehicles of certain
footprints beyond what consumers would naturally demand, and thus
increasing the possibility that fuel savings and CO2
reduction benefits will be forfeited artificially.
     Given the same industry-wide average required fuel economy
or CO2 standard, flatter standards tend to place greater
compliance burdens on full-line manufacturers.
     Given the same industry-wide average required fuel economy
or CO2 standard, dramatically steeper standards tend to
place greater compliance burdens on limited-line manufacturers
(depending of course, on which vehicles are being produced).
     If cutpoints are adopted, given the same industry-wide
average required fuel economy, moving small-vehicle cutpoints to the
left (i.e., up in terms of fuel economy, down in terms of
CO2 emissions) discourages the introduction of small
vehicles, and reduces the incentive to downsize small vehicles in ways
that could compromise overall highway safety.
     If cutpoints are adopted, given the same industry-wide
average required fuel economy, moving large-vehicle cutpoints to the
right (i.e., down in terms of fuel economy, up in terms of
CO2 emissions) better accommodates the design requirements
of larger vehicles -- especially large pickups -- and extends the size
range over which downsizing is discouraged.
4. What mathematical functions have been used previously, and why?
    Notwithstanding the aforementioned discretion under EPCA/EISA, data
should inform consideration of potential mathematical functions, but
how relevant data is defined and interpreted, and the choice of
methodology for fitting a curve to that data, can and should include
some consideration of specific policy goals. This section summarizes
the methodologies and policy concerns that were considered in
developing previous target curves (for a complete discussion see the
2012 FRIA).
    As discussed below, the MY 2011 final curves followed a constrained
logistic function defined specifically in the final rule.\97\ The MYs
2012-2021 final standards and the MYs 2022-2025 augural standards are
defined by constrained linear target functions of footprint, as shown
below: \98\
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    \97\ See 74 FR 14196, 14363-14370 (Mar. 30, 2009) for NHTSA
discussion of curve fitting in the MY 2011 CAFE final rule.
    \98\ The right cutpoint for the light truck curve was moved
further to the right for MYs 2017-2021, so that more possible
footprints would fall on the sloped part of the curve. In order to
ensure that, for all possible footprints, future standards would be
at least as high as MY 2016 levels, the final standards for light
trucks for MYs 2017-2021 is the maximum of the MY 2016 target curves
and the target curves for the give MY standard. This is defined
further in the 2012 final rule. See 77 FR 62624, at 62699-700 (Oct.
15, 2012).
[GRAPHIC] [TIFF OMITTED] TP24AU18.016

    Here, Target is the fuel economy target applicable to vehicles of a
given footprint in square feet (Footprint). The upper asymptote, a, and
the lower asymptote, b, are specified in mpg; the reciprocal of these
values represent the lower and upper asymptotes, respectively, when the
curve is instead specified in gallons per mile (gpm). The

slope, c, and the intercept, d, of the linear portion of the curve are
specified as gpm per change in square feet, and gpm, respectively.
    The min and max functions will take the minimum and maximum values
within their associated parentheses. Thus, the max function will first
find the maximum of the fitted line at a given footprint value and the
lower asymptote from the perspective of gpm. If the fitted line is
below the lower asymptote it is replaced with the floor, which is also
the minimum of the floor and the ceiling by definition, so that the
target in mpg space will be the reciprocal of the floor in mpg space,
or simply, a. If, however, the fitted line is not below the lower
asymptote, the fitted value is returned from the max function and the
min function takes the minimum value of the upper asymptote (in gpm
space) and the fitted line. If the fitted value is below the upper
asymptote, it is between the two asymptotes and the fitted value is
appropriately returned from the min function, making the overall target
in mpg the reciprocal of the fitted line in gpm. If the fitted value is
above the upper asymptote, the upper asymptote is returned is returned
from the min function, and the overall target in mpg is the reciprocal
of the upper asymptote in gpm space, or b.
    In this way curves specified as constrained linear functions are
specified by the following parameters:

a = upper limit (mpg)
b = lower limit (mpg)
c = slope (gpm per sq. ft.)
d = intercept (gpm)

    The slope and intercept are specified as gpm per sq. ft. and gpm
instead of mpg per sq. ft. and mpg because fuel consumption and
emissions appear roughly linearly related to gallons per mile (the
reciprocal of the miles per gallon).
(a) NHTSA in MY 2008 and MY 2011 CAFE (Constrained Logistic)
    For the MY 2011 CAFE rule, NHTSA estimated fuel economy levels by
footprint from the MY 2008 fleet after normalization for differences in
technology,\99\ but did not make adjustments to reflect other vehicle
attributes (e.g., power-to-weight ratios). Starting with the
technology-adjusted passenger car and light truck fleets, NHTSA used
minimum absolute deviation (MAD) regression without sales weighting to
fit a logistic form as a starting point to develop mathematical
functions defining the standards. NHTSA then identified footprints at
which to apply minimum and maximum values (rather than letting the
standards extend without limit) and transposed these functions
vertically (i.e., on a gallons-per-mile basis, uniformly downward) to
produce the promulgated standards. In the preceding rule, for MYs 2008-
2011 light truck standards, NHTSA examined a range of potential
functional forms, and concluded that, compared to other considered
forms, the constrained logistic form provided the expected and
appropriate trend (decreasing fuel economy as footprint increases), but
avoided creating ``kinks'' the agency was concerned would provide
distortionary incentives for vehicles with neighboring footprints.\100\
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    \99\ See 74 FR 14196, 14363-14370 (Mar. 30, 2009) for NHTSA
discussion of curve fitting in the MY 2011 CAFE final rule.
    \100\ See 71 FR 17556, 17609-17613 (Apr. 6, 2006) for NHTSA
discussion of ``kinks'' in the MYs 2008-2011 light truck CAFE final
rule (there described as ``edge effects''). A ``kink,'' as used
here, is a portion of the curve where a small change in footprint
results in a disproportionally large change in stringency.
---------------------------------------------------------------------------

(b) MYs 2012-2016 Standards (Constrained Linear)
    For the MYs 2012-2016 rule, potential methods for specifying
mathematical functions to define fuel economy and CO2
standards were reevaluated. These methods were fit to the same MY 2008
data as the MY 2011 standard. Considering these further specifications,
the constrained logistic form, if applied to post-MY 2011 standards,
would likely contain a steep mid-section that would provide undue
incentive to increase the footprint of midsize passenger cars.\101\ A
range of methods to fit the curves would have been reasonable, and a
minimum absolute deviation (MAD) regression without sales weighting on
a technology-adjusted car and light truck fleet was used to fit a
linear equation. This equation was used as a starting point to develop
mathematical functions defining the standards. Footprints were then
identified at which to apply minimum and maximum values (rather than
letting the standards extend without limit). Finally, these
constrained/piecewise linear functions were transposed vertically
(i.e., on a gpm or CO2 basis, uniformly downward) by
multiplying the initial curve by a single factor for each MY standard
to produce the final attribute-based targets for passenger cars and
light trucks described in the final rule.\102\ These transformations
are typically presented as percentage improvements over a previous MY
target curve.
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    \101\ 75 FR at 25362.
    \102\ See generally 74 FR at 49491-96; 75 FR at 25357-62.
---------------------------------------------------------------------------

(c) MYs 2017 and Beyond Standards (Constrained Linear)
    The mathematical functions finalized in 2012 for MYs 2017 and
beyond changed somewhat from the functions for the MYs 2012-2016
standards. These changes were made to both address comments from
stakeholders, and to further consider some of the technical concerns
and policy goals judged more preeminent under the increased uncertainty
of the impacts of finalizing and proposing standards for model years
further into the future.\103\ Recognizing the concerns raised by full-
line OEMs, it was concluded that continuing increases in the stringency
of the light truck standards would be more feasible if the light truck
curve for MYs 2017 and beyond was made steeper than the MY 2016 truck
curve and the right (large footprint) cut-point was extended only
gradually to larger footprints. To accommodate these considerations,
the 2012 final rule finalized the slope fit to the MY 2008 fleet using
a sales-weighted, ordinary least-squares regression, using a fleet that
had technology applied to make the technology application across the
fleet more uniform, and after adjusting the data for the effects of
weight-to-footprint. Information from an updated MY 2010 fleet was also
considered to support this decision. As the curve was vertically
shifted (with fuel economy specified as mpg instead of gpm or
CO2 emissions) upwards, the right cutpoint was progressively
moved for the light truck curves with successive model years, reaching
the final endpoint for MY 2021; this is further discussed and shown in
Chapter 4.3 of the PRIA.
---------------------------------------------------------------------------

    \103\ The MYs 2012-2016 final standards were signed April 1st,
2010--putting 6.5 years between its signing and the last affected
model year, while the MYs 2017-2021 final standards were signed
August 28th, 2012--giving just more than nine years between signing
and the last affected final standards.
---------------------------------------------------------------------------

