authority. Accordingly, we made no updates to the fuel assumed used in
the engine map models.
---------------------------------------------------------------------------

    \254\ See TSD Chapter 3.1 for a detailed discussion on engine
map model assumptions.
---------------------------------------------------------------------------

(a) IAV Engine Map Models
    Most of the engine map models used in this analysis were developed
by IAV GmbH (IAV) Engineering. IAV is one of the world's leading
automotive industry engineering service partners with an over 35-year
history of performing research and development for powertrain
components, electronics, and vehicle design.\255\ The primary outputs
of IAV's work for this analysis are engine maps that model the
operating characteristics of engines equipped with specific
technologies.
---------------------------------------------------------------------------

    \255\ IAV Automotive Engineering, https://www.iav.com/en/.
(Accessed: February 17, 2022)
---------------------------------------------------------------------------

    The generated engine maps are validated against IAV's global
database of benchmarked data, engine test data, single cylinder test
data, prior modeling studies, technical studies, and information
presented at conferences.\256\ The effectiveness values from the
simulation results are also validated against detailed engine maps
produced from Argonne engine benchmarking programs, as well as
published information from industry and academia, ensuring reasonable
representation of simulated engine technologies.\257\ The engine map
models used in this analysis and their specifications are shown in
Table III-9.
---------------------------------------------------------------------------

    \256\ Friedrich, I., Pucher, H., and Offer, T., ``Automatic
Model Calibration for Engine-Process Simulation with Heat-Release
Prediction,'' SAE Technical Paper 2006-01-0655, 2006, https://doi.org/10.4271/2006-01-0655. (Accessed: February 17, 2022) Rezaei,
R., Eckert, P., Seebode, J., and Behnk, K., ``Zero-Dimensional
Modeling of Combustion and Heat Release Rate in DI Diesel Engines,''
SAE Int. J. Engines 5(3):874-885, 2012, https://doi.org/10.4271/2012-01-1065. (Accessed: February 17, 2022) Multistage Supercharging
for Downsizing with Reduced Compression Ratio (2015). MTZ Rene
Berndt, Rene Pohlke, Christopher Severin and Matthias Diezemann IAV
GmbH. Symbiosis of Energy Recovery and Downsizing (2014). September
2014 MTZ Publication Heiko Neukirchner, Torsten Semper, Daniel
Luederitz and Oliver Dingel IAV GmbH.
    \257\ Bottcher, L., Grigoriadis, P. ``ANL--BSFC map prediction
Engines 22-26.'' IAV (April 30, 2019). https://lindseyresearch.com/wp-content/uploads/2021/09/NHTSA-2021-0053-0002-20190430_ANL_Eng-22-26-20190430_ANL_Eng22-26Updated_Docket.pdf. (Accessed: February 17,
2022)
---------------------------------------------------------------------------

BILLING CODE 4910-59-P

[GRAPHIC] [TIFF OMITTED] TR02MY22.067

BILLING CODE 4910-59-C
    We received a comment from ICCT regarding the validity of the
continued use of the IAV engine map models. ICCT stated that ``[t]he
engine maps that are

included in the agency modeling are severely outdated. For example, all
base naturally aspirated engine maps are based on an unidentified 2013
or older vehicle, all turbo (non-Miller cycle) maps are based on a
vehicle whose specifications match that of the 2011 MINI R56 N18/BMW
N13 engine, the hybrid Atkinson cycle map (for PS and PHEV) is based on
the 2010 Toyota Prius, and the HCR1 map is based on the 2014 Mazda
SkyActiv 2.0L engine. Essentially, NHTSA is assuming there will be no
efficiency improvements in any of these technologies through at least
2026, or for 12 to 16 years from the model year of the vehicle used to
generate the maps.'' \258\
---------------------------------------------------------------------------

    \258\ ICCT, at p. 3.
---------------------------------------------------------------------------

