optimized vehicle models for each technology class driving the test
cycles we described above. As an example, the Autonomie modeling could
start with 14 initial vehicle models (one for each technology class in
the LD and HDPUV analysis). Those 14 initial vehicle models use a
baseline 5-speed automatic transmission.\131\ ANL then builds 14 new
vehicle models; the only difference between the 14 new vehicle models
and the first set of vehicle models is that the

new vehicle models have a 6-speed automatic transmission. Replacing the
AT5 with an AT6 would lead either to an increase or decrease in the
total weight of the vehicle because each technology class includes
different assumptions about transmission weight. ANL then ensures that
the new vehicle models with the 6-speed automatic transmission meet
their performance metrics. Now we have 28 different vehicle models that
can be simulated on the two-cycle tests. This process is repeated for
each technology option and for each technology class. This results in
fourteen separate datasets, each with over 100,000 results, that
include information about a vehicle model made of specific fuel economy
improving technology and the fuel economy value that the vehicle model
achieved driving its simulated test cycles.
---------------------------------------------------------------------------

    \131\ Note that although both the LD and HDPUV analyses include
a 5-speed automatic transmission, the characteristics of those
transmissions differ between the two analyses.
---------------------------------------------------------------------------

    We condense the million or so datapoints from Autonomie into three
datasets used in the CAFE Model. These three datasets include (1) the
fuel economy value (converted into ``fuel consumption'', which is the
inverse of fuel economy; fuel economy is mpg and fuel consumption is
gallons per mile) that each modeled vehicle achieved while driving the
test cycles, for every technology combination in every technology
class; (2) the fuel economy value for PHEVs driving those test cycles,
when those vehicles drive on gasoline-only in order to comply with
statutory constraints; and (3) optimized battery costs for each vehicle
that adopts some sort of electrified powertrain (this is discussed in
more detail below).
    Now, how does this information translate into the technology
effectiveness data that we use in the CAFE Model? An important feature
of this analysis is that the fuel economy improvement from each
technology and combinations of technologies should be accurate and
relative to a consistent baseline vehicle. We use the absolute fuel
economy values from the full vehicle simulations only to determine the
relative fuel economy improvement from adding a set of technologies to
a vehicle, but not to assign an absolute fuel economy value to any
vehicle model or configuration. For this analysis, the baseline
absolute fuel economy value for each vehicle in the analysis fleet is
based on CAFE compliance data. For subsequent technology changes, we
apply the incremental fuel economy improvement values from one or more
technologies to the baseline fuel economy value to determine the
absolute fuel economy achieved for applying the technology change.
Accordingly, when the CAFE Model is assessing how to cost-effectively
add technology to a vehicle in order to improve the vehicle's fuel
economy value, the CAFE Model calculates the difference in the fuel
economy value from an Autonomie modeled vehicle with less technology
and an Autonomie modeled vehicle with more technology. The relative
difference between the two Autonomie modeled vehicles' fuel economy
values is applied to the actual fuel economy value of a vehicle in the
CAFE Model's baseline fleet.
    Let's return to our Ravine Runner F Series, which has a starting
fuel economy value of just over 26 mpg and a starting technology key
``TURBOD; AT10L2; SS12V; ROLL0; AERO5; MR3.'' The equivalent Autonomie
vehicle model has a starting fuel economy value of just over 30.8 mpg
and is represented by the technology descriptors Midsize_SUV, Perfo,
Micro Hybrid, eng38, AUp, 10, MR3, AERO1, ROLL0. In 2028, the CAFE
Model determines that Generic Motors needs to redesign the Ravine
Runner F Series to reach Generic Motors' new light truck CAFE standard.
The Ravine Runner F Series now has lots of new fuel-economy-improving
technology--it is a parallel strong HEV with a TURBOE engine, an
integrated 8-speed automatic transmission, 30% improvement in ROLL, 20%
aerodynamic drag reduction, and 10% lighter glider (i.e., mass
reduction). Its new technology key is now P2TRBE, ROLL30, AERO20, MR3.
Table II-5 shows how the incremental fuel economy improvement from the
Autonomie simulations is applied to the Ravine Runner F Series'
starting fuel economy value.

           Table II-5--Example Translation From the Autonomie Effectiveness Database to the CAFE Model
----------------------------------------------------------------------------------------------------------------
                                 Starting technology key/                 Ending technology key/
             Model                technology descriptors        MPG       technology descriptors        MPG
----------------------------------------------------------------------------------------------------------------
CAFE Model.....................  TURBOD; AT10L2; SS12V;             26.1  P2TRBE, ROLL30,                   36.3
                                  ROLL0; AERO5; MR3.                       AERO20, MR3.
Autonomie......................  Midsize_SUV, Perfo,                30.8  Midsize_SUV, Perfo,               42.9
                                  Micro Hybrid, eng38,                     Par HEV, eng37, AUp
                                  AUp, 10, MR3, AERO1,                     8, MR3, AERO4, ROLL3.
                                  ROLL0.
----------------------------------------------------------------------------------------------------------------

    Note that the fuel economy values we obtain from the Autonomie
modeling are based on the city and highway test cycles (i.e., the two-
cycle test) described above. This is because we are statutorily
required to measure vehicle fuel economy based on the two-cycle
test.\132\ In 2008, EPA introduced three additional test cycles to
bring fuel economy ``label'' values from two-cycle testing in line with
the efficiency values consumers were experiencing in the real world,
particularly for hybrids. This is known as 5-cycle testing. Generally,
the revised 5-cycle testing values have proven to be a good
approximation of what consumers will experience while driving,
significantly better than the previous two-cycle test values. Although
the compliance modeling uses two-cycle fuel economy values, we use the
``on-road'' fuel economy values, which are the ratio of 5-cycle to 2-
cycle testing values (i.e., the CAFE compliance values to the ``label''
values) \133\ to calculate the value of fuel savings to the consumer in
the effects analysis. This is because the 5-cycle test fuel economy
values better represent fuel savings that consumers will experience
from real-world driving. For more information about these calculations,
please see Section 5.3.2 of the CAFE Model Documentation, and our
discussion of the effects analysis later in this section.
---------------------------------------------------------------------------

    \132\ 49 U.S.C. 32904(c) (EPA ``shall measure fuel economy for
each model and calculate average fuel economy for a manufacturer
under testing and calculation procedures prescribed by the
Administrator. However, except under section 32908 of this title,
the Administrator shall use the same procedures for passenger
automobiles the Administrator used for model year 1975 (weighted 55
percent urban cycle and 45 percent highway cycle), or procedures
that give comparable results.'').
    \133\ We apply a certain percent difference between the 2-cycle
test value and 5-cycle test value to represent the gap in compliance
fuel economy and real-world fuel economy.
---------------------------------------------------------------------------

    In sum, we use Autonomie to generate physics-based full vehicle
modeling and simulation technology effectiveness estimates. These
estimates ensure that

our modeling captures differences in technology effectiveness due to
(1) vehicle size and performance relative to other vehicles in the
baseline fleet; (2) other technologies on the vehicle and/or being
added to the vehicle at the same time; and (3) and how the vehicle is
driven. This modeling approach also comports with the NAS 2015
recommendation to use full vehicle modeling supported by application of
lumped improvements at the sub-model level.\134\ The approach allows
the isolation of technology effects in the analysis supporting an
accurate assessment.
---------------------------------------------------------------------------

    \134\ 2015 NAS report, at 292.
---------------------------------------------------------------------------

    In our analysis, ``technology effectiveness values'' are the
relative difference between the fuel economy value for one Autonomie
vehicle model driving the two-cycle tests, and a second Autonomie
vehicle model that uses new technology driving the two-cycle tests. We
add the difference between two Autonomie-generated fuel economy values
to a vehicle in the Market Data Input file's CAFE compliance fuel
economy value. We then calculate the costs and benefits of different
levels of fuel economy standards using the incremental improvement
required to bring a baseline vehicle model's fuel economy value to a
level that contributes to a manufacturer's fleet meeting its CAFE
standard.
    In the next section, Technology Costs, we describe the process of
generating costs for the Technology Costs input file.
4. Technology Costs
    We estimate present and future costs for fuel-saving technologies
based on a vehicle's technology class and engine size. In the
Technologies Input file, there is a separate tab for each technology
class that includes unique costs for that class (depending on the
technology), and a separate tab for each engine size that also contains
unique engine costs for each engine size. These technology cost
estimates are based on three main inputs. First, we estimate direct
manufacturing costs (DMCs), or the component and labor costs of
producing and assembling a vehicle's physical parts and systems. DMCs
generally do not include the indirect costs of tools, capital
equipment, financing costs, engineering, sales, administrative support
or return on investment. We account for these indirect costs via a
scalar markup of DMCs, which is termed the RPE. Finally, costs for
technologies may change over time as industry streamlines design and
manufacturing processes. We estimate potential cost improvements from
improvements in the manufacturing process with learning effects (LEs).
The retail cost of technology 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 DMCs, is important to account for
the real-world price effects of a technology, as well as market
realities. Each of these technology cost components is described
briefly below and in the following individual technology sections, and
in detail in Chapters 2 and 3 of the Draft TSD.
    DMCs are the component and assembly costs of the physical parts and
systems that make up a complete vehicle. We estimate DMCs for
individual technologies in several ways. Broadly, we rely in large part
on costs estimated by the NHTSA-sponsored 2015 NAS study on the Cost,
Effectiveness, and Deployment of Fuel Economy Technologies for LDVs and
other NAS studies on fuel economy technologies; BatPaC, a publicly
available battery pack modeling software developed and maintained by
the DOE's ANL, NHTSA-sponsored teardown studies, and our own analysis
of how much advanced MR technology (i.e., carbon fiber) is available
for vehicles now and in the future; confidential business information
(CBI); and off-cycle and AC efficiency costs from the EPA Proposed
Determination TSD.\135\ While DMCs 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, we use the best
information available at the time of the analysis and will continue to
update cost assumptions for any future analysis.
---------------------------------------------------------------------------

    \135\ Enviromental Protection Agency. 2016. Proposed
Determination on the Appropriateness of the Model Year 2022-2025
Light-Duty Vehicle Greenhouse Gas Emissions Standards under the
Midterm Evaluation: Technical Support Document. Assessment and
Standards Division, Office of Transportation and Air Quality.
Available at: https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P100Q3L4.pdf. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    Our direct costs include materials, labor, and variable energy
costs required to produce and assemble the vehicle; however, direct
costs do not include production overhead, corporate overhead, selling
costs, or dealer costs, which all contribute to the price consumers
ultimately pay for the vehicle. These components of retail prices are
illustrated in Table II-6 below.

                   Table II-6--Retail Price Components
------------------------------------------------------------------------

------------------------------------------------------------------------
                              Direct Costs
------------------------------------------------------------------------
Manufacturing Cost................  Cost of materials, labor, and
                                     variable energy needed for
                                     production.
------------------------------------------------------------------------
                             Indirect Costs
------------------------------------------------------------------------
                           Production Overhead
    Warranty......................  Cost of providing product warranty.
    Research and Development......  Cost of developing and engineering
                                     the product.
    Depreciation and amortization.  Depreciation and amortization of
                                     manufacturing facilities and
                                     equipment.
    Maintenance, repair,            Cost of maintaining and operating
     operations.                     manufacturing facilities and
                                     equipment.
                           Corporate Overhead
    General and Administrative....  Salaries of nonmanufacturing labor,
                                     operations of corporate offices,
                                     etc.
    Retirement....................  Cost of pensions for
                                     nonmanufacturing labor.
    Health Care...................  Cost of health care for
                                     nonmanufacturing labor.
                              Selling Costs
    Transportation................  Cost of transporting manufactured
                                     goods.
    Marketing.....................  Manufacturer costs of advertising
                                     manufactured goods.
                              Dealer Costs
    Dealer selling expense........  Dealer selling and advertising
                                     expense.
    Dealer profit.................  Net Income to dealers from sales of
                                     new vehicles.