5. Reconsidering the Mathematical Functions for This Proposal
(a) Why is it important to reconsider the mathematical functions?
    By shifting the developed curves by a single factor, it is assumed
that the underlying relationship of fuel consumption (in gallons per
mile) to vehicle footprint does not change significantly from the model
year data used to fit the curves to the range of model years for which
the shifted curve shape is applied to develop the standards. However,
it must be recognized that the relationship

between vehicle footprint and fuel economy is not necessarily constant
over time; newly developed technologies, changes in consumer demand,
and even the curves themselves could, if unduly susceptible to gaming,
influence the observed relationships between the two vehicle
characteristics. For example, if certain technologies are more
effective or more marketable for certain types of vehicles, their
application may not be uniform over the range of vehicle footprints.
Further, if market demand has shifted between vehicle types, so that
certain vehicles make up a larger share of the fleet, any underlying
technological or market restrictions which inform the average shape of
the curves could change. That is, changes in the technology or market
restrictions themselves, or a mere re-weighting of different vehicles
types, could reshape the fit curves.
    For the above reasons, the curve shapes were reconsidered using the
newest available data, from MY 2016. With a view toward corroboration
through different techniques, a range of descriptive statistical
analyses were conducted that do not require underlying engineering
models of how fuel economy and footprint might be expected to be
related, and a separate analysis that uses vehicle simulation results
as the basis to estimate the relationship from a perspective more
explicitly informed by engineering theory was conducted as well.
Despite changes in the new vehicle fleet both in terms of technologies
applied and in market demand, the underlying statistical relationship
between footprint and fuel economy has not changed significantly since
the MY 2008 fleet used for the 2012 final rule; therefore, it is
proposed to continue to use the curve shapes fit in 2012. The analysis
and reasoning supporting this decision follows.
(b) What statistical analyses did NHTSA consider?
    In considering how to address the various policy concerns discussed
above, data from the MY 2016 fleet was considered, and a number of
descriptive statistical analyses (i.e., involving observed fuel economy
levels and footprints) using various statistical methods, weighting
schemes, and adjustments to the data to make the fleets less
technologically heterogeneous were performed. There were several
adjustments to the data that were common to all of the statistical
analyses considered.
    With a view toward isolating the relationship between fuel economy
and footprint, the few diesels in the fleet were excluded, as well as
the limited number of vehicles with partial or full electric
propulsion; when the fleet is normalized so that technology is more
homogenous, application of these technologies is not allowed. This is
consistent with the methodology used in the 2012 final rule.
    The above adjustments were applied to all statistical analyses
considered, regardless of the specifics of each of the methods,
weights, and technology level of the data, used to view the
relationship of vehicle footprint and fuel economy. Table II-5, below,
summarizes the different assumptions considered and the key attributes
of each. The analysis was performed considering all possible
combinations of these assumptions, producing a total of eight footprint
curves.
[GRAPHIC] [TIFF OMITTED] TP24AU18.017

(1) Current Technology Level Curves
    The ``current technology'' level curves exclude diesels and
vehicles with electric propulsion, as discussed above, but make no
other changes to each model year fleet. Comparing the MY 2016 curves to
ones built under the same methodology from previous model year fleets
shows whether the observed curve shape has changed significantly over
time as standards have become more stringent. Importantly, these curves
will include any market forces which make technology application
variable over the distribution of footprint. These market forces will
not be present in the ``maximum technology'' level curves: By making
technology levels homogenous, this variation is removed. The current
technology level curves built using both regression types and both
regression weight methodologies from the MY 2008, MY 2010, and MY 2016
fleets, shown in more detail in Chapter <IP_ADDRESS> of the PRIA, support
the curve slopes finalized in the 2012 final rule. The curves built
from most methodologies using each fleet generally shift, but remain
very similar in slope. This suggests that the relationship of footprint
to fuel economy, including both technology and market limits, has not
significantly changed.
(2) Maximum Technology Level Curves
    As in prior rulemakings, technology differences between vehicle
models were considered to be a significant factor producing uncertainty
regarding the relationship between fuel consumption and footprint.
Noting that attribute-based standards are intended to encourage the
application of additional technology to improve fuel efficiency and
reduce CO2 emissions across the distribution of footprint in
the fleet, approaches were considered in which technology application
is simulated for purposes of the curve fitting analysis in order to
produce fleets that are less varied in technology content. This
approach helps reduce ``noise'' (i.e., dispersion) in the plot of
vehicle footprints and fuel consumption levels and identify a more
technology-neutral relationship between footprint and fuel consumption.
The results of updated analysis for maximum technology level curves are
also shown in Chapter <IP_ADDRESS> of the PRIA. Especially if vehicles
progress over time toward more similar size-specific efficiency,
further removing variation in technology application both better
isolates the relationship between fuel consumption and footprint and
further supports the curve slopes finalized in the 2012 final rule.
(c) What other methodologies were considered?
    The methods discussed above are descriptive in nature, using
statistical analysis to relate observed fuel economy levels to observed
footprints for known vehicles. As such, these methods are clearly based
on actual data, answering the question ``how does fuel economy appear
to be related to footprint?'' However, being independent of explicit
engineering theory, they do not answer the question ``how might one
expect fuel economy to be related to footprint?'' Therefore, as an
alternative to the above methods, an alternative methodology was also
developed and applied that, using full-vehicle simulation, comes closer
to answer the second question, providing a basis to either corroborate
answers to the first, or suggest that further investigation could be
important.
    As discussed in the 2012 final rule, several manufacturers have
confidentially shared with the agencies what they described as
``physics-based'' curves, with each OEM showing significantly different
shapes for the footprint-fuel economy relationships. This variation
suggests that manufacturers face different curves given the other
attributes of the vehicles in their fleets (i.e., performance
characteristics) and/or that their curves reflected different levels of
technology application. In reconsidering the shapes of the proposed MYs
2021-2026 standards, a similar estimation of physics-based curves
leveraging third-party simulation work form Argonne National
Laboratories (ANL) was developed. Estimating physics-based curves
better ensures that technology and performance are held constant for
all footprints; augmenting a largely statistical analysis with an
analysis that more explicitly incorporates engineering theory helps to
corroborate that the relationship between fuel economy and footprint is
in fact being characterized.
    Tractive energy is the amount of energy it will take to move a
vehicle.\104\ Here, tractive energy effectiveness is defined as the
share of the energy content of fuel consumed which is converted into
mechanical energy and used to move a vehicle--for internal combustion
engine (ICE) vehicles, this will vary with the relative efficiency of
specific engines. Data from ANL simulations suggest that the limits of
tractive energy effectiveness are approximately 25% for vehicles with
internal combustion engines which do not possess ISG, other hybrid,
plug-in, pure electric, or fuel cell technology.
---------------------------------------------------------------------------

    \104\ Thomas, J. ``Drive Cycle Powertrain Efficiencies and
Trends Derived from EPA Vehicle Dynamometer Results,'' SAE Int. J.
Passeng. Cars--Mech. Syst. 7(4):2014, doi:10.4271/2014-01-2562.
Available at https://www.sae.org/publications/technical-papers/content/2014-01-2562/ (last accessed June 15, 2018).
---------------------------------------------------------------------------

    A tractive energy prediction model was also developed to support
today's proposal. Given a vehicle's mass, frontal area, aerodynamic
drag coefficient, and rolling resistance as inputs, the model will
predict the amount of tractive energy required for the vehicle to
complete the Federal test cycle. This model was used to predict the
tractive energy required for the average vehicle of a given footprint
\105\ and ``body technology package'' to complete the cycle. The body
technology packages considered are defined in Table II-6, below. Using
the absolute tractive energy predicted and tractive energy
effectiveness values spanning possible ICE engines, fuel economy values
were then estimated for different body technology packages and engine
tractive energy effectiveness values.
---------------------------------------------------------------------------

    \105\ The mass reduction curves used elsewhere in this analysis
were used to predict the mass of a vehicle with a given footprint,
body style box, and mass reduction level. The `Body style Box' is 1
for hatchbacks and minivans, 2 for pickups, and 3 for sedans, and is
an important predictor of aerodynamic drag. Mass is an essential
input in the tractive energy calculation.

---------------------------------------------------------------------------

[GRAPHIC] [TIFF OMITTED] TP24AU18.018

    Chapter 6 of the PRIA shows the resultant CAFE levels estimated for
the vehicle classes ANL simulated for this analysis, at different
footprint values and by vehicle ``box.'' Pickups are considered 1-box,
hatchbacks and minivans are 2-box, and sedans are 3-box. These
estimates are compared with the MY 2021 standards finalized in 2012.
The general trend of the simulated data points follows the pattern of
the previous MY 2021 standards for all technology packages and tractive
energy effectiveness values presented in the PRIA. The tractive energy
curves are intended to validate the curve shapes against a physics-
based alternative, and the analysis suggests that the curve shapes
track the physical relationship between fuel economy and tractive
energy for different footprint values.
    Physical limitations are not the only forces manufacturers face;
they must also produce vehicles that consumers will purchase. For this
reason, in setting future standards, the analysis will continue to
consider information from statistical analyses that do not homogenize
technology applications in addition to statistical analyses which do,
as well as a tractive energy analysis similar to the one presented
above.
    The relationship between fuel economy and footprint remains
directionally discernable but quantitatively uncertain. Nevertheless,
each standard must commit to only one function. Approaching the
question ``how is fuel economy related to footprint'' from different
directions and applying different approaches will provide the greatest
confidence that the single function defining any given standard
appropriately and reasonably reflects the relationship between fuel
economy and footprint. Please provide comments on this tentative
conclusion and the above discussion.
D. Characterization of Current and Anticipated Fuel-Saving Technologies
    The analysis evaluates a wide array of technologies manufacturers
could use to improve the fuel economy of new vehicles, in both the near
future and the timeframe of this proposed rulemaking, to meet the fuel
economy and CO2 standards proposed in this rulemaking. The
analysis evaluated costs for these technologies, and looked at how
these costs may change over time. The analysis also considered how
fuel-saving technologies may be used on many types of vehicles (ranging
from small cars to trucks) and how the technologies may perform in
improving fuel economy and CO2 emissions in combination with
other technologies. With cost and effectiveness estimates for
technologies, the analysis can forecast how manufacturers may respond
to potential standards and can estimate the associated costs and
benefits related to technology and equipment changes. This assists the
assessment of technological feasibility and is a building block for the
consideration of economic practicability of potential standards.
    NHTSA, EPA, and CARB issued the Draft Technical Assessment Report
(Draft TAR) \106\ as the first step in the EPA MTE process. The Draft
TAR provided an opportunity for the agencies to share with the public
updated technical analysis relevant to development of future standards.
For this NPRM, the analysis relies on portions of the analysis
presented in the Draft TAR, along with new information that has been
gathered and developed since conducting that analysis, and the
significant, substantive input that was received during the public
comment period.
---------------------------------------------------------------------------