    We disagree with statements that the IAV engine maps are outdated.
Many of the engine maps were developed specifically to support analysis
for the current rulemaking time frame. The engine map models encompass
engine technologies that are present in the analysis fleet and
technologies that could be applied in the rulemaking timeframe. In many
cases those engine technologies are mainstream today and will continue
to be during the rulemaking timeframe. For example, the engines on some
MY 2020 vehicles in the analysis fleet have technologies that were
initially introduced ten or more years ago. Having engine maps
representative of those technologies is important for the analysis. The
most basic engine technology levels also provide a useful baseline for
the incremental improvements for other engine technologies. The
timeframe for the testing or modeling is unimportant because time by
itself doesn't impact engine map data. A given engine or model will
produce the same BSFC map regardless of when testing or modeling is
conducted. Simplistic discounting of engine maps based on temporal
considerations alone could result in discarding useful technical
information.
    If we did use a mix of engine maps from engine modeling and from
benchmarking data, no common reference for measuring impacts of adding
specific technological improvements would exist. Additionally,
manufacturers often implement multiple fuel-saving technologies
simultaneously when redesigning a vehicle and it is not possible to
isolate the effect of individual technologies by using laboratory
measurements of a single production engine or vehicle with a
combination of technologies.\259\ Because so many vehicle and engine
changes are involved, it is not possible to attribute effectiveness
improvements accurately for benchmarked engines to specific technology
changes. Further, while two or more different manufacturers may produce
engines with the same high level technologies (such as a DOHC engine
with VVT and SGDI), each manufacturer's engine will have unique
component designs that cause its version of the engine to have a unique
engine map. For example, engines with the same high level technologies
have unique intake manifold and exhaust manifold runners, cylinder head
ports and combustion chamber geometry that impact charge motion,
combustion and efficiency, as well as unique valve control, compression
ratios, engine friction, cooling systems, and fuel injector spray
characteristics, among other factors. All of these differences lead to
potential overcounting or undercounting technology effectiveness per
cost. As described above, our approach allows the analysis to isolate
the effects of individual technologies by incrementally adding
individual technologies to baseline engine configurations. We selected
this approach for the NPRM and final rule and discuss it in detail in
the TSD.\260\
---------------------------------------------------------------------------

    \259\ See e.g., Toyota Supplemental Comments to the 2018 NPRM,
Docket No. NHTSA-2018-0067-12431 (``Atkinson-cycle operation is just
one of several measures responsible for the 2.5L Dynamic Force
engine achieving a world-best 40 percent thermal efficiency. The
Late Intake Valve Closing (LIVC) of the Atkinson cycle reduces low-
load pumping losses and supports the 13:1 CR by suppressing engine
knock. However, the engine's increased stroke-to-bore ratio (S/B
ratio) and improved cooling, engine warmup, friction reduction, and
exhaust system play an equally important role. For example, the 1.18
S/B ratio preserves stable combustion under high EGR flow rates
which improves thermal efficiency as much as the longer effective
expansion ratio from the Atkinson cycle. The increased S/B ratio
also compliments intake port, valve timing (VVT-iE) and piston
enhancements resulting in greater tumble intensity of the charge-air
intake, higher speed combustion, and increased thermal efficiency.
Greater detail on factors contributing to the thermal efficiency of
the 2018 Camry 2.5L engine can be found in Toyota SAE paper 2017-01-
1021 contained in Appendix 1 of this submission.'').
    \260\ See TSD Chapter 3.1.
---------------------------------------------------------------------------

    As a result, it should not be expected that any of our engine maps
would necessarily align with a specific manufacturer's engine, unless
of course the engine map was developed from that specific engine. We do
not agree that comparing an engine map used for the rulemaking analysis
to a single specific benchmarked engine has technical relevance, beyond
serving as a general corroboration for the engine map. When a vehicle
is benchmarked, the resulting data are dictated by the unique
combination of technologies and design constraints for the whole
vehicle system.
    ICCT further stated: ``As just two examples of how absurd it is to
assume no improvements in any of these engine technologies for at least
12 years, the turbocharged engine introduced by Honda in 2016 was
significantly more efficient than the engine used to generate all the
turbocharged maps in the proposed rule and the 2018 Camry hybrid
improved fuel economy by 15 (XLE/SE) to 25 percent (LE) compared to the
2017 Camry hybrid. And these (unincorporated) improvements were already
in the market by 2016 and 2018--still 8 to 10 years before 2026. For
additional information see UCS Reconsideration Petition pages 68-72.''
\261\ ICCT also stated ``EPA added a 2nd generation turbocharged
downsized engine package based on EPA benchmark testing of the Honda
L15B7 1.5L turbocharged, direct-injection engine to its 2018 MTE, which
was not used in NHTSA's proposed rule.'' \262\
---------------------------------------------------------------------------