    Net income....................  Net income to manufacturers from
                                     production and sales of new
                                     vehicles.
------------------------------------------------------------------------

    To estimate total consumer costs (i.e., both direct and indirect
costs), we multiply a technology's DMCs by an indirect cost factor to
represent the average price for fuel-saving technologies at retail. The
factor that we use is the RPE, and it is the most commonly used to
estimate indirect costs of producing a motor vehicle. The RPE markup
factor is based on an examination of historical financial data
contained in 10-K reports filed by manufacturers with the Securities
and Exchange Commission (SEC). It represents the ratio between the
retail price of motor vehicles and the direct costs of all activities
that manufacturers engage in.
    For more than three decades, the retail price of motor vehicles has
been, on average, roughly 50 percent above the direct cost expenditures
of manufacturers.\136\ This ratio has been remarkably consistent,
averaging roughly 1.5 with minor variations from year to year over this
period. At no point has the RPE markup based on 10-K reports exceeded
1.6 or fallen below 1.4.\137\ During this time frame, the average
annual increase in real direct costs was 2.5 percent, and the average
annual increase in real indirect costs was also 2.5 percent. The RPE
averages 1.5 across the lifetime of technologies of all ages, with a
lower average in earlier years of a technology's life, and, because of
LEs on direct costs, a higher average in later years. Many automotive
industry stakeholders have either endorsed the 1.5 markup,\138\ or have
estimated alternative RPE values. As seen in Table II-7, all estimates
range between 1.4 and 2.0, and most are in the 1.4 to 1.7 range.
---------------------------------------------------------------------------

    \136\ Rogozhin, A. et al. 2009. Automobile Industry Retail Price
Equivalent and Indirect Cost Multipliers. EPA. RTI Project Number
0211577.002.004. Triangle Park, N.C.; Spinney, B.C. et al. 1999.
Advanced Air Bag Systems Cost, Weight, and Lead Time analysis
Summary Report. Contract NO. DTNH22-96-0-12003. Task Orders--001,
003, and 005. Washington, DC.
    \137\ Based on data from 1972-1997 and 2007. Data were not
available for intervening years, but results for 2007 seem to
indicate no significant change in the historical trend.
    \138\ Comment submitted by Chris Nevers, Vice President, Energy
& Environment, Alliance of Automobile Manufacturers via
Regulations.gov. Docket ID No. EPA-HQ-OAR-2018-0283-6186, p. 143.
Available at: https://www.regulations.gov/comment/EPA-HQ-OAR-2018-0283-6186.

            Table II-7--Alternate Estimates of the RPE \139\
------------------------------------------------------------------------
            Author and year                      Value, comments
------------------------------------------------------------------------
Jack Faucett Associates for EPA, 1985..  1.26 initial value, later
                                          corrected to 1.7+ by Sierra
                                          research.
Vyas et al., 2000......................  1.5 for outsourced, 2.0 for
                                          OEM, electric, and hybrid
                                          vehicles.
NRC, 2002..............................  1.4 (corrected to > by Duleep).
McKinsey and Company, 2003.............  1.7 based on European study.
CARB, 2004.............................  1.4 (derived using the JFA
                                          initial 1.26 value, not the
                                          corrected 1.7+ value).
Sierra Research for AAA, 2007..........  2.0 or >, based on Chrysler
                                          data.
Duleep, 2008...........................  1.4, 1.56, 1.7 based on
                                          integration complexity.
NRC, 2011..............................  1.5 for Tier 1 supplier, 2.0
                                          for OEM.
NRC, 2015..............................  1.5 for OEM.
------------------------------------------------------------------------

    An RPE of 1.5 does not imply that manufacturers automatically mark
up each vehicle by exactly 50 percent. Rather, it means that, over
time, the competitive marketplace has resulted in pricing structures
that average out to this relationship across the entire industry.
Prices for any individual model may be marked up at a higher or lower
rate depending on market demand. The consumer who buys a popular
vehicle may, in effect, subsidize the installation of a new technology
in a less marketable vehicle. But, on average, over time and across the
vehicle fleet, the retail price paid by consumers has risen by about
$1.50 for each dollar of direct costs incurred by manufacturers. Based
on our own evaluation and the widespread use and acceptance of the RPE
by automotive industry stakeholders, we have determined that the RPE
provides a reasonable indirect cost markup for use in our analysis. A
detailed discussion of indirect cost methods and the basis for our use
of the RPE to reflect these costs, rather than other indirect cost
markup methods, is available in the Final Regulatory Impact Analysis
(FRIA) for the 2020 final rule.\140\
---------------------------------------------------------------------------

    \139\ Duleep, K.G. 2008. Analysis of Technology Cost and Retail
Price. Presentation to Committee on Assessment of Technologies for
Improving LDV Fuel Economy. January 25, 2008, Detroit, MI.; Jack
Faucett Associates. 1985. Update of EPA's Motor Vehicle Emission
Control Equipment Retail Price Equivalent (RPE) Calculation Formula.
September 4, 1985. Chevy Chase, MD; McKinsey & Company. 2003.
Preface to the Auto Sector Cases. New Horizons--Multinational
Company Investment in Developing Economies. San Francisco, CA.; NRC.
2002. Effectiveness and Impact of Corporate Average Fuel Economy
Standards. The National Academies Press. Washington, DC; NRC. 2011.
Assessment of Fuel Economy Technologies for LDVs. The National
Academies Press. Washington, DC; NRC. 2015. Cost, Effectiveness, and
Deployment of Fuel Economy Technologies in LDVs. The National
Academies Press. Washington, DC; Sierra Research, Inc. 2007. Study
of Industry-Average Mark-Up Factors used to Estimate Changes in
Retail Price Equivalent (RPE) for Automotive Fuel Economy and
Emissions Control Systems. Sierra Research Inc. Sacramento, CA;
Vyas, A. et al. 2000. Comparison of Indirect Cost Multipliers for
Vehicle Manufacturing. Center for Transportation Research, ANL,
April. Argonne, Ill.
    \140\ 2020 FRIA, at pp. 354-76. Available at https://www.nhtsa.gov/sites/nhtsa.gov/files/documents/final_safe_fria_web_version_200701.pdf. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    Finally, manufacturers make improvements to production processes
over time, which often result in lower costs. ``Cost learning''
reflects the effect of experience and volume on the cost of production,
which generally results in better utilization of resources, leading to
higher and more efficient production. As manufacturers gain experience
through production, they refine production techniques, raw material and
component sources, and assembly methods to maximize efficiency and
reduce production costs.
    We estimated cost learning by considering methods established by
T.P. Wright and later expanded upon by J.R. Crawford. Wright, examining
aircraft production, found that every doubling of cumulative production
of airplanes resulted in decreasing labor hours at a fixed percentage.
This fixed percentage is commonly referred to as the progress

rate or progress ratio, where a lower rate implies faster learning as
cumulative production increases. J.R. Crawford expanded upon Wright's
learning curve theory to develop a single unit cost model, which
estimates the cost of the nth unit produced given the following
information is known: (1) cost to produce the first unit; (2)
cumulative production of n units; and (3) the progress ratio.
    Consistent with Wright's learning curve, most technologies in the
CAFE Model use the basic approach by Wright, where we estimate
technology cost reductions by applying a fixed percentage to the
projected cumulative production of a given fuel economy technology in a
given MY.\141\ We estimate the cost to produce the first unit of any
given technology by identifying the DMC for a technology in a specific
MY. As discussed above and in detail below and in Chapter 3 of the
Draft TSD, our technology DMCs come from studies, teardown reports,
other publicly available data, and feedback from manufacturers and
suppliers. Because different studies or cost estimates are based on
costs in specific MYs, we identify the ``base'' MYs for each technology
where the learning factor is equal to 1.00. Then, we apply a progress
ratio to back-calculate the cost of the first unit produced. The
majority of technologies in the CAFE Model use a progress ratio (i.e.,
the slope of the learning curve, or the rate at which cost reductions
occur with respect to cumulative production) of approximately 0.89,
which is derived from average progress ratios researched in studies
funded and/or identified by NHTSA and EPA.\142\ Figure II-10 shows how
technologies on the MY 2022 Ravine Runner Type F decrease in cost over
several years. TURBOD and MR3 are technologies that have existed in
vehicles for some time, so they show a gradual sloping learning curve
implying that cost reductions from learning is moderate and eventually
becomes less steep toward MY2050. Conversely, newer technologies such
as, AT10L2, SS12V, and AERO5 show an initial steep learning curve where
cost reduction occurs at a high rate. Lastly, ROLL0 exhibits a mostly
flat curve implying that this level of rolling resistance technology is
very mature and does not incur much cost reduction, if at all, from
learning.
---------------------------------------------------------------------------

    \141\ We use statically projected cumulative volume production
estimates beause the CAFE Model does not support dynamic projections
of cumulative volume at this time.
    \142\ Simons, J.F. 2017. Cost and weight added by the Federal
Motor Vehicle Safety Standards for MY 1968-2012 passenger cars and
LTVs. Report No. DOT HS 812 354. NHTSA: Washington, DC 30-33;
Argote, L. et al. 1997. The Acquisition and Depreciation of
Knowledge in a Manufacturing Organization--Turnover and Plant
Productivity. Working Paper. Graduate School of Industrial
Administration, Carnegie Mellon University; Benkard, C.L. 2000.
Learning and Forgetting--The Dynamics of Aircraft Production. The
American Economic Review, Vol. 90(4): pp. 1034-54; Epple, D. et al.
1991. Organizational Learning Curves--A Method for Investigating
Intra-Plant Transfer of Knowledge Acquired through Learning by
Doing. Organization Science, Vol. 2(1): pp. 58-70; Epple, D. et al.
1996. An Empirical Investigation of the Microstructure of Knowledge
Acquisition and Transfer through Learning by Doing. Operations
Research, Vol. 44(1): pp. 77-86; Levitt, S. D. et al. 2013. Toward
an Understanding of Learning by Doing--Evidence from an Automobile
Assembly Plant. Journal of Political Economy, Vol. 121 (4): pp. 643-
81.
---------------------------------------------------------------------------

BILLING CODE 4910-59-P

[GRAPHIC] [TIFF OMITTED] TP17AU23.018

BILLING CODE 4910-59-C
    We assign groups of similar technologies or technologies of similar
complexity learning curves. While the grouped technologies differ in
operating characteristics and design, we chose to group them based on
market availability, complexity of technology integration, and
production volume of the technologies that can be implemented by
manufacturers and suppliers. In general, we consider most base and
basic engine and transmission technologies to be mature technologies
that will not experience any additional improvements in design or
manufacturing. Other basic engine technologies, like VVL, SGDI, and
DEAC, do decrease in costs through around MY 2036, because those were
introduced into the market more recently. All advanced engine
technologies follow the same general pattern of a gradual reduction in
costs until MY 2036, when they plateau and remain flat. We expect the
cost to decrease as production volumes increase, manufacturing
processes are improved, and economies of scale are achieved. We also
assigned advanced engine technologies that are based on a singular
preceding technology to the same learning curve as that preceding
technology. Similarly, the more advanced transmission technologies
experience a gradual reduction in costs through MY 2031, when they
plateau and remain flat. Lastly, we estimate that the learning curves
for road load technologies, with the exception of the most advanced MR
level (which decreases at a fairly steep rate through MY 2040, as
discussed further below and in Chapter 3.4 of the Draft TSD), will
decrease through MY 2036 and then remain flat.
    We use the same cost learning rates for both LD and HDPUV
technologies. This approach was used in the HDPUV analysis in the Phase
2 Heavy-Duty joint rule with EPA,\143\ and we believe that this is an
appropriate assumption to continue to use for this analysis. While the
powertrains in HDPUVs do have a higher power output than LD
powertrains, the designs and technology used will be very similar.
Although most HDPUV components will have higher operating loads and
provide different effectiveness values than LD components, the overall
designs are similar between the technologies. The individual technology
design and effectiveness differences between LD and HDPUV technologies
are discussed below and in Chapter 3 of the Draft TSD.
---------------------------------------------------------------------------

    \143\ See MDHD Phase 2 FRIA at 2-56, noting that gasoline
engines used in Class 2b and Class 3 pickup trucks and vans include
the engines offered in a manufacturer's light-duty truck
counterparts, as well as engines specific to the Class 2b and Class
3 segment, and describing that the the technology definitions are
based on those described in the LD analysis, but the effectiveness
values are different.
---------------------------------------------------------------------------

    For technologies that have been in production for many years, like
some engine and transmission technologies, this approach produces
reasonable estimates that we can compare against

other studies and publicly available data. Generating the learning
curve for battery packs for BEVs in future MYs is significantly more
complicated, and we discuss how we generated those learning curves in
Section II.D and in detail in Chapter 3.3 of the Draft TSD. Our battery
pack learning curves recognize that there are many factors that could
potentially lower battery pack costs over time outside of the cost
reductions due to improvements in manufacturing processes due to
knowledge gained through experience in production.
    Table II-8 shows how some of the technologies on the MY 2022 Ravine
Runner Type F decrease in cost over several years. Note that these
costs are specifically applicable to the MedSUVPerf class, and other
technology classes may have different costs for the same technologies.
These costs are pulled directly from the Technology Costs Input file,
meaning that they include the DMC, RPE, and learning.