    \106\ Available at https://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/Draft-TAR-Final.pdf (last accessed June 15, 2018).
---------------------------------------------------------------------------

    The Draft TAR considered many technologies previously assessed in
the 2012 final rule.\107\ In some cases, manufacturers have nearly
universally adopted a technology in today's new vehicle fleet (for
example, electric power steering). In other cases, manufacturers
occasionally use a technology in today's new vehicle fleet (like
turbocharged engines). For a few technologies considered in the 2012
rulemaking, manufacturers began implementing the technologies but have
since largely pivoted to other technologies due to consumer acceptance
issues (for instance, in some cases drivability and performance feel
issues associated with dual clutch transmissions without a torque
converter) or limited commercial success. The analysis utilizes new
information as manufacturers' use of technologies evolves.
---------------------------------------------------------------------------

    \107\ 77 FR 62624 (Oct. 15, 2012).
---------------------------------------------------------------------------

    Some of the emerging technologies described in the Draft TAR were
not included in this analysis, but this includes some additional
technologies not previously considered. As industry invents and
develops new fuel-savings technologies, and as suppliers and
manufacturers produce and apply the technologies, and as consumers
react to the new technologies, efforts are continued to learn more
about the capabilities and limitations of new technologies. While a
technology is in early development, theoretical constructs, limited
access to test data, and CBI is relied on to assess the technology.
After manufacturers commercialize the technology and bring products to
market, the technology may be studied in more detail, which generally
leads to the most reliable information about the technology. In
addition, once in production, the technology is represented in the fuel
economy and CO2 status of the baseline fleet. The technology
analysis is kept as current as possible in light of the ongoing
technology development and implementation in the automotive industry.
    Some technology assumptions have been updated since the MYs 2017-
2025 final rule and, in many cases, since the 2016 Draft TAR. In some
cases, EPA and NHTSA presented different analytical approaches in the
Draft TAR; the analysis is now presented using the

same direct manufacturing costs, retail costs, and learning rates. In
addition, the effectiveness of fuel-economy technologies is now
assessed based on the same assumptions, and with the same tools.
Finally, manufacturers' response to stringency alternatives is forecast
with the same simulation model.
    Since the 2017 and later final rule, many cost assessments,
including tear down studies, were funded and completed, and presented
as part of the Draft TAR analysis. These studies evaluated
transmissions, engines, hybrid technologies, and mass reduction.\108\
As a result, the analysis uses updated cost estimates for many
technologies, some of which have been updated since the Draft TAR. In
addition to those studies, the analysis also leveraged research reports
from other organizations to assess costs.\109\ Today's analysis also
updates the costs to 2016 dollars, as in many cases technology costs
were estimated several years ago.
---------------------------------------------------------------------------

    \108\ FEV prepared several cost analysis studies for EPA on
subjects ranging from advanced 8-speed transmissions to belt
alternator starter, or Start/Stop systems. NHTSA also contracted
with Electricore, EDAG, and Southwest Research on teardown studies
evaluating mass reduction and transmissions. The 2015 NAS report on
fuel economy technologies for light-duty vehicles also evaluated the
agencies' technology costs developed based on these teardown
studies, and the technology costs used in this proposal were updated
accordingly. These studies are discussed in detail in Chapter 6 of
the PRIA accompanying this proposal.
    \109\ For example, the agencies relied on reports from the
Department of Energy's Office of Energy Efficiency & Renewable
Energy's Vehicle Technologies Office. More information on that
office is available at https://www.energy.gov/eere/vehicles/vehicle-technologies-office. Other agency reports that were relied on for
technology or other information are referenced throughout this
proposal and accompanying PRIA.
---------------------------------------------------------------------------

    The analysis uses an updated, peer-reviewed model developed by ANL
for the Department of Energy to provide a more rigorous estimate for
battery costs. The new battery model provides an estimate future for
battery costs for hybrids, plug-in hybrids, and electric vehicles,
taking into account the different battery design characteristics and
taking into account the size of the battery for different
applications.\110\
---------------------------------------------------------------------------

    \110\ For instance, battery electric vehicles with high levels
of mass reduction may use a smaller battery than a comparable
vehicle with less mass reduction technology and still deliver the
same range on a charge.
---------------------------------------------------------------------------

    In the Draft TAR, two possible methodologies to estimate indirect
costs from direct manufacturing costs, described as ``indirect cost
multipliers'' and ``retail price equivalent'' were presented. Both of
these methodologies attempted to relate the price of parts for fuel-
saving technologies to a retail price. Today's analysis utilizes the
direct manufacturing costs (DMC) and the retail price equivalent (RPE)
methodology published in the Draft TAR.
    Two tools to estimate effectiveness of fuel-saving technologies
were used in the Draft TAR, and for today's analysis, only one tool was
used (Autonomie).\111\ Previously, EPA developed ``ALPHA'', an in-house
model that estimated fuel-savings for technologies, which provided a
foundation for EPA's analysis. EPA's ``ALPHA'' results were used to
calibrate a much simpler ``Lumped Parameter Model'' that was developed
by EPA to estimate technology effectiveness for many technologies. The
Lumped Parameter Model (LPM) approximated simulation modeling results
instead of directly using the results and lead to less accurate
estimates of technology effectiveness. Many stakeholders questioned the
efficacy of the Lumped Parameter Model and ALPHA assumptions and
outputs in combination,\112\ especially as the tool was used to
evaluate increasingly heterogeneous combinations of technologies in the
baseline fleet.\113\ For today's analysis, EPA and NHTSA used an
updated version of the Autonomie model--an improved version of what
NHTSA presented in the 2016 Draft TAR--to assess technology
effectiveness of technologies and combinations of technologies. The
Department of Energy's ANL developed Autonomie and the underpinning
model assumptions leveraged research from the DOE's Vehicle
Technologies Office and feedback from the public. Autonomie is
commercially available and widely used; third parties such as
suppliers, automakers, and academic researchers (who publish findings
in peer reviewed academic journals) commonly use the Autonomie
simulation software.
---------------------------------------------------------------------------

    \111\ ANL's Full-Vehicle Simulation Autonomie Model is discussed
in Chapter 6 of the PRIA and in the ANL Model Documentation
available at Docket No. NHTSA-2018-0067.
    \112\ At NHTSA-2016-0068-0082, p. 49, FCA provided the following
comments, ``FCA believes EPA is overestimating the benefits of
technology. As the LPM is calibrated to those projections, so too is
the LPM too optimistic.'' FCA also shared the chart, ``LPM vs.
Actual for 8 Speed Transmissions.''
    \113\ See e.g., Automotive News ``CAFE math gets trickier as
industry innovates'' (Kulisch), March 26, 2018.
---------------------------------------------------------------------------

    Similarly for today's analysis, only one tool is used. Previously,
EPA developed ``OMEGA,'' a tool that looked at costs of technologies
and effectiveness of technologies (as estimated by EPA's Lumped
Parameter Model or ALPHA), and applied cost effective technologies to
manufacturers' fleets in response to potential standards. Many
stakeholders commented that the OMEGA model oversimplified fleet-wide
analysis, resulting in significant shortcomings.\114\ For instance,
OMEGA assumed manufacturers would redesign all vehicles in the fleet by
2021, and then again by 2025; stakeholders purported that these
assumptions did not reflect practical constraints in many
manufacturers' business models.\115\ Additionally, stakeholders
commented that OMEGA did not adequately take into consideration common
parts like shared engines, shared transmissions, and engineering
platforms. The CAFE model does consider refresh and redesign cycles and
parts sharing. The CAFE model can evaluate responses to any policy
alternative on a year-by-year basis, as required by EPCA/EISA \116\ and
as allowed by the CAA, and can also account for manufacturers' year-by-
year plans that involve ``carrying forward'' technology from prior
model years, and some other vehicles possibly applying ``extra''
technology in anticipation of standards in ensuing model years. For
today's analysis, an updated version of the CAFE model is used--an
improved version of what NHTSA presented in the 2016 Draft TAR--to
assess manufacturers' response to policy alternatives. See Section
II.A.1 above for further discussion of the decision to use the CAFE
model for the NPRM analysis.
---------------------------------------------------------------------------