    \261\ ICCT, at p. 4.
    \262\ Id.
---------------------------------------------------------------------------

    Our effectiveness data, including engine map models, is not used in
the rulemaking analysis in the manner described in ICCT's comments. Our
analysis does not apply absolute BSFC levels from the engine maps to
any vehicle model or configuration for the rulemaking analysis. The
absolute fuel economy values from the full vehicle Autonomie
simulations are used only to determine incremental effectiveness for
switching from one technology to another technology. The incremental
effectiveness is applied to the absolute fuel economy of vehicles in
the analysis fleet, which are based on CAFE compliance data. For
subsequent technology changes, incremental effectiveness is applied to
the absolute fuel economy level of the previous technology
configuration. Therefore, for a technically sound analysis, it is most
important that the differences in BSFC among the engine maps be
accurate, and not the absolute values of the individual engine maps.
    This comment also mirrors a similar ICCT comment to the 2018
NPRM.\263\ In the 2020 final rule, we compared two IAV engine maps to
the EPA's benchmarked Toyota 2017 2.5L naturally aspirated engine and
Honda's 2016 1.5L turbocharged downsized engine for predicted
effectiveness improvements. The IAV engines were modeled and simulated
in a midsize non-performance vehicle with an automatic transmission and
the same

road load technologies, MR0, ROLL0 and AERO0, to isolate for the
benefits associated with the specific engine maps.\264\ Eng 12, a 1.6L,
4-cylinder, turbocharged, SGDI, DOHC, dual cam VVT, VVL engine was
selected as the closest engine configuration to the Honda 1.5L.\265\
Eng 22b, a 2.5L, 4 cylinder, VVT Atkinson cycle engine, was selected as
the closest engine configuration to the Toyota 2.5L.\266\ Both the
Toyota 2.5L naturally aspirated engine and Honda's 1.5L engine have
incorporated a number of fuel saving technologies, including improved
accessories and engine friction reduction. To assure an ``apples-to-
apples'' comparison, both IACC and EFR technologies were applied to the
IAV engine maps. IACC technology provides an additional 3.6 percent
incremental improvement and EFR provides an additional 1.4 percent
incremental improvement beyond the IAV engine maps for midsize non-
performance vehicles.
---------------------------------------------------------------------------

    \263\ ICCT, Attachment 3, Docket No. NHTSA-2018-0067-11741, at
p. I-49.
    \264\ See TSD Chapter 3.4, TSD Chapter 3.5, and TSD Chapter 3.6
for more information on road load modeling.
    \265\ See TSD Chapter 3.1 for more discussion on modeled engine
technologies.
    \266\ See TSD Chapter 3.1 for more discussion on modeled engine
technologies.
---------------------------------------------------------------------------

    The comparison shows that the relative effectiveness of the IAV
engine maps are in line with the Honda 1.5L and the Toyota 2.5L
benchmarked engines. Figure III-8 below shows the effectiveness
improvements for the EPA benchmarked engines and the corresponding IAV
engine maps incremental to a baseline vehicle. Accordingly, we believe
that the methodology used in this analysis, and the engine maps and
incremental effectiveness values used, are in line with benchmarking
data and are reasonable for the rulemaking analysis. We believe the
approach used in this rulemaking analysis appropriately allows us to
account for a wide array of engine technologies that could be adopted
during the rulemaking timeframe. Declining to use manufacturer-specific
engines allows us to ensure that all effectiveness and cost
improvements due to the incremental addition of fuel economy improving
technologies are appropriately accounted for.
[GRAPHIC] [TIFF OMITTED] TR02MY22.068

(b) Other Engine Map Models
    Two of the engine map models we show in Table III-9, Eng24 and
Eng25, were not developed as part of the IAV modeling effort and we
only used Eng24 in this analysis. The Eng24 and Eng25 engine maps are
equivalent to the ATK and ATK2 engine map models developed for the 2016
Draft TAR, EPA Proposed Determination, and Final Determination.\267\
The ATK1 engine model is based directly on the 2.0L 2014 Mazda
SkyActiv-G (ATK) engine. The ATK2 represents an Atkinson engine concept
based on the Mazda engine, adding cEGR, cylinder deactivation, and an
increased compression ratio (14:1). In this analysis, Eng24 and Eng25
correspond to the HCR1 and HCR2 technologies.
---------------------------------------------------------------------------