                    Table II-8--Absolute Costs for Example Ravine Runner Type F Technologies
----------------------------------------------------------------------------------------------------------------
                     Technology (MedSUVPerf)                          CY 2022         CY 2027         CY 2032
----------------------------------------------------------------------------------------------------------------
TURBOD (8C2B)...................................................       $8,924.90       $8,877.31       $8,851.36
AT10L2..........................................................        2,848.19        2,806.64        2,790.92
SS12V...........................................................          215.47          191.01          180.28
AERO5...........................................................           55.30           50.91           48.70
----------------------------------------------------------------------------------------------------------------

5. Technology Incentives
    Similar to the regulations that we are proposing, other government
actions have the ability to influence the technology manufacturers
apply to their vehicles. For the purposes of this analysis, we
incorporate two other government actions into our analysis: state ZEV
requirements and Federal tax credits.
a. Simulating the Zero Emissions Vehicle Programs
    The California Air Resources Board (CARB) has developed various
programs to control emissions of criteria pollutants and GHGs from
vehicles sold in California. CARB does so in accordance with Federal
CAA; CAA section 209(a) generally preempts states from adopting
emission control standards for new motor vehicles,\144\ however,
Congress created an exemption program in CAA section 209(b) that allows
the State of California to seek a waiver of preemption related to
adopting or enforcing motor vehicle emissions standards.\145\ EPA must
grant the waiver unless the Agency makes one of three statutory
findings.\146\ Under CAA section 177, other States can adopt and
enforce standards identical to those approved under California's
Section 209(b) waiver.\147\ States that do so are sometimes referred to
as section 177 states, in reference to section 177 of the CAA Since
1990, CARB has included a Zero-Emission Vehicle (ZEV) program as part
of its package of standards that control smog-causing pollutants and
GHG emissions from passenger vehicles sold in California,\148\ and
several states have adopted those ZEV program requirements in
accordance with CAA section 177.
---------------------------------------------------------------------------

    \144\ 42 U.S.C. 7543(a).
    \145\ 42 U.S.C. 7543(b).
    \146\ See 87 FR 14332 (March 14, 2022). (``The CAA section
209(b) waiver is limited ``to any State which has adopted standards
. . . for the control of emissions from new motor vehicles or new
motor vehicle engines prior to March 30, 1966,'' and California is
the only State that had standards in place before that date.'').
NHTSA notes that EPA has not yet granted a waiver of preemption for
the ACC II program, and NHTSA does not prejudge EPA's
decisionmaking. Nonetheless, NHTSA believes it is reasonable, for
reasons discussed in detail below, to consider ZEV sales volumes
that manufacturers will produce in response to ACC II as part of our
consideration of actions that occur in the absence of fuel economy
standards.
    \147\ 42 U.S.C. 7507.
    \148\ CARB. Zero-Emission Vehicle Program. Available at: https://ww2.arb.ca.gov/our-work/programs/zero-emission-vehicle-program/about. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    There are currently three operative ZEV regulations: ACC I (LD ZEV
requirements through MY 2025),\149\ ACC II (LD ZEV requirements from
MYs 2026-2035),\150\ and Advanced Clean Trucks (ACT) (trucks in Classes
2b through 8, for MYs 2024-2035).\151\ We include the main provisions
of the ACC I, II, and ACT programs in the CAFE Model's analysis of
compliance pathways. We are confident that manufacturers will comply
with the ZEV programs because they have complied with state ZEV
programs in the past and they have made announcements of new ZEVs
demonstrating an intent to comply with the requirements going forward.
NHTSA models manufacturers' compliance with these programs because
accounting for technology improvements that manufacturers would make
even in the absence of CAFE standards allows NHTSA to gain a more
accurate understanding of the effects of the proposed rulemaking.
---------------------------------------------------------------------------

    \149\ 13 CCR 1962.2.
    \150\ 13 CCR 1962.4.
    \151\ CARB. Final Regulation Order: Advanced Clean Trucks
Regulation. Available at: https://ww2.arb.ca.gov/sites/default/files/barcu/regact/2019/act2019/fro2.pdf. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    This is the third analysis where we have modeled compliance with
the ACC program (and now the ACC II and ACT program) requirements in
the CAFE Model. While we have in the past received feedback agreeing or
disagreeing with the modeling inclusion of the ZEV programs at all, the
only past substantive comments on the ZEV program modeling methodology
have been requesting the inclusion of more states that have recently
signed on to adopt California's standards in our analysis. As noted
below, the inclusion or exclusion of states in the analysis depends on
which states have signed on to the programs at the time of our
analysis. While we are aware of legal challenges to some states'
adoption of the ZEV programs, it is beyond the scope of this rulemaking
to evaluate the likelihood of success of those challenges. For purposes
of our analysis, what is important is predicting, using a reasonable
assessment, how the fleet will evolve in the future. The following
discussion provides updates to our modeling methodology for the ZEV
programs in the analysis.
    The ACC I, II, and ACT programs require that increasing levels of
manufacturers' sales in California and section 177 states in each MY be
ZEVs, specifically BEVs, PHEVs, FCEVs.\152\ BEVs, PHEVs, and FCEVs each
contribute a different ``value'' towards a manufacturer's annual ZEV
requirement, which is a product of the manufacturer's production volume
sold in a ZEV state, multiplied by a ``percentage requirement.'' The
percentage requirements increase in

each year so that a greater portion of a manufacturer's fleet sold in
ZEV states in a particular MY must be ZEVs. For example, a manufacturer
selling 100,000 vehicles in California and 10,000 vehicles in
Connecticut (both states that have ZEV programs) in MY 2028 must ensure
that 51,000 of the California vehicles and 5,100 of the Connecticut
vehicles are ZEVs.
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    \152\ CARB. Final Regulation Order. Available at: https://ww2.arb.ca.gov/sites/default/files/barcu/regact/2022/accii/acciifro1962.2.pdf. (Accessed: May 31, 2023).
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    At the time of our analysis, sixteen states in addition to
California either formally signed on to the ACC II standards or were in
the process of adopting them.\153\ Although a few states are adopting
these requirements in future MYs, we include every state that
officially committed to adopting the requirements by the start of
December 2022 (regardless of MY start date),\154\ which was the time of
analysis, as being part of the unified ACC II states group for ease of
modeling. We consider all ACC II states together and do not model
specific states' years of joining, as states that have recently joined
the program have done so within a relatively short span of MYs and
represent only a very small percentage of new LDV sales.\155\
Similarly, nine states including California have formally adopted the
ACT standards at the time of analysis.\156\ As other states are
currently considering adopting ACT standards, we plan to update this
number in the final rule analysis if those states formally adopt it.
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    \153\ California, Colorado, Connecticut, Delaware, Maine,
Maryland, Massachusetts, Minnesota, Nevada, New York, New Jersey,
New Mexico, Oregon, Rhode Island, Vermont, Virginia, and Washington.
See California Air Resource Board. States that have Adopted
California's Vehicle Standards under Section 1777 of the Federal
Clean Air Act. Available at: https://ww2.arb.ca.gov/sites/default/files/2022-05/%C2%A7177_states_05132022_NADA_sales_r2_ac.pdf.
(Accessed: May 31, 2023).
    \154\ See States that have Adopted California's Vehicle
Standards under Section 177 of the Federal Clean Air Act, May 13,
2022, https://ww2.arb.ca.gov/sites/default/files/2022-05/%C2%A7177_states_05132022_NADA_sales_r2_ac.pdf; https://governor.nc.gov/eo-faq/open. We consider these to be states that
have passed laws or have progressed sufficiently in the process of
adopting requirements. States indicating interest or that still need
to vote on adopting these provisions are not counted in this group.
    \155\ Id.
    \156\ California, Connecticut, Massachusetts, New Jersey, New
York, North Carolina, Oregon, Vermont and Washington. We include
Connecticut as their House passed the legislation instructing their
Department of Energy and Environmental Protection to adopt ACT. See
https://www.electrictrucksnow.com/states; https://vermontbiz.com/news/2022/november/24/vermont-adopts-rules-cleaner-cars-and-trucks;
https://deq.nc.gov/about/divisions/air-quality/motor-vehicles-and-air-quality/advanced-clean-trucks; https://www.cga.ct.gov/2022/fc/pdf/2022HB-05039-R000465-FC.pdf.
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    It is important to note that not all section 177 states have
adopted the ACC II or ACT program components.\157\ Furthermore, more
states have formally adopted the ACC II program than the ACT program,
so the discussion in the following sections will call states that have
opted in ``ACC II states'' or ``ACT states.'' Separately, many states
signed a memorandum of understanding (MOU) in 2020 to indicate their
intent to work collaboratively towards a goal of turning 100% of MD and
HD vehicles into ZEVs in the future.\158\ For the purposes of CAFE
analysis, we include only those states that have formally adopted the
ACT in our modeling as ``ACT states''. States that have signed the MOU
but not formally adopted the ACT program are referred to as ``MOU
states'' and are not included in CAFE modeling. When the term ``ZEV
programs'' is used hereafter, it refers to both the ACC II and ACT
programs.
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    \157\ At the time of writing, Pennsylvania has adopted the Low-
emission Vehicle standards, but not the ZEV (now ACC II) portion.
See Pennsylvania Department of Environmental Protection. Clean
Vehicle Program. Available at: https://www.dep.pa.gov/Business/Air/BAQ/Automobiles/Pages/CleanVehicleProgram.aspx. (Accessed: May 31,
2023).
    \158\ Northeast States for Coordinated Air Use Management
(NESCAUM). Multi-State Medium and Heavy-Duty Zero Emission Vehicle
Memorandum of Understanding. July 13, 2020. Available at: https://www.nescaum.org/documents/mhdv-zev-mou-20220329.pdf/. (Accessed: May
31, 2023).
---------------------------------------------------------------------------

    Incorporating these programs into the model includes converting
vehicles that have been identified as potential ZEV candidates into
BEVs at the vehicle's ZEV application year so that a manufacturer's
fleet meets its required ZEV credit requirements. We focused on BEVs as
ZEV conversions, rather than PHEVs or FCEVs, because, as for 2026-2035,
manufacturers cannot earn more than 20% of their ZEV credits through
PHEV sales. Similarly, PHEVs receive a smaller number of credits than
BEVs and FCEVs since their powertrain still incorporates use of an ICE.
We determined that including PHEVs in the ZEV modeling would have
introduced unnecessary complication to the modeling and would have
provided manufacturers little benefit in the modeled program. In
addition, although FCEVs can earn the same number of credits as BEVs,
we chose to focus on BEV technology pathways since FCEVs are generally
less cost-effective than BEVs and most manufacturers have not been
producing them at high volumes.
    Total credits are calculated by multiplying the credit value each
ZEV receives by the vehicle's volume. In the ACC II program, from 2026
onwards, each full ZEV earns one credit value per vehicle, while
partial ZEVs (PHEVs) earn credits based on their AER. In the context of
this section, ``full ZEVs'' refers to BEVs and FCEVs, as a PHEV
generally receives a smaller number of credits than other ZEVs, as
discussed above. Credit targets in the ACT program (referred to as
deficits) are calculated by multiplying sales by percentage requirement
and weight class multiplier. Each HDPUV full ZEV in the 2b/3 class
earns 0.8 credits and each NZEV (called PHEVs in the CAFE Model) earns
0.75 credits.\159\
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    \159\ CARB. Final Regulation Order: Advanced Clean Trucks
Regulation. Available at: https://www.cga.ct.gov/2022/fc/pdf/2022HB-05039-R000465-FC.pdf. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    The CAFE Model is designed to present outcomes at a national scale,
so the ZEV programs analysis considers the states as a group as opposed
to estimating each state's ZEV credit requirements individually. To
capture the appropriate volumes subject to the ACC II and ACT
requirements, we calculated each manufacturer's total market share in
ACC II or ACT states. We used Polk's National Vehicle Population
Profile (NVPP) from January 2022 to calculate these percentages.\160\
These data include vehicle characteristics such as powertrain, fuel
type, manufacturer, nameplate, and trim level, as well as the state in
which each vehicle is sold. At the time of the data snapshot, MY 2021
data from the NVPP contained the most current estimate of new vehicle
market shares for most manufacturers, and best represented the
registered vehicle population on January 1, 2022. We assumed that new
registrations data best approximate new sales given the data options.
For MY 2021 vehicles in the latest NVPP, the ACC II State group makes
up approximately 38% of the total LD sales in the United States. The
ACT state groups comprise approximately 19% of the new Class 2b and 3
vehicle market in the U.S.\161\ We based the volumes used for the ZEV
credit target calculation on each manufacturer's future assumed market
share in ACC II and ACT states. We made this assumption after examining
three past years of market share data and determining that the
geographic

distribution of manufacturers' market shares remained fairly constant.
We welcome comment on the assumptions described in this paragraph.
---------------------------------------------------------------------------