    \114\ The Alliance of Automobile Manufacturers commented that
``the OMEGA model is over-optimized and unrealistic . . . many of
these issues either are not present or are accounted for in DOT's
Volpe model. The Alliance therefore recommends that EPA focus on
ensuring needs specific to its regulatory analysis are appropriately
addressed in the Volpe model.'' Alliance at 48 (Docket ID. EPA-HQ-
OAR-2015-0827-9194).
    \115\ For example, FCA provided the following comments: ``EPA's
expectation of 10-20% mass reduction rates across 70% of FCA's
fleet, which includes a 70% truck mix, is simply unreasonable as the
magnitude of change would require complete product redesigns in less
than eight years shortening existing production needed to amortize
the large capital cost involved.'' FCA at 19 (Docket ID. EPA-HQ-OAR-
2015-0827-6160).
    \116\ 49 U.S.C. 32902(b)(2)(B).
---------------------------------------------------------------------------

    Each aforementioned change is discussed briefly in the remainder of
this section and in much greater detail in Chapter 6 of the PRIA. A
brief summary of the technologies considered in this proposal is
discussed below. Please provide comments on all aspects of the analysis
as discussed here and as detailed in the PRIA.

1. Data Sources and Processes for Developing Individual Technology
Assumptions
    Technology assumptions were developed that provide a foundation for
conducting a fleet-wide compliance analysis. As part of this effort,
the analysis estimated technology costs, projected technology
effectiveness values, and identified possible limitations for some
fuel-saving technologies. There is a preference to use values developed
from careful review of commercialized technologies; however, in some
cases for technologies that are new, and are not yet for sale in any
vehicle, the analysis relied on information from other sources,
including CBI and third-party research reports and publications. Many
emerging technologies are still being evaluated for the analysis
supporting the final rule, including those that are currently emerging.
    For today's analysis, one set of cost assumptions, one set of
effectiveness values (developed with one tool), and one set of
assumptions about the limitations of some technologies are presented.
Many sources of data were evaluated, in addition to many stakeholder
comments received on the Draft TAR. Throughout the process of
developing the assumptions for today's analysis, the preferred approach
was to harmonize on sources and methodologies that were data-driven and
reproducible in independent verification, produced using tools utilized
by OEMs, suppliers, and academic institutions, and using tools that
could support both CAFE and CO2 analysis. A single set of
assumptions also facilitates and focuses public comment by reducing
burden on stakeholders who seek to review all of the supporting
documentation for this proposal.
(a) Technology Costs
    The analysis estimated present and future costs for fuel-saving
technologies, taking into consideration the type of vehicle, or type of
engine if technology costs vary by application. Cost estimates were
developed based on three main inputs. First, direct manufacturing costs
(DMC), or the component costs of the physical parts and systems, were
considered, with estimated costs assuming high volume production. DMCs
generally do not include the indirect costs of tools, capital
equipment, and financing costs, nor do they cover indirect costs like
engineering, sales, and administrative support. Second, indirect costs
via a scalar markup of direct manufacturing costs (the retail price
equivalent, or RPE) was taken into account. Finally, costs for
technologies may change over time as industry streamlines design and
manufacturing processes. Potential cost improvements with learning
effects (LE) were also considered. The retail cost of equipment in any
future year is estimated to be equal to the product of the DMC, RPE,
and LE. Considering the retail cost of equipment, instead of merely
direct manufacturing costs, is important to account for the real-world
price effects of a technology, as well as market realities. Absent
government mandate, a manufacturer will not undertake expensive
development and support costs to implement technologies without
realistic prospects of consumer willingness to pay enough for such
technology to allow for the manufacturer to recover its investment.
(1) Direct Manufacturing Costs
    In many instances, the analysis used agency-sponsored tear-down
studies of vehicles and parts to estimate the direct manufacturing
costs of individual technologies. In the simplest cases, the studies
produced results that confirmed third-party industry estimates, and
aligned with confidential information provided by manufacturers and
suppliers. In cases with a large difference between the tear-down study
results and credible independent sources, study assumptions were
scrutinized, and sometimes the analysis was revised or updated
accordingly.\117\ Studies were conducted on vehicles and technologies
that would cover a breadth of fuel-savings technologies, but because
tear-down studies can be time-intensive and expensive, the agencies did
not sponsor teardown studies for every technology. For some
technologies, independent tear-down studies were also utilized, in
addition to other publications and confidential business
information.\118\ Due to the variety of technologies and their
applications, a detailed tear-down study could not be conducted for
every technology, including pre-production technologies.
---------------------------------------------------------------------------

    \117\ For instance, in previous analysis, EPA referenced an old
study that purported the first 7-10% of mass reduction to be
``free'' or at a significant ``cost savings'' to for many vehicles
and many manufacturers.
    \118\ The analysis referenced studies from private businesses
and business analysts for emerging technologies and for off-the-
shelf technologies that were commercially mature.
---------------------------------------------------------------------------

    Many fuel-saving technologies were considered that are pre-
production, or sold in very small pilot volumes. For emerging
technologies that could be applied in the rulemaking timeframe, a tear-
down study cannot be conducted to assess costs because the product is
not yet in the marketplace for evaluation. In these cases, third-party
estimates and confidential information from suppliers and manufacturers
are relied upon; however, there are some common pitfalls with relying
on confidential business information to estimate costs. The agencies
and the source may have had incongruent or incompatible definitions of
``baseline.'' \119\ The source may have provided direct manufacturer
costs at a date many years in the future, and assumed very high
production volumes, important caveats to consider for agency analysis.
In addition, a source, under no contractual obligation to the agencies,
may provide incomplete and/or misleading information. In other cases,
intellectual property considerations and strategic business
partnerships may have contributed to a manufacturer's cost information
and could be difficult to account for in the model as not all
manufacturer's may have access to proprietary technologies at stated
costs. New information is carefully evaluated in light of these common
pitfalls, especially regarding emerging technologies. The analysis used
third-party, forward looking information for advanced cylinder
deactivation and variable compression ratio engines, and while these
cost estimates may be cursory (as is the case with many emerging
technologies prior to commercialization), the agencies took care to use
early information provided fairly and reasonably. While costs for fuel-
saving technologies reflect the best estimates available today,
technology cost estimates will likely change in the future as
technologies are deployed and as production is expanded. For emerging
technologies, the best information available at the time of the
analysis was utilized, and cost assumptions will continue to be
updated.
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    \119\ ``Baseline'' here refers to a reference part, piece of
equipment, or engineering system that efficiency improvements and
costs are relative to.
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(2) Indirect Costs
    As explained above, in addition to direct manufacturing costs, the
analysis estimates and considers indirect manufacturing costs. To
estimate indirect costs, direct manufacturing costs are multiplied by a
factor to represent the average price for fuel-saving technologies at
retail. This factor, referred to as the retail price equivalence (RPE),
accounts for indirect costs like engineering, sales, and administrative
support, as well as other overhead costs, business expenses, warranty
costs, and return on capital

considerations. This approach to the RPE remains unchanged from the RPE
approach NHTSA presented in the Draft TAR.
    The RPE was chosen for this analysis instead of indirect cost
multipliers (ICM) because it provides the best estimate of indirect
costs. For a more detailed discussion of the approach to indirect
costs, see PRIA Chapter 9.
(3) Stranded Capital Costs
    Past analyses accounted for costs associated with stranded capital
when fuel economy standards caused a technology to be replaced before
its costs were fully amortized. The idea behind stranded capital is
that manufacturers amortize research, development, and tooling expenses
over many years, especially for engines and transmissions. The
traditional production life-cycles for transmissions and engines have
been a decade or longer. If a manufacturer launches or updates a
product with fuel-saving technology, and then later replaces that
technology with an unrelated or different fuel-saving technology before
the equipment and research and development investments have been fully
paid off, there will be unrecouped, or stranded, capital costs.
Quantifying stranded capital costs attempted to account for such lost
investments. In the Draft TAR analysis, there were only a few
technologies for a few manufacturers that were projected to have
stranded capital costs.
    As more technologies are included in this analysis, and as the CAFE
model has been expanded to account for platform and engine sharing and
updated with redesign and refresh cycles, accounting for stranded
capital has become increasingly complex. Separately, the fact that
manufacturers may be shifting their investment strategies in ways that
may affect stranded capital calculations was considered. For instance,
Ford and General Motors agreed to jointly develop next generation
transmission technologies,\120\ and some suppliers sell similar
transmissions to multiple manufacturers. These arrangements allow
manufacturers to share in capital expenditures, or amortize expenses
more quickly. Manufacturers increasingly share parts on vehicles around
the globe, achieving greater scale and greatly affecting tooling
strategies and costs. Given these trends in the industry and their
uncertain effect on capital amortization, and given the difficulty of
handling this uncertainty in the CAFE model, this analysis does not
account for stranded capital. However, these trends will be monitored
to assess the role of stranded capital moving forward.
---------------------------------------------------------------------------