    \267\ Ellies, B., Schenk, C., and Dekraker, P., ``Benchmarking
and Hardware-in-the-Loop Operation of a 2014 MAZDA SkyActiv 2.0L
13:1 Compression Ratio Engine,'' SAE Technical Paper 2016-01-1007,
2016, doi:10.4271/2016-01-1007.
---------------------------------------------------------------------------

    We used the same HCR2 engine map model application in this analysis
as we used in the 2020 final rule.\268\ The agency believes the use of
HCR0, HCR1, and the new addition of HCR1D reasonably represents the
application of Atkinson Cycle engine technologies within the current
light-duty fleet and the anticipated applications of Atkinson Cycle
technology in the MY 2024-2026 timeframe. We sought comment on whether
and how to change our engine maps for HCR2 in the analysis for the
final rule.
---------------------------------------------------------------------------

    \268\ 85 FR 24425-27 (April 30, 2020).

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

    ICCT, among others supported the use of the HCR2 engine map model
stating that: 269 270 271 272
---------------------------------------------------------------------------

    \269\ NRDC, at p. 47.
    \270\ UCS, at p. 6.
    \271\ CARB, at p. 4.
    \272\ California Attorney General et al., A2, at p. 33.

    Not only does EPA's proposed rule allow HCR2 technology to be
used in their modeling, but comments previously submitted and
previous EPA documentation provide extensive justification for HCR
technology benefits beyond just HCR1D. Also, both cooled EGR and
cylinder deactivation have been in production since 2018. Thus, it
is not credible to assume no further advances in HCR technology
prior to 2027. Further, the manufacturer claim of ``diminishing
returns to additional conventional engine technology improvements''
is also not credible, given the discussion in the Appendix Section 1
of extensive engine technologies under development that can reduce
GHG emissions by over 30 [percent]. ICCT certainly supports
developing an updated family of HCR engine map models that
incorporate many of the technologies discussed in Section 1 for
future rulemakings. But in the interim, HCR2 should be allowed in
the Final Rule using EPA's engine map for HCR2 developed in the
Technical Support Documents for EPA's Proposed and 2017 Final
Determination.\273\
---------------------------------------------------------------------------

    \273\ ICCT, at p. 11.

    Other commenters were opposed to the use of the HCR2 engine map
model in the analysis. Toyota provided comment on both the NHTSA and
---------------------------------------------------------------------------
EPA analysis, stating that:

    HCR2 Atkinson engine technology has returned to EPA's compliance
modeling. EPA now defines HCR2 as ``the addition of dynamic cylinder
deactivation and cooled EGR within non-HEV Atkinson Cycle engine
applications''. However, the cost, technology effectiveness, and
underlying engine map used for modeling HCR2 technology appears
identical to that used for the SAFE 2 Final Rule which is
represented by the simulated and experimental effectiveness of the
2014 2.0L SKYACTIV engine with the addition of cooled Exhaust Gas
Recirculation (cEGR), 14:1 compression ratio (CR), and cylinder
deactivation. There is still no U.S. production vehicle that
incorporates this definition of HCR2 technology because the 14:1 CR
requires higher octane than currently available in U.S. regular
grade gasoline. Further, there are more cost-effective pathways than
combining cylinder deactivation with Atkinson cycle engines which
have inherently low pumping loss characteristics.
    EPA compliance modeling applies HCR2 engine technology to over
40 percent of Toyota's fleet by 2026 model year. For example, Camry
receives HCR2 along with engine friction reduction (EFR) in 2024
model year. The resulting 51.7 mpg fuel economy is about a 9
[percent] improvement over Toyota's current generation Camry powered
by a 2.5L Atkinson engine which has a world-best 40 [percent]
thermal efficiency. The modeled [CO2] and fuel economy
are closer to hybrid Camry performance and are unreasonably large
for the technologies involved. First, cylinder deactivation is the
only practical distinction between HCR2 and Toyota's 2.5L Dynamic
Force Atkinson engine. NHTSA's evaluation has determined applying
only cylinder deactivation to Atkinson cycle engines (HCR1) nets an
incremental improvement of roughly 2 percent. Second, the 2.5L
Dynamic Force engine already encompasses EFR as explained in past
comments under CBI. Finally, IACC and EFR benefits appear to be
double counted on top of ERF already being included in the Camry
2.5L Atkinson engine. This is because IACC and EFR are both fully
included in the simulated HCR2 engine map, yet both technologies are
added again in the CAFE Model runs.
    EPA modeling sequentially adds enhanced technology to a 2017
baseline fleet until compliance with the proposed standards is
achieved. The 2017 model year fleet is outdated because it fails to
capture more recent state-of-the-art technologies in the U.S. fleet
and requires the [CO2] reduction effectiveness of those
technologies to be assumed or simulated. An example is Toyota's 2.5L
Atkinson engine technology which has been in the market since 2018
model year. The Camry example above could largely be avoided using a
more recent baseline. A 2020 model year baseline fleet is more
appropriate and provides a more accurate performance assessment, and
with fewer product redesign cycles available, there is less chance
for technology effectiveness errors to propagate through the fleet.
The 2017 baseline has resulted in more Atkinson technology being
assumed in the 2018 through 2021 model year fleets than really
exists in the market.