    \160\ National Vehicle Population Profile (NVPP). 2022. Includes
content supplied by IHS Markit. Copyright R.L. Polk & Co., 2022. All
rights reserved. Available at: https://repository.duke.edu/catalog/caad9781-5438-4d65-b908-bf7d97a80b3a. (Accessed: May 31, 2023).
    \161\ We consulted with Polk and determined that their NVPP data
set that included vehicles in the 2b/3 weight class provided the
most fulsome dataset at the time of analysis, recognizing that the
2b/3 weight class includes both 2b/3 HD pickups and vans and other
classes within 2b/3 segment. While we determined that this dataset
was the best option for the analysis, it does not contain all Class
3 pickups and vans sold in the United States.
---------------------------------------------------------------------------

    We calculated total credits required for ACC II and ACT compliance
by multiplying the percentages from each program's ZEV requirement
schedule by the ACC II or ACT state volumes.\162\ For the first set of
ACC requirements covering 2022 (the first modeled year in our analysis)
through 2025, the percentage requirements start at 14.5% and ramp up in
increments to 22 percent by 2025.\163\ For ACC II, the percentage
requirements start at 35% in MY 2026 and ramp up to 100% in MY 2035 and
subsequent years.\164\ For ACT Class 2b-3 Group vehicles (equivalent to
HDPUVs in our analysis), the percentage requirements start at 5% in MY
2024 and increase to 55% in MYs 2035 and beyond.\165\ We then multiply
the resulting national sales volume predictions by manufacturer by each
manufacturer's total market share in the ACC II or ACT states to
capture the appropriate volumes in the ZEV credits calculation.
Required credits by manufacturer, per year, are determined within the
CAFE Model by multiplying the ACC II state volumes by CARB's ZEV credit
percentage requirement for each program respectively.
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    \162\ Note that the ACT credit target calculation differs
slightly from the ACC II calculation because it includes a vehicle
class-specific weight modifier.
    \163\ 13 CCR 1962.2(b).
    \164\ 13 CCR 1962.4(c)(1)(B).
    \165\ 13 CCR 1963.1(b).
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    To ensure that the ACC II and ACT credit requirements are met in
the baseline in each modeling scenario, we add ZEV candidate vehicles
to the baseline. We flag ZEV candidates in the `vehicles' worksheet in
the Market Data Input File, which is described above and in detail in
Draft TSD Chapter 2.2. Although we identify the ZEV candidates in the
Market Data Input File, the actual conversion from non-ZEV to ZEV
vehicles occurs within the CAFE Model. The CAFE Model converts a
vehicle to a ZEV during the specified ZEV application year.
    We flag ZEV candidates in two ways: using reference vehicles with
ICE powertrains or using PHEVs already in the existing fleet. When
using ICE powertrains as reference vehicles, we create a duplicate row
(which we refer to as the ZEV candidate row) in the Market Data Input
File's Vehicles tab for the ZEV version of the original vehicle,
designated with a unique vehicle code. The ZEV candidate row specifies
the relevant electrification technology level of the ZEV candidate
vehicle (e.g., BEV1, BEV2, and so on), the year that the
electrification technology is applied,\166\ and zeroes out the
candidate vehicle's sales volume. We identify all ICE vehicles with
varying levels of technology up to and including strong hybrid electric
vehicles (SHEVs) with rows that have 100 sales or more as ZEV
candidates. The CAFE Model moves the sales volume from the reference
vehicle row to the ZEV candidate row on an as-needed basis, considering
the MY's ZEV credit requirements. When using existing PHEVs within the
fleet as a starting point for identifying ZEV candidates, we base our
determination of ZEV application years for each model based on
expectations of manufacturers' future EV offerings. The entire sales
volume for that PHEV model row is converted to BEV on the application
year. This approach allows for only the needed additional sales volumes
to flip to ZEVs, based on the ACC II and ACT targets, and keeps us from
overestimating ZEVs in future years.
---------------------------------------------------------------------------

    \166\ The model turns all ZEV candidates into BEVs in 2023, so
sales volumes can be shifted from the reference vehicle row to the
ZEV candidate row as necessary.
---------------------------------------------------------------------------

    We identify LD ZEV candidates by duplicating every row with 100 or
more sales that is not a PHEV, BEV, or FCEV. We refer to the original
rows as `reference vehicles.' Although PHEVs are all ZEV candidates, we
do not duplicate those rows as we focus the CAFE Model's simulation of
the ACC II and ACT programs on BEVs. However, any PHEVs already in the
analysis fleet or made by the model will still receive the appropriate
ZEV credits. While flagging the ZEV candidates, we identified each one
as a BEV1, BEV2, BEV3, and BEV4 (BEV technology types based on range),
based partly on their price, market segment, and vehicle features. For
instance, we assumed luxury cars would have longer ranges than economy
cars. We also assigned AWD/4WD variants of vehicles shorter BEV ranges
when appropriate. See Draft TSD Chapter 3.3 for more detailed
information on electrification options for this analysis. The CAFE
Model assigns credit values per vehicle depending on whether the
vehicle is a ZEV in a MY prior to 2026 or after, due to the change in
value after the update of the standards from ACC II.
    We follow a similar process in assigning HDPUV ZEV candidates as in
assigning LD ZEV candidates. We duplicate every van row with 100 or
more sales and duplicate every pickup truck row with 100 or more sales
provided the vehicle model has a WF less than 7,500 and a diesel- or
gasoline-based range lower than 500 miles based on their rated fuel
economy and fuel tank size. This is consistent with our treatment of
HDPUVs in the CAFE technology pathways, which is discussed below in
Section II.D and in Draft TSD Chapter 3. Note that the model can still
apply PHEV technology to HDPUVs. When identifying ZEV candidates, we
assign each candidate as either a BEV1 or a BEV2 based on their price,
market segment, and other vehicle attributes.
    The CAFE Model brings manufacturers into compliance with ACC II and
ACT first in the baseline, solving for the technology compliance
pathway used to meet increasing ZEV standards.
    We did not include two provisions of the ZEV regulations in our
modeling. First, while the ACC II Program includes compliance options
for providing reduced-price ZEVs to community mobility programs and for
selling used ZEVs (known as ``environmental justice vehicle values''),
these are focused on a more local level than we could reasonably
represent in the CAFE Model. The data for this part of the program are
also not available from real world application. Second, CARB allows for
some banking of ZEV credits and credit pooling.\167\ We did not assume
compliance with ZEV requirements through banking of credits when
simulating the program in the CAFE Model and focus instead on
simulating manufacturer's compliance fully through the production of
new ZEVs. In past rules, we assumed 80% compliance through vehicle
requirements and the remaining 20% with banked credits.\168\ Due to the
complicated nature of accounting for the entire credit program, and
after conversations with CARB, we have decided not to incorporate
banked credits into the ZEV modeling at this time. Based on guidance
from CARB and assessment of CARB's responses to manufacturer comments,
we expect impacts of banked credit provisions on overall volumes to be
small.\169\
---------------------------------------------------------------------------

    \167\ CARB. Final Regulation Order: Section 1962.4, Title 13,
California Code of Regulations. Available at: https://ww2.arb.ca.gov/sites/default/files/barcu/regact/2022/accii/acciifro1962.2.pdf. (Acessed: May 31, 2023).
    \168\ CAFE TSD 2024-2026. Pg. 129.
    \169\ CARB. Final Statement of Resons for Rulemaking, Including
Summary of Comments and Agency Response. Appendix C: Summary of
Comments to ZEV Regulation and Agency Response. Available at:
https://ww2.arb.ca.gov/sites/default/files/barcu/regact/2022/accii/fsorappc.pdf. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    Draft TSD Chapter 2.3 includes more information about the process
we use to

simulate ACC II and ACT program compliance in this analysis.
b. IRA Tax Credits
    The IRA included several new and expanded tax credits intended to
encourage the adoption of clean vehicles.\170\ NHTSA models two of the
IRA provisions in this analysis. The first is the Advanced
Manufacturing Production Tax Credit (AMPC). This provision provides a
$35 per kWh tax credit for manufacturers of battery cells and an
additional $10 per kWh for manufacturers of battery modules (all
applicable to manufacture in the United States).\171\ The second
provision modeled is the Clean Vehicle Tax Credit (CVC),\172\ which
provides up to $7,500 toward the purchase of clean vehicles with
critical minerals and battery components manufactured in North
America.\173\ The credits are currently in effect and are scheduled to
sunset by 2032.\174\ Since the CAFE Model forecasts by model years, and
MYs typically are released in the preceding CYs, NHTSA applies the
credits to MYs 2024-2033 in the analysis for both LDVs and HDPUVs.
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    \170\ Public Law No: 117-169.
    \171\ 26 U.S.C. 45X. If a manufacturer produces a battery module
without battery cells, they are eligible to claim up to $45 per kWh
for the battery module. Two other provisions of the AMPC are not
modeled at this time; (i) a credit equal to 10 percent of the
manufacturing cost of electrode active materials, (ii) a credit
equal to 10 percent of the manufacturing cost of critical minerals
for battery production. We are not modeling these credits directly
because of how we estimate battery costs and to avoid the potential
to double count the tax credits if they are included into other
analyses that feed into our inputs.
    \172\ 26 U.S.C 30D.
    \173\ There are vehicle price and consumer income limitations on
the CVC, as well. See Congressional Research Service. 2022. Tax
Provisions in the Inflation Reduction Act of 2022 (H.R. 5376).
Available at: https://crsreports.congress.gov/product/pdf/R/R47202/6. (Accessed: May 31, 2023).
    \174\ The AMPC has a phase-out beginning in CY 2030.
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    Interactions between producers and consumers in the marketplace
tend to ensure that subsidies like the AMPC and the CVC, regardless of
whether they are initially paid to producers or consumers, are
ultimately shared between the two groups. For this analysis, the agency
assumes that manufacturers and consumers will each capture half of the
dollar value of the AMPC and CVC. The agency assumes that
manufacturers' shares of both credits will offset part of the cost to
supply models that are eligible for the credits--PHEVs, BEVs, and FCVs.
The subsidies reduce the costs of eligible vehicles and increase their
attractiveness to buyers (however, in the LD fleet, the tax credits do
not alter the penetration rate of BEVs in the regulatory
alternatives).\175\ Because the AMPC credit scales with battery
capacity, NHTSA staff determined average battery energy capacity by
powertrain (e.g., PHEV, BEV, FCV) for passenger cars, light trucks, and
HDPUVs based on ANL simulation outputs. For a more detailed discussion
of these assumptions, see Draft TSD Chapter 2.3.2.
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    \175\ In Table 9-4 of the PRIA, both the reference case (labeled
``RC'') and the no tax credit case (``No EV tax credits'') show a
32.3% penetration rate for BEVs in the baseline and preferred
alternative.
---------------------------------------------------------------------------

    The CAFE Model's approach to analyzing the effects of the CVC
includes another restriction. The CAFE Model accounts for the MSRP
restrictions of the CVC by assuming that it cannot be applied to cars
with an MSRP above $55,000 or other vehicles with an MSRP above
$80,000, since these are ineligible for the incentive. NHTSA recognizes
that manufacturers may be unable to comply immediately with the CVC's
domestic component and critical mineral sourcing requirements, and that
domestic production may ramp-up over the coming years. To reflect this
ramp-up, the model phases-in the tax credit. See Chapter 2.5.2 of the
Draft TSD for details.
    NHTSA is unable to explicitly represent all of the requirements of
the CVC. For example, NHTSA cannot capture the income restrictions of
the CVC in its analysis because the CAFE Model does not account for
purchasers' income. We do not have reliable data on the income levels
of consumers purchasing specific models. However, the agency's
procedure for modeling MSRP restrictions partially captures the CVC
income thresholds indirectly, insofar as high-income buyers are more
likely to purchase luxury vehicles that exceed the CVC's MSRP caps.
    Nor does NHTSA's analysis explicitly represent the tax credits'
accompanying restrictions on the location of final assembly and battery
production or the origin of critical minerals. While it is unlikely
that all PHEVs, BEVs and FCEVs sold in the United States at any point
will meet both the critical mineral and battery component requirements,
we do not have a reliable method or source to estimate where production
is likely to occur during future MYs, particularly as manufacturers
respond to the provisions of the IRA.\176\ Instead, we make the
simplifying assumption for modeling purposes that all PHEVs, BEVs, and
FCEVs produced and sold during the time frame that tax credits are
offered will be eligible for those credits subject to the MSRP
restrictions discussed above.
---------------------------------------------------------------------------