    \120\ See, e.g., Nick Bunkley, Ford to invest $1.4 billion to
build 10-speed transmissions for 2017 F-150, Automotive News (Apr.
26, 2016), http://www.autonews.com/article/20160426/OEM01/160429878/
ford-to-invest-$1.4-billion-to-build-10-speed-transmissions-for-
2017.
---------------------------------------------------------------------------

    The analysis continues to rely on projected refresh and redesign
cycles in the CAFE model to moderate the cadence for technology
adoption and limit the occurrence of stranded capital and the need to
account for it. Stranded capital is an important consideration to
appropriately account for costs if there is too rapid of a turnover for
certain technologies.
(4) Cost Learning
    Manufacturers make improvements to production processes over time,
often resulting in lower costs. Today's analysis estimates cost
learning by considering Wright's learning theory, which states that as
every time cumulative volume for a product doubles, the cost lowers by
a scalar factor. The analysis accounts for learning effects with model
year-based cost learning forecasts for each technology that reduce
direct manufacturing costs over time. Historical use of technologies
were evaluated, and industry forecasts were reviewed to estimate future
volumes for the purpose of developing the model year-based technology
cost learning curves. The CAFE model does not dynamically update
learning curves, based on compliance pathways chosen in simulation.
    As discussed above, cost inputs to the CAFE model incorporate
estimates of volume-based learning. As an alternative approach, Volpe
Center staff have considered modifications such that the CAFE model
would calculate degrees of volume-based learning dynamically,
responding to the model's application of affected technologies. While
it is intuitive that the degree of cost reduction achieved through
experience producing a given technology should depend on the actual
accumulated experience (i.e., volume) producing that technology, staff
have thus far found such dynamic implementation in the CAFE model
infeasible. Insufficient data has been available regarding
manufacturers' historical application of specific technology. Also,
insofar as underlying direct manufacturing costs already make some
assumptions about volume and scale, insufficient information is
currently available to determine how to dynamically adjust these
underlying costs. It should be noted that if learning responds
dynamically to volume, and volume responds dynamically to learning, an
internally consistent model solution would likely require iteration of
the CAFE model to seek a stable solution within the model's
representation multiyear planning. Thus far, these challenges suggest
it would be infeasible to calculate degrees of volume-based learning in
a manner that responds dynamically to modeled technology application.
Nevertheless, the agencies invite comment on the issue, and seek data
and methods that would provide the basis for a practicable approach to
doing so.
    Today's analysis also updates the way learning effects apply to
costs. In the Draft TAR analysis, NHTSA applied learning curves only to
the incremental direct manufacturing costs or costs over the previous
technology on the tech tree. In practice, two things were observed: (1)
If the incremental direct manufacturing costs were positive,
technologies could not become less expensive than their predecessors on
the tech tree, and (2) absolute costs over baseline technology depended
on the learning curves of root technologies on the tech tree. Today's
analysis applies learning effects to the incremental cost over the null
technology state on the tech tree. After this step, the analysis
calculates year-by-year incremental costs over preceding technologies
on the tech tree to create the CAFE model inputs.
    Direct manufacturing costs and learning effects for many
technologies were reviewed by evaluating historical use of technologies
and industry forecasts to estimate future volumes. This approach
produced reasonable estimates for technologies already in production.
For technologies not yet in production in MY 2016, the cumulative
volume in MY 2016 is zero, because manufacturers have not yet produced
the technologies. For pre-production cost estimates, the analysis often
relies on confidential business information sources to predict future
costs. Many sources for pre-production cost estimates include
significant learning effects, often providing cost estimates assuming
high volume production, and often for a timeframe late in the first
production generation or early in the second generation of the
technology. Rapid doubling and re-doubling of a low cumulative volume
base with Wright's learning curves can provide unrealistic cost
estimates. In addition, direct manufacturing cost projections can vary
depending on the initial production volume assumed. Direct costs with
learning were carefully examined, and adjustments were made to the
starting

point for those technologies on the learning curve to better align with
the assumptions used for the initial direct cost estimate. See PRIA
Chapter 9 for more detailed information on cost learning.
(b) Technology Effectiveness
(1) Technology Effectiveness Simulation Modeling
    Full-vehicle simulation modeling was used to estimate the fuel
economy improvements manufacturers could make to their fleet by adding
new technologies, taking into account MY 2016 vehicle specifications,
as well as how combinations of technologies interact. Full-vehicle
simulation modeling uses computer software and physics-based models to
predict how combinations of technologies perform together.
    The simulation and modeling requires detailed specifications for
each technology that describes its efficiency and performance-related
characteristics. Those specifications generally come from design
specifications, laboratory measurements, simulation or modeling, and
may involve additional analysis. For example, the analysis used engine
maps showing fuel use vs. engine torque vs. engine speed, and
transmission maps taking into account gear efficiency for a range of
loads and speeds. With physics-based technology specifications, full-
vehicle simulation modeling can be used to estimate technology
effectiveness for various combinations and permutations of technologies
for many vehicle classes. To develop the specifications used for the
simulation and modeling, laboratory test data was evaluated for
production and pre-production technologies, technical publications,
manufacturer and supplier CBI, and simulation modeling of specific
technologies. Evaluating recently introduced production products to
inform the technology effectiveness models of emerging technologies is
preferred because doing so allows for a more reliable analysis of
incremental improvements over previous technologies; however, some
technologies were considered that are not yet in production. As
technologies evolve and new applications emerge, this work will be
continued and may include additional technologies and/or updated
modeling for the final rule. The details of new and emerging
technologies are discussed in PRIA Chapter 6.
    Using full-vehicle simulation modeling has two primary advantages
over using single or limited point estimates for fuel efficiency
improvements of technologies. First, technology effectiveness often
differs significantly depending on the type of vehicle and the other
technologies that are on the vehicle, and this is shown in full-vehicle
simulations. Different technologies may provide different fuel economy
improvements depending on whether they are implemented alone or in
tandem with other technologies. Single point estimates often
oversimplify these important, complex relationships and lead to less
accurate effectiveness estimates. Also, because manufacturers often
implement a number of fuel-saving technologies simultaneously at
vehicle redesigns, it is generally difficult to isolate the effect of
individual technologies using laboratory measurement of production
vehicles alone. Simulation modeling offers the opportunity to isolate
the effects of individual technologies by using a single or small
number of baseline 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 and reduces
potential double counting or undercounting technology effectiveness.
Note: It is most important that the incremental effectiveness of each
technology and combinations be accurate and relative to a consistent
baseline, because it is the incremental effectiveness that is applied
to each vehicle model/configuration in the MY 2016 baseline fleet (and
to each vehicle model/configuration's absolute fuel economy value) to
determine the absolute fuel economy of the model/configuration with the
additional technology. The absolute fuel economy values of the
simulation modeling runs by themselves are used only to determine the
incremental effectiveness and are never used directly to assign an
absolute fuel economy value to any vehicle model/configuration for the
rulemaking analysis. Therefore, commenters on technology effectiveness
should be specific about the incremental effectiveness of technologies
relative to other specifically defined technologies. The fuel economy
of a specific vehicle or simulation modeling run in isolation may be
less useful.
    Second, full-vehicle simulation modeling requires explicit
specifications and assumptions for each technology; therefore, these
assumptions can be presented for public review and comment. For
instance, transmission gear efficiencies, shift logic, and gear ratios
are explicitly stated as model inputs and are available for review and
comment. For today's analysis, every effort was made to make the input
specifications and modeling assumptions available for review and
comment. PRIA Chapter 6 and referenced documents provide more detailed
information.
    Technology development and application will be monitored to acquire
more information for the final rule. The agencies may update the
analysis for the final rule based on comments and/or new information
that becomes available.
    Today's analysis utilizes effectiveness estimates for technologies
developed using Autonomie software,\121\ a physics-based full-vehicle
simulation tool developed and maintained by the Department of Energy's
ANL. Autonomie has a long history of development and widespread
application by users in industry, academia, research institutions and
government.\122\ Real-world use has contributed significantly to
aspects of Autonomie important to producing realistic estimates of fuel
economy and CO2 emission rates, such as estimation and
consideration of performance, utility, and driveability metrics (e.g.,
towing capability, shift business, frequency of engine on/off
transitions). This steadily increasing realism has, in turn, steadily
increased confidence in the appropriateness of using Autonomie to make
significant investment decisions. Notably, DOE uses Autonomie for
analysis supporting budget priorities and plans for programs managed by
its Vehicle Technologies Office (VTO) and to decide among competing
vehicle technology R&D projects.
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    \121\ More information about Autonomie is available at https://www.anl.gov/technology/project/autonomie-automotive-system-design
(last accessed June 21, 2018).
    \122\ ANL Model Documentation, ``A Detailed Vehicle Simulation
Process To Support CAFE Standards'' ANL/ESD-18/6.
---------------------------------------------------------------------------

    In the 2015 National Academies of Science (NAS) study of fuel
economy improving technologies, the Committee recommended that the
agencies use full-vehicle simulation to improve the analysis method of
estimating technology effectiveness.\123\ The committee acknowledged
that developing and executing tens or hundreds of thousands of
constantly changing vehicle packages models in

real-time is extremely challenging. While initially this approach was
not considered practical to implement, a process developed by Argonne
in collaboration with NHTSA and the DOT Volpe Center has succeeded in
enabling large scale simulation modeling. For more details about the
Autonomie simulation model and its submodels and inputs, see PRIA
Chapter 6.2.
---------------------------------------------------------------------------