    Toyota further stated,

    For compliance modeling of gasoline powertrains, EPA is
extensively relying on the HCR2 classification of Atkinson engine
technology for which the assumed efficacy remains unproven and
highly unlikely as previously explained. NHTSA effectively deploys
only to the HCR1 level of Atkinson engines which better reflects the
state of technology in the fleet today and identifies HCR1D as a
more advanced future pathway that while not cost-effective has a
considerably more reasonable assumed technology effectiveness than
HCR2.\274\
---------------------------------------------------------------------------

    \274\ Toyota, at pp. 3-4.

    The Auto Innovators also provided information and comment on the
---------------------------------------------------------------------------
HCR2 engine map model:

    In the GHG NPRM [86 FR 43726, August 10, 2021], EPA resurrected
highly optimistic effectiveness estimates for future Atkinson cycle
engines based on a speculative engine map, and used the results as
``HCR2'' technology. The use of this technology package can diminish
the integrity of the analysis and distort discussions of
technological feasibility and economic practicability of future
standards. We recommend against the inclusion of this technology
package in the CAFE Model at this time.
    While some organizations have asserted that EPA's 2016
characterization of HCR2 is a reasonable characterization of engines
in the market today, like Toyota's 2.5L on the Camry and RAV4, or
Mazda's 2.5L on the CX-5, history has shown that the HCR2
assumptions used in EPA's analysis significantly and unreasonably
overestimate the real-world fuel saving capability of state-of-the-
art Atkinson engine technology in these applications. The EPA HCR2
engine map assumes engine accessory drive improvements (``IACC'')
and engine friction reduction (``EFR'') have already been used to
the maximum extent possible, so reapplying these technologies again
in the modeling (as the EPA analysis does) incorrectly double counts
the potential effectiveness of these technologies. EPA incorrectly
states that HCR2 technology, as modeled, exists in the fleet and is
widely available for adoption.\275\
---------------------------------------------------------------------------

    \275\ Auto Innovators, at pp. 49-51.

    After review of the comments provided, we continue to believe HCR
engine technology shows promise for future ICE fuel economy
improvements and we continue with testing and validation for the IAV-
generated HCR engine map model family so that those engine map models
can be used in future analyses. However, we also believe that this
specific engine map model presents several problems when considered in
the context of this analysis. First, we believe that the technology
combination modeled by the HCR2 engine map is unlikely to be utilized
in the rulemaking timeframe based on comments received from the
industry leaders in HCR technology application. Second, as illustrated
by the Auto Innovators, this specific engine map model provides an
excessive jump in effectiveness when compared to the other IAV-based
engine map models used in this analysis. As a result, we have decided
to continue to exclude the HCR2 engine map model from our central
analysis. We will continue to expand the HCR engine map model family of
technologies in future analyses. This is consistent with EPA's current
assessment of their own model and choice to exclude the HCR2 engine in
their final rule analysis.\276\
---------------------------------------------------------------------------