    \176\ Note that the labor component of this analysis makes
certain assumptions about the location of vehicle production.
However, we do not make assumptions about how our standards will
alter the origination of components and vehicles. Instead, we assume
the porportion of hours spent in the United States to produce a
component or assemble a vehicle remains constant, but the quantity
of components and vehicles assembled will alter.
---------------------------------------------------------------------------

    To account for these limitations, we assume that the average credit
value for the CVC across all PHEV, BEV, and FCEV sales in a given year
will never reach its full $7,500 value for all vehicles, and instead
assume a maximum average credit value of $5,000. We believe this
assumption is also supported by the fact that some manufacturers may
have optimized their supply chains and relocating component production
to the United States could increase their costs of production, the
price to the consumer, or both; and the CVC is a non-refundable tax
credit, which means if the credit is claimed by the consumer, their tax
liability must be at least $7,500 for the credit to reach its full
value.
    We seek comment on our methodology for modeling the CVC and AMPC.
The agency has also included several sensitivity cases testing
different passthrough amounts and maximum credit values. If commenters
believe the agency should be modeling additional components of either
of the tax credits, the agency requests commenters identify both
potential data sources and methodologies.
    There are several other provisions of the IRA related to clean
vehicles that are excluded from the analysis. The Previously-owned
Clean Vehicle credit provides a tax credit for the first resale of a
clean vehicle by a qualified dealership.\177\ The agency excluded this
tax credit because we do not track resale prices in the model, nor do
we have a method of distinguishing between dealership and person-to-
person sales. Furthermore, this credit is only relevant to our analysis
to the extent it may reduce scrappage rates of eligible vehicles, which
is outside the capabilities of the model to forecast at this time.
---------------------------------------------------------------------------

    \177\ 26 U.S.C. 25E.
---------------------------------------------------------------------------

    The Commercial Clean Vehicle credit (Commercial Credit) provides
commercial entities an alternative to the CVC.\178\ The value of the
Commercial Credit for vehicles covered by this proposal is the cost
differential between a qualified vehicle and a comparable non-qualified
vehicle but is capped at $7,500. The Commercial Credit has

none of the origination and MSRP requirements of the CVC. At the time
NHTSA was developing its approach to modeling the IRA tax credits and
coordinating with EPA, the Treasury Department had yet to release its
guidance on the Commercial Credit and NHTSA was uncertain if vehicles
leased to consumers would qualify for the credit or how the incremental
value of commercial clean vehicles would be calculated. As such, NHTSA
felt that if leased vehicles were ineligible for the Commercial Credit
or that the incremental approach could lead to a significant amount of
vehicles receiving less than the maximum credit, that the value of the
Commercial Credit would be subsumed by our approach to modeling the CVC
given we allow all vehicles to qualify for the CVC.
---------------------------------------------------------------------------

    \178\ 26 U.S.C. 45W.
---------------------------------------------------------------------------

    Since then, the Treasury Department has clarified that leased
vehicles qualify for the Commercial Credit and that the credit will be
calculated based off of the DOE's Incremental Purchase Cost Methodology
and Results for Clean Vehicles report for at least CY 2023 rather than
having the taxpayer estimate the actual cost differential.\179\ To the
extent that our modeling of the CVC misses vehicles that may qualify
for a higher credit through the Commercial Credit, our decision to not
model the Commercial Credit may understate the impacts of the IRA.
---------------------------------------------------------------------------

    \179\ See responses to Q2-Q4 of Internal Revenue Service Fact
Sheet Topic G-Frequently Asked Questions About Qualified Commercial
Clean Vehicles Credits. Avaliable at: https://www.irs.gov/newsroom/topic-g-frequently-asked-questions-about-qualified-commercial-clean-vehicles-credit. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    Given these updates, EPA modified their approach to modeling the
IRA tax credits prior to finalizing their Multi-Pollutant Emissions
Standards for Model Years 2027 and Later Light-Duty and Medium-Duty
Vehicles proposal.\180\ EPA elected to model the CVC and Commercial
Credit jointly, which resulted in a quicker phase-in schedule with a
higher maximum average credit value than that used in NHTSA's analysis.
---------------------------------------------------------------------------

    \180\ See U.S. EPA. Multi-Pollutant Emissions Standards for
Model Years 2027 and Later Light-Duty and Medium-Duty Vehicles Draft
Regulatory Impact Analysis., EPA-420-D-23-003 (April 2023), Chapter
2.6.8 and <IP_ADDRESS>.4. Federal Register, Vol. 88, No. 87, Friday, May
5, 2023.
---------------------------------------------------------------------------

    NHTSA is considering incorporating EPA's revised approach for
modeling the CVC and Commercial Credit jointly for the final rule to
account for the guidance issued by the Treasury Department. Under this
approach, NHTSA could retain the same basic mechanisms employed to
model the CVC but would modify the phase-in and maximum average credit
to account for the possibility that the Commercial Credit is available
and offers a higher tax benefit than the CVC. NHTSA seeks comment on
whether it should adopt this approach, and, if so, specifically
requests commenters help identify what would be an appropriate maximum
average credit, phase-in schedule, and elasticity share between
producers and consumers for this approach. EPA and NHTSA will continue
to monitor developments with the IRA tax credits and consult with each
other on how best to implement the credits for the analyses supporting
their respective final rules.
    Finally, the Qualifying Advanced Energy Project credit (48C)
provides manufacturers an amount equal to 30 percent of the qualified
investment, including building or retooling plants for BEVs, PHEVs, or
FCEVs.\181\ The agency excluded this tax credit for several reasons.
The credit requires Treasury's pre-approval and the total amount of
credits awarded under this provision may not exceed $10 billion.\182\
Furthermore, the AMPC cannot be claimed for any battery cell or module
produced from a project that claimed a Qualifying Advanced Energy
Project credit. For the sake of simplicity, we assume that
manufacturers will chose the AMPC over the Qualified Advanced Energy
Project credit. We also do not model other Federal programs that
incentivize the production or purchase of clean vehicles and their
infrastructure, such as the IRA Sec.  50142 Advanced Technology Vehicle
Manufacturing Loan Program, IRA Sec.  50143 Domestic Manufacturing
Conversion Grants, IRA Sec.  70002 USPS Clean Fleets, or IRA Sec.
13404 Alternative Fuel Vehicle Refueling Property Credit. These credits
and grants incentivize clean vehicles through avenues the CAFE Model is
currently unable to consider as they typically affect a smaller subset
of the vehicle market and may influence purchasing decisions through
means other than price, e.g., through expanded charging networks.
---------------------------------------------------------------------------

    \181\ 26 U.S.C. 48C.
    \182\ Public Law 117-169, section 13502.
---------------------------------------------------------------------------

    We do not model individual state tax credit or rebate programs.
Unlike ZEV requirements which are uniform across states that adopt
them, state clean vehicle tax credits and rebates vary from
jurisdiction to jurisdiction and are subject to more uncertainty than
their Federal counterparts.\183\ Tracking sales by jurisdiction and
modeling each program's individual compliance program would require
significant revisions to the CAFE Model and likely provide minimal
changes in the net outputs of the analysis.
---------------------------------------------------------------------------

    \183\ States have additional mechanisms to amend or remove tax
incentives or rebates. Sometimes, even after these programs are
enacted, uncertainty persists, see e.g. Farah, N. 2023. The Untimely
Death of America's `Most Equitable' EV Rebate. Last Revised: 01/30/
2023. Available at: https://www.eenews.net/articles/the-untimely-death-of-americas-most-equitable-ev-rebate/. (Accessed: May 31,
2023)
---------------------------------------------------------------------------

    We seek comment on our decision to exclude these credits. Excluding
these credits may overstate the projected cost to consumers of certain
vehicles. If commenters feel that we should include any of these
credits in the final rule, the agency requests commenters address the
limitations noted above and provide data sources to assist with
modeling the credit.
6. Technology Applicability Equations and Rules
    How does the CAFE Model decide how to apply technology to the
baseline fleet of vehicles? We described above that the CAFE Model
projects cost-effective ways that vehicle manufacturers could comply
with CAFE standards, subject to limits that ensure that the model
reasonably replicates manufacturer's decisions in the real-world. This
section describes the equations the CAFE Model uses to determine how to
apply technology to vehicles, including whether technologies are cost-
effective, and why we believe the CAFE Model's calculation of potential
compliance pathways reasonably represents manufacturers' decision-
making. This section also gives a high-level overview of real-world
limitations that vehicle manufacturers face when designing and
manufacturing vehicles, and how we include those in the technology
inputs and assumptions in the analysis.
    The CAFE Model begins by looking at a manufacturer's fleet in a
given MY and determining whether the fleet meets its CAFE standard. If
the fleet does not meet its standard, the model begins the process of
applying technology to vehicles. We described above how vehicle
manufacturers use the same or similar engines, transmissions, and
platforms across multiple vehicle models, and we track vehicle models
that share technology by assigning Engine, Transmission, and Platform
Codes to vehicles in the analysis fleet. As an example, the Ford 10R80
10-speed transmission is currently used in the following Ford Motor
Company vehicles: 2017-present Ford F-150, 2018-present Ford Mustang,
2018-present Ford Expedition/Lincoln Navigator, 2019-present Ford
Ranger,

2020-present Ford Explorer/Lincoln Aviator, and the 2020-present Ford
Transit.\184\ The CAFE Model first determines whether any technology
should be ``inherited'' from an engine, transmission, or platform that
currently uses the technology to a vehicle that is due for a refresh or
redesign. Using the Ford 10R80 10-speed transmission analysis as
applied to the CAFE Model, the above models would be linked using the
same Transmission Code. Even though the vehicles might be eligible for
technology applications in different years because each vehicle model
is on a different refresh or redesign cycle, each vehicle could
potentially inherit the 10R80 10-speed transmission. The model then
again evaluates whether the manufacturer's fleet complies with its CAFE
standard. If it does not, the model begins the process of evaluating
what from our universe of technologies could be applied to the
manufacturer's vehicles.
---------------------------------------------------------------------------

    \184\ DOE. 2013. Light-Duty Vehicles Technical Requirements and
Gaps for Lightweight and Propulsion Materials. Final Report.
Available at: https://www.energy.gov/eere/vehicles/articles/workshop-reportlight-duty-vehicles-technical-requirements-and-gaps.
(Accessed: May 31, 2023).
---------------------------------------------------------------------------

    The CAFE Model applies the most cost-effective technology out of
all technology options that could potentially be applied. To determine
whether a particular technology is cost-effective, the model will
calculate the ``effective cost'' of multiple technology options and
choose the option that results in the lowest ``effective cost.'' The
``effective cost'' calculation is actually multiple calculations, but
we only describe the highest levels of that logic here; interested
readers can consult the CAFE Model Documentation for additional
information on the calculation of effective cost. Equation II-6 shows
the CAFE Model's effective cost calculation for this analysis.
[GRAPHIC] [TIFF OMITTED] TP17AU23.019

Where:

TechCostTotal: the total cost of a candidate technology evaluated on
a group of selected vehicles;
TaxCreditsTotal: the cumulative value of additional vehicle and
battery tax credits (or, Federal Incentives) resulting from
application of a candidate technology evaluated on a group of
selected vehicles;
FuelSavingsTotal: the value of the reduction in fuel consumption
(or, fuel savings) resulting from application of a candidate
technology evaluated on a group of selected vehicles;
[Delta]Fines: the change in manufacturer's fines in the analysis
year if the CAFE compliance program is being evaluated, or zero if
evaluating compliance with CO2 standards;
[Delta]ComplianceCredits: the change in manufacturer's compliance
credits in the analysis year, which depending on the compliance
program being evaluated, corresponds to the change in CAFE credits
(denominated in thousands of gallons) or the change in
CO2 credits (denominated in metric tons); and
EffCost: the calculated effective cost attributed to application of
a candidate technology evaluated on a group of selected vehicles.