    \123\ National Research Council. 2015. Cost, Effectiveness, and
Deployment of Fuel Economy Technologies for Light-Duty Vehicles.
Washington, DC: The National Academies Press [hereinafter ``2015 NAS
Report''] at pg. 263, available at https://www.nap.edu/catalog/21744/cost-effectiveness-and-deployment-of-fuel-economy-technologies-for-light-duty-vehicles (last accessed June 21, 2018).
---------------------------------------------------------------------------

    Today's analysis modeled more than 50 fuel economy-improving
technologies, and combinations thereof, on 10 vehicle types (an
increase from five vehicle types in NHTSA's Draft TAR analysis). While
10 vehicle types may seem like a small number, a large portion of the
production volume in the MY 2016 fleet have specifications that are
very similar, especially in highly competitive segments (for instance,
many mid-sized sedans, many small SUVs, and many large SUVs coalesce
around similar specifications, respectively), and baseline simulations
have been aligned around these modal specifications. The sequential
addition of these technologies generated more than 100,000 unique
technology combinations per vehicle class. The analysis included 10
technology classes, so more than one million full-vehicle simulations
were run. In addition, simulation modeling was conducted to determine
the appropriate amount of engine downsizing needed to maintain baseline
performance across all modeled vehicle performance metrics when
advanced mass reduction technology or advanced engine technology was
applied, so these simulations take into account performance neutrality,
given logical engine down-sizing opportunities associated with specific
technologies.
    Some baseline vehicle assumptions used in the simulation modeling
were updated based on public comment and the assessment of the MY 2016
production fleet. The analysis included updated assumptions about curb
weight, component inertia, as well as technology properties like
baseline rolling resistance, aerodynamic drag coefficients, and frontal
areas. Many of the assumptions are aligned with published research from
the Department of Energy's Vehicle Technologies Office and other
independent sources.\124\ Additional transmission technologies and more
levels of aerodynamic technologies than NHTSA presented in the Draft
TAR analysis were also added for today's analysis. Having additional
technologies allowed the agencies to assign baselines and estimate
fuel-savings opportunities with more precision.
---------------------------------------------------------------------------

    \124\ Pannone, G. ``Technical Analysis of Vehicle Load Reduction
Potential for Advanced Clear Cars,'' April 29, 2015. Available at
https://www.arb.ca.gov/research/apr/past/13-313.pdf (last accessed
June 21, 2018).
---------------------------------------------------------------------------

    The 10 vehicle types (referred to as ``technology classes'' in the
modeling documentation) are shown in Table II-7. Each vehicle type
(technology class) represented a large segment of vehicles, such as
medium cars, small SUVs, and medium performance SUVs.\125\ Baseline
parameters were defined with ANL for each technology class, including
baseline curb weight, time required to accelerate from stop to 60 miles
per hour, time required to accelerate from 50 miles per hour to 80
miles per hour, ability of the vehicle to maintain constant 65 miles
per hour speed on a six percent upgrade, and (for some classes)
assumptions about towing capability.
---------------------------------------------------------------------------

    \125\ Separate technology classes were created for high
performance and low performance vehicles to better account for
performance diversity across the fleet.

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    From these baseline specifications, incremental combinations of
fuel saving technologies were applied. As the combinations of
technologies change, so too may predicted performance.
    The analysis attempts to maintain performance by resizing engines
at a few specific incremental technology steps. Steps from one
technology to another typically associated with a major vehicle
redesign, or engine redesign, were identified, and engine resizing was
restricted only to these steps. The analysis allowed engine resizing
when mass reduction of 10% or greater was applied to the vehicle glider
mass,\126\ and when one powertrain architecture was replaced with
another architecture.\127\ The analysis resized engines to the extent
that performance was maintained for the least capable performance
criteria to maintain vehicle utility for that criteria; therefore,
sometimes other performance attributes may improve. For instance, the
amount of engine resizing may be determined based on its high speed
acceleration time if it is the least capable criteria, but that
resizing may also improve the low speed acceleration time.\128\ The
analysis did not re-size the engine in response to adding technologies
that have small effects on vehicle performance. For instance, if a
vehicle's weight is reduced by a small amount causing the 0-60 mile per
hour time to improve slightly, the analysis would not resize the
engine. Manufacturers have repeatedly told the agencies that the high
costs for redesign and the increased manufacturing complexity that
would result from resizing engines for such small changes in the
vehicle preclude doing so. The analysis should not, in fact, include
engine resizing with the application of every technology or for
combinations of technologies that drive small performance changes so
that the analysis better reflects what is feasible for manufacturers to
do.\129\
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    \126\ The vehicle glider is defined here as the vehicle without
the engine, transmission, and driveline. See PRIA Chapter 6.3 for
further information.
    \127\ Some engine and accessory technologies may be added to an
engine without an engine architecture change. For instance,
manufacturers may adapt, but not replace engine architectures to
include cylinder deactivation, variable valve lift, belt-integrated
starter generators, and other basic technologies. However, switching
from a naturally aspirated engine to a turbo-downsized engine is an
engine architecture change typically associated with a major
redesign and radical change in engine displacement.
    \128\ The simulation database, or summary of simulation outputs,
includes all of the estimated performance metrics for each
combination of technology as modeled.
    \129\ For instance, a vehicle would not get a modestly bigger
engine if the vehicle comes with floor mats, nor would the vehicle
get a modestly smaller engine without floor mats. This example
demonstrates small levels of mass reduction. If manufacturers
resized engines for small changes, manufacturers would have
dramatically more part complexity, potentially losing economies of
scale.
---------------------------------------------------------------------------

2. CAFE model
    The CAFE model is designed to simulate compliance with a given set
of CAFE or CO2 standards for each manufacturer that sells
vehicles in the United States. The model begins with a

representation of the MY 2016 vehicle model offerings for each
manufacturer that includes the specific engines and transmissions on
each model variant, observed sales volumes, and all fuel economy
improving technology that is already present on those vehicles. From
there the model adds technology, in response to the standards being
considered, in a way that minimizes the cost of compliance and reflects
many real-world constraints faced by automobile manufacturers. The
model addresses fleet year-by-year compliance, taking into
consideration vehicle refresh and redesign schedules and shared
platforms, engines, and transmissions among vehicles.
    As a result of simulating compliance, the CAFE model provides the
technology pathways that manufacturers could use to comply with
regulations, including how technologies could be applied to each of
their vehicle model/configurations in response to a given set of
standards. The model calculates the impacts of the simulated standard:
Technology costs, fuel savings (both in gallons and dollars),
CO2 reductions, social costs and benefits, and safety
impacts.
    The current analysis reflects several changes made to the CAFE
model since 2012, when NHTSA used the model to estimate the effects,
costs, and benefits of final CAFE standards for light-duty vehicles
produced during MYs 2017-2021 and augural standards for MYs 2022-2025.
The changes are discussed in Section II.A.1, above, and PRIA Chapter 6.
3. Assumptions About Individual Technology Cost and Effectiveness
Values
    Cost and effectiveness values were estimated for each technology
included in the analysis, with a summary list of all technologies
provided in Table II-1 (List of Technologies with Data Sources for
Technology Assignments) of Preamble Chapter II.B, above. In all, more
than 50 technologies were considered in today's analysis, and the
analysis evaluated many combinations of these technologies on many
applications. Potential issues in assessing technology effectiveness
and cost were identified, including:
     Baseline (MY 2016) vehicle technology level assessed as
too low, or too high. Compliance information was extensively reviewed
and supplemented with available literature on many MY 2016 vehicle
models. Manufacturers could also review the baseline technology
assignments for their vehicles, and the analysis incorporates feedback
received from manufacturers.
     Technology costs too low or too high. Tear down cost
studies, CBI, literature, and the 2015 NAS study information were
referenced to estimate technology costs. In cases that one technology
appeared exemplary on cost and effectiveness relative to all other
technologies, information was acquired from additional sources to
confirm or reject assumptions. Cost assumptions for emerging
technologies are continuously being evaluated.
     Technology effectiveness too high or too low in
combination with other vehicle technologies. Technology effectiveness
was evaluated using the Autonomie full-vehicle simulation modeling,
taking into account the impact of other technologies on the vehicle and
the vehicle type. Inputs and modeling for the analysis took into
account laboratory test data for production and some pre-production
technologies, technical publications, manufacturer and supplier CBI,
and simulation modeling of specific technologies. Evaluating recently
introduced production products to inform the technology effectiveness
models of emerging technologies was preferred; however, some
technologies that are not yet in production were considered, via CBI.
Simulation modeling used carefully chosen baseline configurations to
provide a consistent, reasonable reference point for the incremental
effectiveness estimates.
     Vehicle performance not considered or applied in an
infeasible manner. Performance criteria, including low speed
acceleration (0-60 mph time), high speed acceleration (50-80 mph time),
towing, and gradeability (six percent grade at 65 mph) were also
considered. In the simulation modeling, resizing was applied to achieve
the same performance level as the baseline for the least capable
performance criteria but only with significant design changes. The
analysis struck a balance by employing a frequency of engine downsizing
that took product complexity and economies of scale into account.
     Availability of technologies for production application
too soon or too late. A number of technologies were evaluated that are
not yet in production. CBI was gathered on the maturity and timing of
these technologies and the likely cadence at which manufacturers might
adopt these technologies.
     Product complexity and design cadence constraints too low
or too high. Product platforms, refresh and redesign cycles, shared
engines, and shared transmissions were also considered in the analysis.
Product complexity and the cadence of product launches were matched to
historical values for each manufacturer.
     Customer acceptance under estimated or over estimated.
Resale prices for hybrid vehicles, electric vehicles, and internal
combustion engine vehicles were evaluated to assess consumer
willingness to pay for those technologies. The analysis accounts for
the differential in the cost for those technologies and the amount
consumers have actually paid for those technologies. Separately, new
dual-clutch transmissions and manual transmissions were applied to
vehicles already equipped with these transmission architectures.
    Please provide comments on all assumptions for fuel economy and
CO2 technology costs, effectiveness, availability, and
applicability to vehicles in the fleet.
    The technology effectiveness modeling results show effectiveness of
a technology often varies with the type of vehicle and the other
technologies that are on the vehicle. Figure II-1 and Figure II-2 show
the range of effectiveness for each technology for the range of vehicle
types and technology combinations included in this NPRM analysis. The
data reflect the change in effectiveness for applying each technology
by itself while all other technologies are held unchanged. The data
show the improvement in fuel consumption (in gallons per mile) and
tailpipe CO2 over the combined 2-cycle test procedures. For
many technologies, effectiveness values ranged widely; only a few
technologies for which effectiveness may be reasonably represented as a
fixed offset were identified.
    For engine technologies, the effectiveness improvement range is
relative to a comparably equipped vehicle with only variable valve
timing (VVT) on the engine. For automatic transmission technologies,
the effectiveness improvement range is over a 5-speed automatic
transmission. For manual transmission technologies, the effectiveness
improvement range is over a 5-speed manual transmission. For road load
technologies like aerodynamics, rolling resistance, and mass reduction,
the effectiveness improvement ranges are relative to the least advanced
technology state, respectively. For hybrid and electric drive systems
that wholly replace an engine and transmission, the effectiveness
improvement ranges are relative to a comparably equipped vehicle with a
basic engine with VVT only and a 5-speed automatic transmission. For
hybrid or electrification technologies that complement other advanced
engine