    \276\ See U.S. EPA, ``Revised 2023 and Later Model Year Light-
Duty Vehicle GHG Emissions Standards: Regulatory Impact Analysis.''
December 2021. EPA-420-R-21-028. https://nepis.epa.gov/Exe/ZyPDF.cgi?Dockey=P1013ORN.pdf. (Accessed: March 9, 2022)
---------------------------------------------------------------------------

(2) Analogous Engine Effectiveness Improvements and Fuel Economy
Improvement Values
    For some technologies, the effectiveness for applying an
incremental engine technology is determined by using the effectiveness
values for applying the same engine technology to a reasonably similar
base

engine. An example of this can be seen in the determination of the
application of SGDI to the baseline SOHC engine. Currently there is no
engine map model for the SOHC+VVT+SGDI engine configuration. To create
the effectiveness data required as an input to the CAFE Model, first, a
pairwise comparison between technology configurations that included the
DOHC+VVT engine (Eng1) and the DOHC+VVT+SGDI (Eng18) engine was
conducted. Then, the results of that comparison were used to generate a
data set of emulated performance values for adding the SGDI technology
to the SOHC+VVT engine (Eng5b) systems.
    The pairwise comparison is performed by finding the difference in
fuel consumption performance between every technology configuration
using the analogous base technology (e.g., Eng1) and every technology
configuration that only changes to the analogous technology (e.g.,
Eng18). The individual changes in performance between all the
technology configurations are then added to the same technology
configurations that use the new base technology (e.g., Eng5b) to create
a new set of performance values for the new technology (e.g.,
SOHC+VVT+SGDI). Table III-10 shows the engine technologies where
analogous effectiveness values were used.
[GRAPHIC] [TIFF OMITTED] TR02MY22.069

    The agency received a comment about the use of analogous estimation
from ICCT. ICCT stated,

    The modeled benefit of adding cylinder deactivation to
turbocharged and HCR1 vehicles is only about 25 [percent] of the
benefit from adding DEAC or ADEAC to a basic engine. While adding
DEAC to a turbocharged or HCR1 engine has smaller pumping loss
reductions than for base naturally aspirated engines, DEAC still has
significant pumping loss reductions and has the additional benefit
of enabling the engine to operate in a more thermal efficient region
of the engine fuel map. The agencies also failed to provide even the
most basic information supporting their effectiveness estimates for
TURBOD. Further compounding the problem, NHTSA based the
effectiveness of adding DEAC to HCR engines on the TURBOD estimate,
without any further justification.\277\
---------------------------------------------------------------------------

    \277\ ICCT, at pp. 4-5.

    We disagree with ICCT's characterization of the TURBOD engine map
model as ``not having information supporting its creation.'' A
discussion of the creation of the TURBOD engine map model, along with
all the engine map models, is provided in Chapter <IP_ADDRESS> of the TSD.
Furthermore, as discussed in Chapter <IP_ADDRESS>.1 of the TSD, the HCR1D
effectiveness values are based on application of the DEAC technology to
a similar technology model (TURBO1) where there is a reduced pumping
loss benefit. Additionally, commenters did not indicate what
effectiveness values they would consider reasonable or plausible, and
NHTSA has no new data to support the ICCT position. As a result, we
will continue to use the effectiveness values from the NPRM for the
final rule analysis.
    We also developed a static fuel efficiency improvement factor to
simulate applying an engine technology for some technologies where
there is either, no appropriate analogous technology, or there are not
enough data to create a full engine map model. The improvement factors
are developed based on a literature review or confidential business
information (CBI) provided by stakeholders. Table III-11 provides a
summary of the technology effectiveness values simulated using
improvement factors, and the value and rules for how the improvement
factors are applied. Advanced cylinder deactivation (ADEAC, TURBOAD,
DSLIAD), advanced diesel engines (DSLIA) and engine friction reduction
(EFR) are the three technologies modeled using improvement factors.
    The application of the advanced cylinder deactivation is
responsible for three of the five technologies using an improvement
factor in this analysis. The initial review of the advanced cylinder
deactivation technology is based on a technical publication that used a
MY 2010 SOHC VVT basic engine.\278\ Additional information about the
technology effectiveness came from a benchmarking analysis of pre-
production 8-cylinder OHV prototype systems.\279\ However, at the time
of the

analysis no studies of production versions of the technology are
available, and the only available technology effectiveness came from
existing studies, not operational information. Thus, only estimates of
effect can be developed and not a full model of operation. No engine
map model can be developed, and no other technology pairs are
analogous.
---------------------------------------------------------------------------