    For the effective cost calculation, the CAFE Model considers the
total cost of a technology that could be applied to a group of
connected vehicles, just as a vehicle manufacturer might consider what
new technologies it has that are ready for the market, and which
vehicles should and could receive the upgrade. Next, like the
technology costs, the CAFE Model calculates the total value of Federal
incentives (for this analysis, Federal tax credits) available for a
technology that could be applied to a group of vehicles and subtracts
that total incentive from the total technology costs. For example, even
though we do not consider the fuel economy of LD BEVs in our standard-
setting analysis, we do account for the costs of vehicles that
manufacturers may build in response to California's ACC I and ACC II
program (and in the HDPUV analysis, the ACT program) as part of our
evaluation of how the world would look without our regulation, or more
simply, the regulatory baseline. If the CAFE Model is evaluating
whether to build a BEV outside of the MYs for which NHTSA is setting
standards (if the applicable in the modeling scenario), it starts with
the total technology cost for a group of BEVs and subtracts the total
value of the tax credits that could be applied to that group of
vehicles.
    The total fuel savings calculation is slightly more complicated.
Broadly, when considering total fuel savings from switching from one
technology to another, the CAFE Model must calculate the total fuel
cost for the vehicle before application of a technology and subtract
the total fuel cost for the vehicle after calculation of that
technology. The total fuel cost for a given vehicle depends on both the
price of gas (or gasoline equivalent fuel) and the number of miles that
a vehicle is driven, among other factors. As technology is applied to
vehicles in groups, the total fuel cost is then multiplied by the sales
volume of a vehicle in a MY to equal total fuel savings. This equation
also includes an assumption that consumers are likely to buy vehicles
with fuel economy improving technology that pays for itself within 2.5
years, or 30 months. Finally, in the numerator, we subtract the change
in a manufacturer's expected fines before and after application of a
specific technology. Then, the result from the sequence above is
divided by the change in compliance credits, which means a
manufacturer's credits earned (expressed as thousands of gallons for
the purposes of effective cost calculation) in a compliance category
before and after the application of a technology to a group of
vehicles.
    The effective cost calculation has evolved over successive CAFE
Model iterations to become increasingly more complex; however,
manufacturers' decision-making regarding what fuel economy improving
technology to add to vehicles has also become increasingly more
complex. We believe this calculation appropriately captures a number of
manufacturers implicit or explicit considerations.
    The model accounts explicitly for each MY, applying technologies
when vehicles are scheduled to be redesigned or freshened and carrying
forward technologies between MYs once they are applied. The CAFE Model
accounts explicitly for each MY because manufacturers actually ``carry
forward'' most technologies between MYs, tending to concentrate the
application of new technology to vehicle redesigns or mid-cycle
``freshenings,'' and design cycles vary widely among manufacturers and
specific products. Comments by manufacturers and model peer reviewers
strongly support explicit year-by-year simulation. The multi-year
planning capability, simulation of ``market-driven overcompliance,''
and

EPCA credit mechanisms increase the model's ability to simulate
manufacturers' real-world behavior, accounting for the fact that
manufacturers will seek out compliance paths for several MYs at a time,
while accommodating the year-by-year requirement. This same multi-year
planning structure is used to simulate responses to standards defined
in grams CO2/mile and utilizing the set of specific credit
provisions defined under EPA's program.
    In addition to the model's technology application decisions
pursuant to the compliance simulation algorithm, there are also several
technology inputs and assumptions that work together to determine which
technologies the CAFE Model can apply. The technology pathways,
discussed in detail above, are one significant way that we instruct the
CAFE Model to apply technology. Again, the pathways define technologies
that are mutually exclusive (i.e., that cannot be applied at the same
time), and define the direction in which vehicles can advance as the
modeling system evaluates specific technologies for application. Then,
the arrows between technologies instruct the model on the order in
which to evaluate technologies on a pathway, to ensure that a vehicle
that uses a more fuel-efficient technology cannot downgrade to a less
efficient option.
    In addition to technology pathway logic, we have several technology
applicability rules that we use to better replicate manufacturers'
decision-making. The ``skip'' input--represented in the Market Data
Input File as ``SKIP'' in the appropriate technology column
corresponding to a specific vehicle model--is particularly important
for accurately representing how a manufacturer applies technologies to
their vehicles in the real world. This tells the model not to apply a
specific technology to a specific vehicle model. SKIP inputs are used
to simulate manufacturer decisions with cost-benefit in mind, including
(1) parts and process sharing; (2) stranded capital; and (3)
performance neutrality.
    First, parts sharing includes the concepts of platform, engine, and
transmission sharing, which are discussed in detail in Section II.C.2
and Section II.C.3, above. A ``platform'' refers to engineered
underpinnings shared on several differentiated vehicle models and
configurations. Manufacturers share and standardize components,
systems, tooling, and assembly processes within their products (and
occasionally with the products of another manufacturer) to manage
complexity and costs for development, manufacturing, and assembly.
Detailed discussion for this type of SKIP is provided in the ``adoption
features'' section for different technologies, if applicable, in
Chapter 3 of the Draft TSD.
    Similar to vehicle platforms, manufacturers create engines that
share parts. 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. 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
identified as such, and we also may apply a SKIP to a particular engine
technology where we know that a manufacturer shares an engine
throughout several of their vehicle models, and the engine technology
is not appropriate for any of the platforms that share the same engine.
    It is important to note that manufacturers define common engines
differently. Some manufacturers consider engines as ``common'' if the
engines share 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. For example, a manufacturer may have listed two
engines separately 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. We consider engines together (for purposes of coding,
discussed in Section II.C.2 above, and for SKIP application) if the
engines share a common cylinder count and configuration, displacement,
valvetrain, and fuel type, or if the engines only differed slightly in
compression ratio (CR), horsepower, and displacement.
    Parts sharing also includes the concept of sharing manufacturing
lines (the systems, tooling, and assembly processes discussed above),
since manufacturers are unlikely to build a new manufacturing line to
build a completely new engine. A new engine that is designed to be mass
manufactured on an existing production line will have limits in number
of parts used, type of parts used, weight, and packaging size due to
the weight limits of the pallets, material handling interaction points,
and conveyance line design to produce one unit of a product. The
restrictions will be reflected in the usage of a SKIP of engine
technology that the manufacturing line would not accommodate.
    SKIPs also relate to instances of stranded capital when
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 accounts for such lost investments. One design where
manufacturers take an iterative redesign approach, as described in a
recent SAE paper,\185\ is the MacPherson strut suspension. It is a
popular low-cost suspension design and manufacturers use it across
their fleet.
---------------------------------------------------------------------------

    \185\ Pilla, S. et al. 2021. Parametric Design Study of
McPherson Strut to Stabilizer Bar Link Bracket Weld Fatigue Using
Design for Six Sigma and Taguchi Approach. SAE Technical Paper 2021-
01-0235. Available at: https://doi.org/10.4271/2021-01-0235.
(Accessed: May 31, 2023).
---------------------------------------------------------------------------

    As we observed previously, manufacturers may be shifting their
investment strategies in ways that may alter how stranded capital could
be considered. For example, some suppliers sell similar transmissions
to multiple manufacturers. Such arrangements allow manufacturers to
share in capital expenditures or amortize expenses more quickly.
Manufacturers share parts on vehicles around the globe, achieving
greater scale and greatly affecting tooling strategies and costs.
    As a proxy for stranded capital in recent CAFE analyses, the CAFE
Model

has accounted for platform and engine sharing and includes redesign and
refresh cycles for significant and less significant vehicle updates.
This analysis continues to rely on the CAFE Model's explicit year-by-
year accounting for estimated refresh and redesign cycles, and shared
vehicle platforms and engines, to moderate the cadence of technology
adoption and thereby limit the implied occurrence of stranded capital
and the need to account for it explicitly. In addition, confining some
manufacturers to specific advanced technology pathways through
technology adoption features acts as a proxy to indirectly account for
stranded capital. Adoption features specific to each technology, if
applied on a manufacturer-by-manufacturer basis, are discussed in each
technology section. We will monitor these trends to assess the role of
stranded capital moving forward.
    Finally, we ensure that our analysis is performance neutral because
the goal is to capture the costs and benefits of vehicle manufacturers
adding fuel economy improving technology because of CAFE
standards,\186\ and not to inappropriately capture costs and benefits
for changing other vehicle attributes that may have a monetary value
associated with them.\187\ This means that we ``SKIP'' some
technologies where we can reasonably assume that the technology would
not be able to maintain a performance attribute for the vehicle, and
where our simulation over test cycles may not capture the technology
limitation.
---------------------------------------------------------------------------

    \186\ One example is GM's 2nd generation High Feature V6 engine
manufactured at their Romulus, MI plant (https://www.gm.com/company/facilities/romulusaccessed2/24/2023). These engines are represented
by engine codes 113601, 113602, 113603 and should all be skipped for
HCR due to 113603 being a pickup engine on the GMC Canyon and
Chevrolet Colorado. DOT staff will add these skips for the final
rule.
    \187\ See, e.g., 87 FR 25887, citing EPA, Consumer Willingness
to Pay for Vehicle Attributes: What is the Current State of
Knowledge? (2018) (``The agency has previously attempted to model
the potential opportunity cost associated with changes in other
vehicle attributes in sensitivity analyses. In those other
rulemakings, the agency acknowledged that it is extremely difficult
to quantify the potential changes to other vehicle attributes. To
accurately do so requires extensive projections about which and how
much of other attributes will be altered and a detailed accounting
of how much value consumers assigned to those attributes. The agency
modeled the opportunity cost associated with changes in other
vehicle attributes using published empirical estimates of tradeoffs
between higher fuel economy and improvements to other attributes,
together with estimates of the values buyers attach to those
attributes. The agency does not believe this is an appropriate
methodology since there is considerable uncertainty in the
literature about how much fuel economy consumers are willing to pay
for and how consumers value other vehicle attributes. We note, for
example, a recent EPA-commissioned study that `found very little
useful consensus' regarding `estimates of the values of various
vehicle attributes,' which ultimately were `of little use for
informing policy decisions.' '').
---------------------------------------------------------------------------

    For example, prior to the development of SAE J2807, manufacturers
used internal rating methods for their vehicle towing capacity.
Manufacturers switched to the SAE tow rating standard at the next
redesign of their respective vehicles so that they could mitigate costs
via parts sharing and remain competitive in performance. Usually, the
most capable powertrain configuration will also have the highest towing
capacity and can be reflected in using this input feature. Separately,
we also ensure that the analysis is performance neutral through other
inputs and assumptions, like developing our engine maps assuming use
with a fuel grade most commonly available to
consumers.188 189 Those assumptions are discussed throughout
this section, and in Chapters 2 and 3 of the Draft TSD. Technology
``phase-in caps'' and the ``phase-in start years'' are defined in the
Technology Cost Input file and offer a way to gradually ``phase-in''
technology that is not yet fully mature to the analysis. They apply to
the manufacturer's entire estimated production and, for each
technology, define a share of production in each MY that, once
exceeded, will stop the model from further applying that technology to
that manufacturer's fleet in that MY.
---------------------------------------------------------------------------

    \188\ See, e.g., 85 FR 24386 (``Vehicle manufacturers typically
develop their engines and engine control system calibrations based
on the fuel available to consumers. In many cases, manufacturers may
recommend a fuel grade for best performance and to prevent potential
damage. In some cases, manufacturers may require a specific fuel
grade for both best performance, to achieve advertised power
ratings, and/or to prevent potential engine damage. Consumers,
though, may or may not choose to follow the manufacturer's
recommendation or requirement for a specific fuel grade for their
vehicle. As such, vehicle manufacturers often choose to employ
engine control strategies for scenarios where the consumer uses a
lower than recommended, or required, fuel octane level, as a way to
mitigate potential engine damage over the life of a vehicle. These
strategies limit the extent to which some efficiency improving
engine technologies can be implemented, such as increased
compression ratio and intake system and combustion chamber designs
that increase burn rates and rate of in-cylinder pressure rise. If
the minimum octane level available in the market were higher
(especially the current sub-octane regular grade in the mountain
states), vehicle manufacturers might not feel compelled to design
vehicles sub-optimally to accommodate such blends.'').
    \189\ Id. at 24390 (``As described in the NPRM and PRIA, the
agencies developed engine maps for technologies that are in
production today or that are expected to be available in the
rulemaking timeframe. The agencies recognize that engines with the
same combination of technologies produced by different manufacturers
will have differences in Brake-specific fuel consumption and other
performance measures, due to differences in the design of engine
hardware (e.g., intake runners and head ports, valves, combustion
chambers, piston profile, compression ratios, exhaust runners and
ports, turbochargers, etc.), control software, and emission
calibration. Therefore, the engine maps are intended to represent
the levels of performance that can be achieved on average across the
industry in the rulemaking timeframe.'').
---------------------------------------------------------------------------

    The influence of these inputs varies with regulatory stringency and
other model inputs. For example, setting the inputs to allow immediate
100 percent penetration of a technology will not guarantee any
application of the technology if stringency increases are low and the
technology is not at all cost effective. Also, even if these are set to
allow only very slow adoption of a technology, other model aspects and
inputs may nevertheless force more rapid application than these inputs,
alone, would suggest (e.g., because an engine technology propagates
quickly due to sharing across multiple vehicles, or because BEV
application must increase quickly in response to ZEV requirements). For
this analysis, nearly all of these inputs are set at levels that do not
limit the simulation at all.
    This analysis also applies phase-in caps and corresponding start
years to prevent the simulation from showing unlikely rates of applying
battery-electric vehicles (BEVs), such as showing that a manufacturer
producing very few BEVs in MY 2022 could plausibly replace every
product with a 300- or 400-mile BEV by MY 2026. Also, this analysis
applies phase-in caps and corresponding start years intended to ensure
that the simulation's plausible application of the highest included
levels of MR (20 percent reductions of vehicle ``glider'' weight) do
not, for example, outpace plausible supply of raw materials and
development of entirely new manufacturing facilities.
    These model logical structures and inputs act together to produce
estimates of ways each manufacturer could potentially shift to new
fuel-saving technologies over time, reflecting some measure of
protection against rates of change not reflected in, for example,
technology cost inputs. This does not mean that every modeled solution
would necessarily be economically practicable. Using technology
adoption features like phase-in caps and phase-in start years is one
mechanism that can be used so that the analysis better represents the
potential costs and benefits of technology application in the
rulemaking timeframe.