and transmission technologies, the effectiveness improvement ranges are
relative to a comparably equipped vehicle without the hybrid or
electrification technologies (for instance, parallel strong hybrids and
belt integrated starter generators retain engine technologies, such as
a turbo charged engine or an Atkinson cycle engine). Many technologies
have a wide range of estimated effectiveness values. Figure II-3 below
shows a hierarchy of technologies discussed.
BILLING CODE 4910-59-P
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4. Engine Technologies
    There are a number of engine technologies that manufacturers can
use to improve fuel economy and CO2. Some engine
technologies can be incorporated into existing engines with minor or
moderate changes to the engines, but many engine technologies require
an entirely new engine architecture.
    In this section and for this analysis, the terms ``basic engine
technologies'' and ``advanced engine technologies'' are used only to
define how the CAFE model applies a specific engine technology and
handles incremental costs and effectiveness improvements. ``Basic
engine technologies'' refer to technologies that, in many cases, can be
adapted to an existing engine with minor or moderate changes to the
engine. ``Advanced engine technologies'' refer to technologies that
generally require significant changes or an entirely new engine
architecture. In the CAFE model, basic engine technologies may be
applied in combination with other basic engine technologies; advanced
engine technologies (defined by an engine map) stand alone as an
exclusive engine technology. The words ``basic'' and ``advanced'' are
not meant to confer any information about the level of sophistication
of the technology. Also, many advanced engine technology

definitions include some basic engine technologies, but these basic
technologies are already accounted for in the costs and effectiveness
values of the advance engine. The ``basic engine technologies'' need
not be (and are not) applied in addition to the ``advanced engine
technologies'' in the CAFE model.
    Engines come in a wide variety of shapes, sizes, and
configurations, and the incremental engine costs and effectiveness
values often depend on engine architecture. The agencies modeled single
overhead cam (SOHC), dual overhead cam (DOHC), and overhead valve (OHV)
engines separately to account for differences in engine architecture.
The agencies adjusted costs for some engine technologies based on the
number of cylinders and number of banks in the engine, and the agencies
evaluated many production engines to better understand how costs and
capabilities may vary with engine configuration. Table II-8, Table II-
9, Table II-10 below shows the summary of absolute costs \130\ for
different technologies.
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    \130\ ``Absolute'' being in reference to cost above the lowest
level of technology considered in simulations. For instance, an
engine of the same architecture with no VVT, VVL, SGDI, or DEAC.

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BILLING CODE 4910-59-C
    Many types of production powertrains were reviewed and tested for
this analysis, and engine maps were developed for each combination of

engine technologies. For a given engine configuration, some production
engines may be less efficient than the engine maps presented in the
analysis, and some may be more efficient. Developing engine maps that
reasonably represented most vehicles equipped with the engine
technology, and that are in production today, was the preferred
approach for this analysis. Additionally, some advanced engines were
included in the simulation that are not yet in production. The engine
maps for these engines were either based on CBI or were theoretical.
The most recently released production engines are still being reviewed,
and the analysis may include updated engine maps in the future or add
entirely new engine maps to the analysis if either action could improve
the quality of the fleet-wide analysis.
    Stakeholders provided many comments on the engine maps that were
presented in the Draft TAR. These comments were considered, and today's
analysis utilizes several engine maps that were updated since the Draft
TAR. Most notably, for turbocharged and downsized engines, the engine
maps were adjusted in high torque, low speed operating conditions to
address engine knock with regular octane fuel to align with the fuel
octane that manufacturers recommend be used for the majority of
vehicles. In the Draft TAR, NHTSA assumed high octane fuel to develop
engine maps. See the discussion below and in PRIA Chapter 6.3 for more
details. Please provide comment on the appropriateness of assuming the
use of lower octane fuels.
(a) ``Basic'' Engine Technologies
    The four ``basic'' engine technologies in today's model are
Variable Valve Timing (VVT), Variable Valve Lift (VVL), Stoichiometric
Gasoline Direct Injection (SGDI), and basic Cylinder Deactivation
(DEAC). Over the last decade, manufacturers upgraded many engines with
these engine technologies. Implementing these technologies involves
changes to the cylinder head of the engine, but the engine block,
crankshaft, pistons, and connecting rods require few, if any, changes.
In today's analysis, manufacturers may apply the four basic engine
technologies in various combinations, just as manufacturers have done
recently.
(1) Variable Valve Timing (VVT)
    Variable Valve Timing (VVT) is a family of valve-train designs that
dynamically adjusts the timing of the intake valves, exhaust valves, or
both, in relation to piston position. This family of technologies
reduces pumping losses. VVT is nearly universally used in the MY 2016
fleet.
(2) Variable Valve Lift (VVL)
    Variable Valve Lift (VVL) dynamically adjusts the travel of the
valves to optimize airflow over a broad range of engine operating
conditions. The technology increases effectiveness by reducing pumping
losses and may improve efficiency by affecting in-cylinder charge (fuel
and air mixture), motion, and combustion.
(3) Stoichiometric Gasoline Direct Injection (SGDI)
    Stoichiometric Gasoline Direct Injection (SGDI) sprays fuel at high
pressure directly into the combustion chamber, which provides cooling
of the in-cylinder charge via in-cylinder fuel vaporization to improve
spark knock tolerance and enable an increase in compression ratio and/
or more optimal spark timing for improved efficiency. SGDI appears in
about half of basic engines produced in MY 2016, and the technology is
used in many advanced engines as well.
(4) Basic Cylinder Deactivation (DEAC)
    Basic Cylinder Deactivation (DEAC) disables intake and exhaust
valves and prevents fuel injection into some cylinders during light-
load operation. The engine runs temporarily as though it were a smaller
engine, which reduces pumping losses and improves efficiency.
Manufacturers typically disable one-cylinder bank with basic cylinder
deactivation. In the MY 2016 fleet, manufacturers used DEAC on V6, V8,
V10, and V12 engines on OHV, SOHC, and DOHC engine configurations. With
some engine configurations in some operating conditions, DEAC creates
noise-vibration-and-harshness (NVH) challenges. NVH challenges are
significant for V6 and I4 DEAC configurations. For I4 engine
configurations, manufacturers can operate the DEAC function of an
engine in very few operating conditions, with limited potential to save
fuel. No manufacturers sold I4 DEAC engines in the MY 2016 fleet.
Typically, the smaller the engine displacement, the less opportunity
DEAC provides to improve fuel consumption.
    Manufacturers and suppliers continue to evaluate more improved
versions of cylinder deactivation, including advanced cylinder
deactivation and pairing basic cylinder deactivation with turbo charged
engines. No manufacturers produced such technologies in the MY 2016
fleet. Advanced cylinder deactivation and turbo technologies were
modeled and considered separately in today's analysis.
(b) ``Advanced'' Engine Technologies
    The analysis included ``advanced'' engine technologies that can
deliver high levels of effectiveness but often require a significant
engine design change or a new engine architecture. In the CAFE model,
``basic'' engine technologies may be considered in combination and
applied before advanced engine technologies. ``Advanced'' engine
technologies generally include one or more basic engine technologies in
the simulation, without the need to layer on ``basic'' engine
technologies on top of ``advanced'' engines. Once an advanced engine
technology is applied, the model does not reconsider the basic engine
technologies. The characterization of each advanced engine technology
takes into account the prerequisite technologies.
    Many of the newest advanced engine technologies improve
effectiveness over their predecessors, but the engines may also include
sophisticated materials or manufacturing processes that contribute to
efficiency improvements. For instance, one recently introduced turbo
charged engine uses sodium filled valve stems.\131\ Another recently
introduced high compression ratio engine uses a sophisticated laser
cladding process to manufacture valve seats and improve airflow.\132\
To fully consider these advancements (and their potential benefits),
the incremental costs of these technologies, as well as the
effectiveness improvements, must be accounted for.
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    \131\ See Honda, ``2018 Honda Accord Press Kit--Powertrain,''
Oct. 2, 2017. Available at http://news.honda.com/newsandviews/article.aspx?g=honda-automobiles&id=9932-en. (last accessed June 21,
2018).
    \132\ Hakariya et al., ``The New Toyota Inline 4-Cylinder 2.5L
Gasoline Engine,'' SAE Technical Paper 2017-01-1021 (Mar. 28, 2017),
available at https://www.sae.org/publications/technical-papers/content/2017-01-1021/.
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(1) Turbocharged Engines
    Turbo engines recover energy from hot exhaust gas and compress
intake air, thereby increasing available airflow and increasing
specific power level. Due to specific power improvements on turbo
engines, engine displacement can be downsized. The downsizing reduces
pumping losses and improves fuel economy at lower loads. For the NPRM
analysis, a level of downsizing is assumed to be applied that achieves
performance similar to the baseline naturally-aspirated engine. This
assumes manufacturers would apply the benefits toward improved fuel
economy