    \278\ Wilcutts, M., Switkes, J., Shost, M., and Tripathi, A.,
``Design and Benefits of Dynamic Skip Fire Strategies for Cylinder
Deactivated Engines,'' SAE Int. J. Engines 6(1):278-288, 2013,
available at https://doi.org/10.4271/2013-01-0359 (Accessed:
February 17, 2022); Eisazadeh-Far, K. and Younkins, M., ``Fuel
Economy Gains through Dynamic-Skip-Fire in Spark Ignition Engines,''
SAE Technical Paper 2016-01-0672, 2016, available at https://doi.org/10.4271/2016-01-0672. (Accessed: February 17, 2022).
    \279\ EPA, 2018. ``Benchmarking and Characterization of a Full
Continuous Cylinder Deactivation System.'' Presented at the SAE
World Congress, April 10-12, 2018. Retrieved from https://www.regulations.gov/document?D=EPA-HQ-OAR-2018-0283-0029. (Accessed:
February 17, 2022).
---------------------------------------------------------------------------

    To model the effects of advanced cylinder deactivation, an
improvement factor is determined based on the information referenced
above and applied across the engine technologies. The effectiveness
values for naturally aspirated engines are predicted by using full
vehicle simulations of a basic engine with DEAC, SGDI, VVL, and VVT,
and adding 3 percent or 6 percent improvement based on engine cylinder
count: 3 percent for engines with 4 cylinders or less and 6 percent for
all other engines. Effectiveness values for turbocharged engines are
predicted using full vehicle simulations of the TURBOD engine and
adding 1.5 percent or 3 percent improvement based on engine cylinder
count: 1.5 percent for engines with 4 cylinders or less and 3 percent
for all other engines. For diesel engines, effectiveness values are
predicted by using the DSLI effectiveness values and adding 4.5 percent
or 7.5 percent improvement based on vehicle technology class: 4.5
percent improvement is applied to small and medium non-performance
cars, small performance cars, and small non-performance SUVs. 7.5
percent improvement is applied to all other vehicle technology classes.
    The analysis models advanced engine technology application to the
baseline diesel engine by applying an improvement factor to the ADSL
engine technology combinations. A 12.8 percent improvement factor is
applied to the ADSL technology combinations to create the DSLI
technology combinations. The improvement in performance is based on the
application of a combination of low pressure and high pressure EGR,
reduced parasitic loss, advanced friction reduction, incorporation of
highly integrated exhaust catalyst with low temp light off
temperatures, and closed loop combustion
control.280 281 282 283
---------------------------------------------------------------------------

    \280\ 2015 NAS Report, at p. 104.
    \281\ Hatano, J., Fukushima, H., Sasaki, Y., Nishimori, K.,
Tabuchi, T., Ishihara, Y. ``The New 1.6L 2-Stage Turbo Diesel Engine
for HONDA CR-V.'' 24th Aachen Colloquium--Automobile and Engine
Technology 2015.
    \282\ Steinparzer, F., Nefischer, P., Hiemesch, D., Kaufmann,
M., Steinmayr, T. ``The New Six-Cylinder Diesel Engines from the BMW
In-Line Engine Module.'' 24th Aachen Colloquium--Automobile and
Engine Technology 2015.
    \283\ Eder, T., Weller, R., Spengel, C., B[ouml]hm, J., Herwig,
H., Sass, H. Tiessen, J., Knauel, P. ``Launch of the New Engine
Family at Mercedes-Benz.'' 24th Aachen Colloquium--Automobile and
Engine Technology 2015.
---------------------------------------------------------------------------

    As discussed above, the application of the EFR technology does not
simulate the application of a specific technology, but the application
of an array of potential improvements to an engine. All reciprocating
and rotating components in the engine are potential candidates for
friction reduction, and small improvements in several components can
add up to a measurable fuel economy
improvement.284 285 286 287 Because of the incremental
nature of this analysis, a range of 1-2 percent improvement was
identified initially, and narrowed further to a specific 1.39 percent
improvement. The final value is likely representative of a typical
value industry may be able to achieve in future years.
---------------------------------------------------------------------------