D. Technology Pathways, Effectiveness, and Cost

    The previous section discussed, at a high level, how we generate
the technology inputs and assumptions used in the CAFE Model. We do
this in several ways: by evaluating data

submitted by vehicle manufacturers; consolidating publicly available
data, press materials, marketing brochures, and other information;
collaborative research, testing, and modeling with other Federal
agencies; research, testing, and modeling with independent
organizations; determining that work done for prior rules is still
relevant and applicable; considering feedback from stakeholders on
prior rules and meetings conducted prior to the commencement of this
rulemaking; and using our own engineering judgment.
    This section discusses the specific technology pathways,
effectiveness, and cost inputs and assumptions used in the compliance
analysis. As an example, interested readers learned in the previous
section that the starting point for estimating technology costs is an
estimate of the DMC--the component and assembly costs of the physical
parts and systems that make up a complete vehicle--for any particular
technology; in this section, readers will learn that our transmission
technology DMCs are based on estimates from the NAS.
    After spending over a decade refining the technology pathways,
effectiveness, and cost inputs and assumptions used in successive CAFE
Model analyses, we have developed guiding principles to ensure that the
CAFE Model's compliance analysis results in impacts that we would
reasonably expect to see in the real world. These guiding principles
are as follows:
    Technologies will have complementary or non-complementary
interactions with the full vehicle technology system. The fuel economy
improvement from any individual technology must be considered in
conjunction with the other fuel-economy-improving technologies applied
to the vehicle, because technologies added to a vehicle will not result
in a simple additive fuel economy improvement from each individual
technology. We expect this result in particular from engine and other
powertrain technologies that improve fuel economy by allowing the ICE
to spend more time operating at efficient engine speed and load
conditions, or from engine technologies that both work to reduce the
effective displacement of the engine.
    The effectiveness of a technology depends on the type of vehicle
the technology is being applied to. When we talk about ``vehicle type''
in our analysis, we're referring to our vehicle technology classes--
e.g., a small car, a medium performance SUV, or a pickup truck, among
other classes. A small car and a medium performance SUV that use the
exact same technology will start with very different fuel economy
values; so, when the exact same technology is added to both of those
vehicles, the technology will provide a different effectiveness
improvement on both of those vehicles.
    The cost and effectiveness values for each technology should be
reasonably representative of what can be achieved across the entire
industry. Each technology model employed in the analysis is designed to
be representative of a wide range of specific technology applications
used in industry. Some vehicle manufacturers' systems may perform
better and cost less than our modeled systems and some may perform
worse and cost more. However, employing this approach will ensure that,
on balance, the analysis captures a reasonable level of costs and
benefits that would result from any manufacturer applying the
technology.
    The baseline for cost and effectiveness values must be identified
before assuming that a cost or effectiveness value could be employed
for any individual technology. For example, as discussed below, this
analysis uses a set of engine map models that were developed by
starting with a small number of baseline engine configurations, and
then, in a very systematic and controlled process, adding specific
well-defined technologies to create a new map for each unique
technology combination. Again, providing a consistent reference point
to measure incremental technology effectiveness values ensures that we
are capturing accurate effectiveness values for each technology
combination.
    The following sections discuss the engine, transmission,
electrification, MR, aerodynamic, ROLL, and other vehicle technologies
considered in this analysis. The following sections discuss:
     How we define the technology in the CAFE Model,\190\
---------------------------------------------------------------------------

    \190\ Note, due to the diversity of definitions industry
sometimes employs for technology terms, or in describing the
specific application of technology, the terms defined here may
differ from how the technology is defined in the industry.
---------------------------------------------------------------------------

     How we assigned the technology to vehicles in the analysis
fleet used as a starting point for this analysis,
     Any adoption features applied to the technology, so the
analysis better represents manufacturers' real-world decisions,
     The technology effectiveness values, and
     Technology cost.
    Please note that the following technology effectiveness sections
provide examples of the range of effectiveness values that a technology
could achieve when applied to the entire vehicle system, in conjunction
with the other fuel-economy-improving technologies already in use on
the vehicle. To see the incremental effectiveness values for any
particular vehicle moving from one technology key to a more advanced
technology key, see the CAFE Model Fuel Economy Adjustment Files that
are installed as part of the CAFE Model Executable File, and not in the
input/output folders. Similarly, the technology costs provided in each
section are examples of absolute costs seen in specific MYs, for
specific vehicle classes. Please refer to the Technologies Input File
to see all absolute technology costs used in the analysis across all
MYs.
    For the LD analysis we show two sets of technology effectiveness
charts for each technology type, titled ``Unconstrained'' and
``Standard Setting.'' For the Standard Setting charts, effectiveness
values reflect the application of 49 U.S.C. 32902(h) considerations to
the technologies; for example, PHEV technologies only show the
effectiveness achieved when operating in a gasoline only mode (charge
sustaining mode). The Unconstrained charts show the effectiveness
values modeled for the technologies without the 49 U.S.C; 32902(h)
constraints; for example, PHEV technologies show effectiveness for
their full dual fuel use functionality. The standard setting values are
used during the standard setting years being assessed in this analysis,
and the unconstrained values are used for all other years.
1. Engine Paths
    ICEs convert chemical energy in fuel to useful mechanical power.
The chemical energy is converted to mechanical power by being burned or
oxidized inside the engine. The air/fuel mixture entering the engine
and burned fuel/exhaust by-products leaving the engine are the working
fluids in the engine. The engine power output is a direct result of the
work interaction between these fluids and the mechanical components of
the engine.\191\ The generated mechanical power is used to perform
useful work, such as vehicle propulsion. For a complete discussion on
fundamentals of engine characteristics, such as torque, torque maps,
engine load, power density, brake mean effective pressure (BMEP),
combustion cycles, and

components, please refer to Heywood 2018.\192\
---------------------------------------------------------------------------

    \191\ Heywood, John B. Internal Combustion Engine Fundamentals.
McGraw-Hill Education, 2018. Chapter 1.
    \192\ Heywood, John B. Internal Combustion Engine Fundamentals.
McGraw-Hill Education, 2018.
---------------------------------------------------------------------------

    We classify the extensive variety of both LD and HDPUV vehicle IC
engine technologies into discrete Engine Paths. These paths are used to
model the most representative characteristics, costs, and performance
of the fuel-economy improving engine technologies most likely available
during the rulemaking time frame. The paths are intended to be
representative of the range of potential performance levels for each
engine technology. In general, the paths are tied to ease of
implementation of additional technology and how closely related the
technologies are. The technology paths for LD and HDPUV can be seen in
Figure II-11 and Figure II-12 respectively.
BILLING CODE 4910-59-P
[GRAPHIC] [TIFF OMITTED] TP17AU23.020

[GRAPHIC] [TIFF OMITTED] TP17AU23.021

BILLING CODE 4910-59-C
    The LD Engine Paths have been selected and refined over a period of
more than ten years, based on engines in the market, stakeholder
comments, and our engineering judgment, subject to the following
factors: we included technologies most likely available during the
rulemaking time frame and the range of potential performance levels for
each technology, and excluded technologies unlikely to be feasible in
the rulemaking timeframe, technologies unlikely to be compatible with
U.S. fuels, or technologies for which there was not appropriate data
available to allow the simulation of effectiveness across all vehicle
technology classes in this analysis.
    For technologies on the HDPUV Engine Paths, we revisited work done
for the HDPUV analysis in the Phase 2 rulemaking. We have updated our
HDPUV Engine Paths based on that work, the availability of technology
in the HDPUV baseline fleet, and technologies we believe will be
available in the rulemaking timeframe. The HDPUV fleet is significantly
smaller than the LD fleet with the majority of vehicles being produced
by only three manufacturers. These vehicles include work trucks and
vans that are focused on transporting people, moving equipment and
supplies, and tend to be more focused on a common need than that of
vehicles in the LD fleet, which includes everything from sports cars to
commuter cars and pickup trucks. The engines options between the two
fleets are different in the real world and are accordingly different in
the analysis. HDPUVs are work vehicles and their engines must be able
to handle the additional work such as higher payloads, towing, and
additional stop and go demands. This results in HDPUVs often requiring
larger, more robust, and more powerful engines. As a result of the
HDPUV's smaller fleet size and narrowed focus, fewer engines and engine
technologies are developed or used in this fleet. That said, we believe
that the range of technologies between the HDPUV Engine Paths and
Electrification/Hybrid/Electrics Path presents a reasonable
representation of powertrain options available for HDPUVs now and in
the rulemaking time frame.
    We begin defining engine technology options by defining potential
engine configurations: dual over-head camshaft (DOHC) engines have two
camshafts per cylinder head (one operating the intake valves and one
operating the exhaust valves), single over-head camshaft (SOHC) engines
have a single camshaft, and over-head valve (OHV) engines also have a
single camshaft located inside of the engine (south of the valves
rather than over-head) connected to a rocker arm that actuates the
valves. DOHC and SOHC engine configurations are common in the LD fleet,
while OHV engine configurations are more common in the HDPUV fleet.
    The next step along the Engine Paths is at the Basic Engine Path
technologies. These include variable valve lift (VVL), stoichiometric
gasoline direct injection (SGDI), and a basic level of cylinder
deactivation (DEAC). VVL dynamically adjusts how far the valve opens
and reduces fuel consumption by reducing pumping losses and optimizing
airflow over broader range of engine operating conditions. Instead of
injecting fuel at lower pressures and before the intake valve, SGDI
injects fuel directly into the cylinder at high pressures allowing for
more precise fuel delivery while providing a cooling effect and
allowing for an increase in the CR and/or more optimal spark timing for
improved efficiency. DEAC disables the intake and exhaust valves and
turns off fuel injection on select cylinders which effectively, allows
the engine to operate temporarily as if it were smaller while