and not trade off fuel economy improvements to increase overall vehicle
performance. In practice, manufacturers have often also improved some
vehicle performance attributes at the expense of not maximizing
potential fuel economy improvements.
    Manufacturers may develop engines to operate on varying levels of
boost,\133\ with higher levels of boost achieving higher engine
specific power and enabling greater levels of engine downsizing and
corresponding reductions in pumping losses for improved efficiency.
However, engines operating at higher boost levels are generally more
susceptible to engine knock,\134\ especially at higher torques and low
engine speeds. Additionally, engines with higher boost levels typically
require larger induction and exhaust system components, dissipate
greater amounts of heat, and with greater levels of engine downsizing
have increased challenges with turbo lag.\135\ For these reasons, three
levels of turbo downsizing technologies are separately modeled in this
analysis.
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    \133\ Boost refers to the degree to which the turbocharger
compresses the intake air for the engine, which may affect the
specific power of the engine.
    \134\ Knock refers to rapid uncontrolled combustion in the
cylinder part way through the combustion process, which can create
an audible sound and can damage the engine.
    \135\ Turbo lag refers to the delay time between power demanded
and power delivered; it is typically associated with rapid
accelerations from a stopped vehicle at idle.
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    The analysis also modeled turbocharged engines with parallel hybrid
technology. In simulations with high stringencies, many manufacturers
produced turbo-hybrid electric vehicles. In the MY 2016 fleet, of the
vehicles that use parallel hybrid technology, many use turbocharged
engines.
    Since the Draft TAR, the turbo family engine maps were updated to
reflect operation on 87 AKI regular octane fuel.\136\ In the Draft TAR,
turbo engine maps were developed assuming premium fuel. For this
rulemaking analyses, pathways to improving fuel economy and
CO2 are analyzed, while also maintaining vehicle
performance, capability, and other attributes. This includes assuming
there is no change in the fuel octane required to operate the vehicle.
Using 87 AKI regular octane fuel is consistent with the fuel octane
that manufacturers specify for the majority of vehicles, and enables
the modeling to account for important design and calibration issues
associated with regular octane fuel. Using the updated criteria assures
the NPRM analysis reflects real-world constraints faced by
manufacturers to assure engine durability, and acceptable drivability,
noise and harshness, and addresses the over-estimation of potential
fuel economy improvements related to the fuel octane assumptions, which
did not fully account for these constraints, in the Draft TAR. Compared
with the NHTSA analysis in the Draft TAR, these engine maps adjust the
fuel use at high torque and low speed operation and at high speed
operation to fully account for knock limitations with regular octane
fuel.
---------------------------------------------------------------------------

    \136\ Specifically, 87 Anti-Knock Index (AKI) Tier 3
certification fuel. 87 AKI is also known as 87 (R+M)/2 or 87
(Research Octane + Motor Octane)/2.
---------------------------------------------------------------------------

    The analysis assumes engine downsizing with the addition of turbo
technology. For instance, in the simulations, manufacturers may have
replaced a naturally-aspirated V8 engine with a turbo V6 engine, and
manufacturers may have replaced a naturally-aspirated V6 engine with a
turbo I4 engine. When manufacturers reduced the number of banks or
cylinders of an engine, some cost savings is projected due to fewer
cylinders and fewer valves. Such cost savings is projected to help
offset the additional costs of turbo charger specific hardware, making
turbo downsizing a very attractive technology progression for some
engines.\137\
---------------------------------------------------------------------------

    \137\ In particular, the step from a naturally-aspirated V6 to a
turbo I4 was particularly cost effective in agency simulations.
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(a) TURBO1
    Level 1 Turbo Charging (TURBO1) adds a turbo charger to a DOHC
engine with SGDI, VVT, and continuously VVL. The engine operates at up
to 18 bar brake mean effective pressure (BMEP).
    Manufacturers used Turbo1 technology in a little less than a
quarter of the MY 2016 fleet with particularly high concentrations in
premium vehicles.
(b) TURBO2
    Level 2 Turbo Charging (TURBO2) operates at up to 24 bar BMEP. The
step from Turbo1 to Turbo2 is accompanied with additional displacement
downsizing for reduced pumping losses. Very few manufacturers have
Turbo2 technology in the MY 2016 fleet.
(c) CEGR1
    Turbo Charging with Cooled Exhaust Gas Recirculation (CEGR1)
improves the knock resistance of Turbo2 engines by mixing cooled inert
exhaust gases into the engine's air intake. That allows greater boost
levels, more optimal spark timing for improved fuel economy, and
performance and greater engine downsizing for lower pumping losses.
CEGR1 technology is used in only a few vehicles in the MY 2016 fleet,
and many of these vehicles include high-performance utility either for
towing or acceleration.
(a) Turbocharged Engine Technologies Not Considered
    Previous analyses considered turbo charged engines with even higher
BMEP than today's Turbo2 and CEGR1 technologies, but today's analysis
does not present 27 bar BMEP turbo engines. Turbo engines with very
high BMEP have demonstrated limited potential to improve fuel economy
due to practical limitations on engine downsizing and tradeoffs with
launch performance and drivability. Based on the analysis, and based on
CBI, CEGR2 turbo engine technology was not included in this NPRM
analysis.
(2) High Compression Ratio Engines (Atkinson Cycle Engines)
    Atkinson cycle gasoline engines use changes in valve timing (e.g.,
late-intake-valve-closing or LIVC) to reduce the effective compression
ratio while maintaining the expansion ratio. This approach allows a
reduction in top-dead-center (TDC) clearance ratio (e.g., increase in
``mechanical'' or ``physical'' compression ratio) to increase the
effective expansion ratio without increasing the effective compression
ratio to a point that knock-limited operation is encountered.
Increasing the expansion ratio in this manner improves thermal
efficiency but also lowers peak BMEP, particularly at lower engine
speeds.
    Often knock concerns for these engines limit applications in high
load, low RPM conditions. Some manufacturers have mitigated knock
concerns by lowering back pressure with long, intricate exhaust
systems, but these systems must balance knock performance with
emissions tradeoffs, and the increased size of the exhaust manifold can
pose packaging concerns, particularly on V-engine configurations.\138\
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    \138\ Some HCR1 4-cylinder (I-4) engines use an intricate 4-2-1
exhaust manifold to lower backpressure and to improve engine
efficiency. Manufacturers sometimes fitted such an exhaust system
into a front-wheel-drive vehicle with an I-4 engine by using a high
underbody tunnel or rearward dashpanel (trading off some interior
space), but packaging such systems on rear-wheel-drive vehicles may
pose challenges, especially if the engine has two banks and would
therefore require room for two such exhaust manifolds.
---------------------------------------------------------------------------

    Only a few manufacturers produced internal combustion engine
vehicles with Atkinson cycle engines in MY

2016; however, these engines are commonly paired with hybrid electric
vehicle technologies due to the synergy of peak efficiency of Atkinson
cycle engines and immediate torque from electric motors in strong
hybrids. Atkinson cycle engines are very common on power split hybrids
and are sometimes observed as part of a parallel hybrid system or plug-
in hybrid system.
    Atkinson cycle engines played a prominent role in EPA's January
2017 final determination, which has since been withdrawn. Today's
analysis recognizes that the technology is not suitable for many
vehicles due to performance, emissions and packaging issues, and/or the