also reducing pumping losses to improve efficiency. New for this
analysis is that variable valve timing (VVT) technology is integrated
in all non-diesel engines, so we do not have a separate box for it on
the Basic Engine Path. For the LD analysis, VVL, SGDI, and DEAC can be
applied to an engine individually or in combination with each other,
and for the HDPUV analysis, SGDI and DEAC can be applied individually
or in combination.
    Moving beyond the Basic Engine Path technologies are the
``advanced'' engine technologies, which means that applying the
technology--both in our analysis and in the real world--would require
significant changes to the structure of the engine or an entirely new
engine architecture. The advanced engine technologies represent the
application of alternate combustion cycles, various applications of
forced induction technologies, or advances in cylinder deactivation.
    Advanced cylinder deactivation (ADEAC) systems, also known as
rolling or dynamic cylinder deactivation systems, allow the engine to
vary the percentage of cylinders deactivated and the sequence in which
cylinders are deactivated. Depending on the engine's speed and
associated torque requirements, an engine might have most cylinders
deactivated (e.g., low torque conditions as with slower speed driving)
or it might have all cylinders activated (e.g., high torque conditions
as with merging onto a highway).\193\ An engine operating at low speed/
low torque conditions can then save fuel by operating as if it is only
a fraction of its total displacement. We model two ADEAC technologies,
advanced cylinder deactivation on a single overhead camshaft engine
(ADEACS), and advanced cylinder deactivation on a dual overhead
camshaft engine (ADEACD).
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    \193\ See for example, Dynamic Skip Fire, Tula Technology, DSF
in real world situations, https://www.tulatech.com/combustion-engine/. Our modeled ADEAC system is not based on this specific
system, and therefore the effectiveness improvement will be
different in our analysis than with this system, however, the theory
still applies.
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    Forced induction gasoline engines include both supercharged and
turbocharged downsized engines, which can pressurize or force more air
into an engine's intake manifold when higher power output is needed.
The raised pressure results in an increased amount of airflow into the
cylinder supporting combustion, increasing the specific power of the
engine. The baseline turbocharged downsized technology (TURBO0) engine
represents a basic level of forced air induction technology being
applied to a DOHC engine. Cooled exhaust gas recirculation (CEGR)
systems take engine exhaust gasses and passes them through a heat
exchanger to reduce their temperature, and then mixes them with
incoming air in the intake manifold. We model the base TURBO0
turbocharged engine with cooled exhausted recirculation (TURBOE), basic
cylinder deactivation (TURBOD), and advanced cylinder deactivation
(TURBOAD). Walking down the Turbo Engine Path leads to engines that
have higher BMEP, which is a function of displacement and power. The
higher the BMEP, the higher the engine performance. We model two levels
of advanced turbocharging technology (TURBO1 and TURBO2) that run
increasingly higher turbocharger boost levels, burning more fuel, and
making more power for a given displacement. As discussed above, we pair
turbocharging with engine downsizing, meaning that the turbocharged
downsized engines in our analysis improve vehicle fuel economy by using
less fuel to power the smaller engine while maintaining vehicle
performance.
    In this analysis, high compression ratio (HCR) engines represent a
class of engines that achieve a higher level of fuel efficiency by
implementing a high geometric CR with varying degrees of late intake
valve closing (LIVC) (i.e., closing the intake valve later than usual)
using VVT, and without the use of an electric drive
motor.194 195 These engines operate on a modified Atkinson
cycle allowing for improved fuel efficiency under certain engine load
conditions but still offering enough power to not require an electric
motor; however, there are limitations on how HCR engines can apply LIVC
and the types of vehicles that can use this technology. The way that
each individual manufacturer implements a modified Atkinson cycle will
be unique, as each manufacturer must balance not only fuel efficiency
considerations, but emissions, on-board diagnostics, and safety
considerations that includes the vehicle being able to operate
responsively to the driver's demand.
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    \194\ Late intake valve closing (LIVC) is a method manufacturers
use to reduce the effective compression ratio and allow the
expansion ratio to be greater than the compression ratio resulting
in improved fuel economy but reduced power density. Further
technical discussion on HCR and Atkinson Engines are discussed in
Draft TSD Chapter <IP_ADDRESS>.3.
    \195\ See the 2015 NAS report, Appendix D, for a short
discussion on thermodynamic engine cycles.
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    We define HCR engines as being naturally aspirated, gasoline, SI,
using a geometric CR of 12.5:1 or greater,\196\ and able to dynamically
apply various levels of LIVC based on load demand. An HCR engine uses
less fuel for each engine cycle, which increases fuel economy, but
decreases power density (or torque). Generally, during high loads--when
more power is needed--the engine will use variable valve actuation to
reduce the level of LIVC by closing the intake valve earlier in the
compression stroke (leaving more fuel in the compression chamber),
increasing the effective CR, reducing over-expansion, and sacrificing
efficiency for increased power density.\197\ However, there is a limit
to how much fuel can remain in the compression chamber of an HCR engine
because over-compression of the air-fuel mixture can lead to engine
knock.\198\ Conversely, at low loads the engine will typically increase
the level of LIVC by closing the intake valve later in the compression
stroke, reducing the effective CR, increasing the over-expansion, and
sacrificing power density for improved efficiency. By closing the
intake valve later in the compression stroke (i.e., applying more
LIVC), the engine's displacement is effectively reduced, which results
in less air and fuel for combustion and a lower power output.\199\
Varying LIVC can be used to mitigate, but not eliminate, the low power
density issues that can constrain the application of an Atkinson-only
engine.
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    \196\ Note that even if an engine has a compression ratio of
12.5:1 or greater, it does not necessarily mean it is an HCR engine
in our analysis, as discussed below. We look at a number of factors
to perform baseline engine assignments.
    \197\ Variable valve actuation is a general term used to
describe any single or combination of VVT, VVL, and variable valve
duration used to dynamically alter an engines valvetrain during
operation.
    \198\ Engine knock in spark ignition engines occurs when
combustion of some of the air/fuel mixture in the cylinder does not
result from propagation of the flame front ignited by the spark
plug, but one or more pockets of air/fuel mixture explodes outside
of the envelope of the normal combustion front.
    \199\ Power = (force x displacement)/time.
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    When we say, ``lower power density issues,'' this translates to a
low torque density,\200\ meaning that the engine cannot create the
torque required at necessary speeds to meet load demands. To the extent
that a vehicle requires more power in a given condition than an engine
with low power density can provide, that engine would experience issues
like engine knock for the reasons discussed above, but more
importantly, an engine designer would not allow an engine application
where the engine has the potential to operate in unsafe conditions in
the first place. Instead, a manufacturer could significantly

increase an engine's displacement (i.e., size) to overcome those low
power density issues,\201\ or could add an electric motor and battery
pack to provide the engine with more power, but a far more effective
pathway would be to apply a different type of engine technology, like a
downsized, turbocharged engine.\202\
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    \200\ Torque = radius x force.
    \201\ But see the 2022 EPA Trends Report at 46 (``As vehicles
have moved towards engines with a lower number of cylinders, the
total engine size, or displacement, is also at an all-time low.''),
and the discussion below about why we do not believe manufacturers
will increase the displacement of HCR engines to make the necessary
power.
    \202\ See, e.g., Toyota Newsroom. 2024 Toyota Tacoma Makes Debut
on the Big Island, Hawaii. May 19, 2023. Available at: https://pressroom.toyota.com/2024-toyota-tacoma-makes-debut-on-the-big-island-hawaii/. (Accessed: May 31, 2023). The 2024 Toyota Tacoma
comes in 8 ``grades,'' all of which use a turbocharged engine.
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    Vehicle manufacturers' intended performance attributes for a
vehicle--like payload and towing capability, intention for off-road
use, and other attributes that affect frontal area and rolling
resistance--dictate whether an HCR engine can be a suitable technology
choice for that vehicle.203 204 As vehicles require higher
payloads and towing capacities,\205\ or experience road load increases
from larger all-terrain tires or a larger frontal area and less
aerodynamic design, or experience driveline losses for AWD and 4WD
configurations, more engine torque is required at all engine speeds.
Any time more engine torque is required the application of this
technology becomes less effective and more limited.\206\ For these
reasons, to maintain a performance-neutral analysis, and as discussed
further below, we limit non-hybrid and non-plug-in-hybrid HCR engine
application to certain categories of vehicles.\207\ Also for these
reasons, HCR engines are not found in the HDPUV baseline fleet nor are
they available as an engine option in the HDPUV analysis.
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    \203\ Supplemental Comments of Toyota Motor North America, Inc.,
Notice of Proposed Rulemaking: Safer Affordable Fuel-Efficient
Vehicles Rule, Docket ID Numbers: NHTSA-2018-0067 and EPA-HQ-OAR-
2018-0283. p.6.
    \204\ Feng, R. et al. 2016. Investigations of Atkinson Cycle
Converted from Conventional Otto Cycle Gasoline Engine. SAE
Technical Paper. Available at: https://www.sae.org/publications/technical-papers/content/2016-01-0680/. (Accessed: May 31, 2023).
    \205\ See Tucker, S. 2023. What Is Payload: A Complete Guide,
Kelly Blue Book. Last revised: Feb. 2, 2023. Availale at: https://www.kbb.com/car-advice/payload-guide/#link3. (Accessed: May 31,
2023). (``Roughly speaking, payload capacity is the amount of weight
a vehicle can carry, and towing capacity is the amount of weight it
can pull. Automakers often refer to carrying weight in the bed of a
truck as hauling to distinguish it from carrying weight in a trailer
or towing.'').
    \206\ Supplemental Comments of Toyota Motor North America, Inc.,
Notice of Proposed Rulemaking: Safer Affordable Fuel-Efficient
Vehicles Rule, Docket ID Numbers: NHTSA-2018-0067 and EPA-HQ-OAR-
2018-0283. (``Tacoma has a greater coefficient of drag from a larger
frontal area, greater tire rolling resistance from larger tires with
a more aggressive tread, and higher driveline losses from 4WD.
Similarly, the towing, payload, and off road capability of pick-up
trucks necessitate greater emphasis on engine torque and horsepower
over fuel economy.
     This translates into engine specifications such as a larger
displacement and a higher stroke-to-bore ratio. . . . Tacoma's
higher road load and more severe utility requirements push engine
operation more frequently to the less efficient regions of the
engine map and limit the level of Atkinson operation . . . This
endeavor is not a simple substitution where the performance of a
shared technology is universal. Consideration of specific vehicle
requirements during the vehicle design and engineering process
determine the best applicable powertrain.'').
    \207\ To maintain performance neutrality when sizing powertrains
and selecting technologies we perform a series of simulations in
Automime which are further discussed in the TSD Chapter 2.3.4 and in
the CAFE Analysis Autonomie Documentation. The concept of
performance neutrality is discussed in detail above in Section
II.C.3, Technology Effectiveness Values, and additional reasons why
we maintain a performance neutral analysis are discussed in Section
II.C.6, Technology Applicability Equations and Rules.
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    For this analysis, our HCR Engine Path includes three technology
options: (1) a baseline Atkinson-enabled engine (HCR) with VVT and
SGDI, (2) an Atkinson enabled engine with cooled exhaust gas
recirculation (HCRE), and finally, (3) the Atkinson enabled engine with
DEAC (HCRD). This updated family of HCR engine map models also reflects
our statement in NHTSA's May 2, 2022 final rule that a single engine
that employs an HCR, CEGR, and DEAC ``is unlikely to be utilized in the
rulemaking timeframe based on comments received from the industry
leaders in HCR technology application.'' \208\
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    \208\ 87 FR 25796 (May 2, 2022).
---------------------------------------------------------------------------

    These three HCR Engine Path technology options (HCR, HCRE, HCRD)
should not be confused with the hybrid and plug-in hybrid electric
pathway options that also utilize HCR engines in combination with an
electrified powertrain (i.e., P2HCR, P2HCRE, PHEV20H, and PHEV50H);
those hybridization path options are discussed in Section II.D.3,
below. In contrast, Atkinson engines in this analysis (SHEVPS,
PHEV20PS, and PHEV50PS) run the Atkinson Cycle full time, but are
connected to an electric motor. The full-time Atkinson engines are
discussed in Section II.D.3 below.
    The Miller cycle is another alternative combustion cycle that uses
an extended expansion stroke, similar to the Atkinson cycle, to improve
fuel efficiency. Miller cycle-enabled engines have a similar trade-off
in power density as Atkinson engines; the lower power density requires
a larger volume engine in comparison to an Otto cycle-based
turbocharged system for similar applications.\209\ To address the
impacts of the extended expansion stroke on power density during high
load operating conditions, the Miller cycle operates in combination
with a forced induction system. In our analysis, the baseline Miller
cycle-enabled engine includes the application of variable turbo
geometry technology (VTG), or what is also known as a variable-geometry
turbocharger. VTG technology allows the turbocharger to adjust key
geometric characteristics of the system, thus allowing adjustment of
boost profiles and response based on the engine's operating needs. The
adjustment of boost profile during operation increases the engine's
power density over a broader range of operating conditions and
increases the functionality of a Miller cycle-based engine. The use of
a variable geometry turbocharger also supports the use of CEGR. The
second level of VTG Engine technology in our analysis (VTGE) is an
advanced Miller cycle-enabled system that includes the application of
at least a 40V-based electronic boost system. An electronic boost
system has an electric motor added to assist the turbocharger; the
motor assist mitigates turbocharger lag and low boost pressure by
providing the extra boost needed to overcome the torque deficit at low
engine speeds.
---------------------------------------------------------------------------

    \209\ National Academies of Sciences, Engineering, and Medicine.
2021. Assessment of Technologies for Improving Light-Duty Vehicle
Fuel Economy 2025-2035.The National Academies Press: Washington, DC.
Section 4. Available at: https://doi.org/10.17226/26092. (Accessed:
May 31, 2023). [hereinafter 2021 NAS report].
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    Variable compression ratio (VCR) engines work by changing the
length of the piston stroke of the engine to optimize the CR and
improve thermal efficiency over the full range of engine operating
conditions. Engines that use VCR technology are currently in production
as small displacement turbocharged in-line four-cylinder, high BMEP
applications.
    Diesel engines have several characteristics that result in better
fuel efficiency over traditional gasoline engines, including reduced
pumping losses due to lack of (or greatly reduced) throttling, high
pressure direct injection of fuel, a combustion cycle that operates at
a higher CR, and a very lean air/fuel mixture relative to an
equivalent-performance gasoline engine. However, diesel technologies
require additional systems to control NOX emissions, such as
a NOX adsorption catalyst system or a urea/ammonia selective
catalytic reduction system. We included two

levels of diesel engine technology in both the LD and HDPUV analyses:
the baseline diesel engine technology (ADSL) is a turbocharged diesel
engine, and the more advanced diesel engine (DSLI) adds DEAC to the