    \284\ ``Polyalkylene Glycol (PAG) Based Lubricant for Light- &
Medium-Duty Axles,'' 2017 DOE Annual Merit Review. Ford Motor
Company, Gangopadhyay, A., Ved, C., Jost, N. https://energy.gov/sites/prod/files/2017/06/f34/ft023_gangopadhyay_2017_o.pdf.
    \285\ ``Power-Cylinder Friction Reduction through Coatings,
Surface Finish, and Design,'' 2017 DOE Annual Merit Review. Ford
Motor Company. Gangopadhay, A. Erdemir, A. https://energy.gov/sites/prod/files/2017/06/f34/ft050_gangopadhyay_2017_o.pdf. (Accessed:
February 17, 2022).
    \286\ ``Nissan licenses energy-efficient engine technology to
HELLER,'' https://newsroom.nissan-global.com/releases/170914-01-e?lang=en-US&rss&la=1&downloadUrl=%2Freleases%2F170914-01-e%2Fdownload (accessed: February 17, 2022).
    \287\ ``Infiniti's Brilliantly Downsized V-6 Turbo Shines,''
https://wardsauto.com/engines/infiniti-s-brilliantly-downsized-v-6-turbo-shines (accessed: February 17, 2022).
[GRAPHIC] [TIFF OMITTED] TR02MY22.070

(3) Engine Effectiveness Values
    The effectiveness values for the engine technologies, for all ten
vehicle technology classes, are shown in Figure III-8. Each of the
effectiveness values shown are representative of the improvements seen
for upgrading only the listed engine technology for a given combination
of other technologies. In other words, the range of effectiveness
values seen for each specific technology (e.g., TURBO1) represents the
addition of the TURBO1 technology to every technology combination that
could select the addition of TURBO1. See Table III-12 for several
specific examples. It must be emphasized, the change in fuel
consumption values between entire technology keys are

used,\288\ and not the individual technology effectiveness values.
Using the change between whole technology keys captures the
complementary or non-complementary interactions among technologies.
---------------------------------------------------------------------------

    \288\ Technology key is the unique collection of technologies
that constitutes a specific vehicle, see Section III.C.4.c).
[GRAPHIC] [TIFF OMITTED] TR02MY22.071

    Some of the advanced \289\ engine technologies have values that
indicate seemingly low effectiveness. Investigation of these values
shows the low effectiveness is a result of applying the advanced
engines to existing SHEVP2 architectures. This effect is expected and
illustrates the importance of using the full vehicle modeling to
capture interactions between technologies and capture instances of both
complimentary technologies and non-complimentary technologies. In this
instance, the SHEVP2 powertrain improves fuel economy, in part, by
allowing the engine to spend more time operating at efficient engine
speed and load conditions. This reduces the advantage of adding
advanced engine technologies, which also improve fuel economy, by
broadening the range of speed and load conditions for the engine to
operate at high efficiency. This redundancy in fuel savings mechanism
results in a lower effectiveness when the technologies are added to
each other.
---------------------------------------------------------------------------

    \289\ The full data set we used to generate this example can be
found in the FE_1 Improvements file.

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

[GRAPHIC] [TIFF OMITTED] TR02MY22.072

(e) Engine \290\ Costs
---------------------------------------------------------------------------

    \290\ The box shows the inner quartile range (IQR) of the
effectiveness values and whiskers extend out 1.5 x IQR. The dots
outside this range show effectiveness values outside those
thresholds. The data used to create this figure can be found in the
FE_1 Improvements file.
---------------------------------------------------------------------------

    We consider both cost and effectiveness in the CAFE Model when
selecting any technology changes. As discussed in detail in TSD Chapter
3.1.8, the engine costs we use in this analysis build on estimates from
the 2015 NAS Report, from agency-funded teardown studies, and from work
performed by non-government organizations.\291\
---------------------------------------------------------------------------

    \291\ FEV prepared several cost analysis studies for EPA on
subjects ranging from advanced 8-speed transmissions to belt
alternator starters or start/stop systems. NHTSA contracted
Electricore, EDAG, and Southwest Research for teardown studies
evaluating mass reduction and transmissions. The 2015 NAS Report
also evaluated technology costs developed based on these teardown
studies.
---------------------------------------------------------------------------

    We use the absolute costs of the engine technology in this
analysis, instead of relative costs used prior to the 2020 final rule.
We use absolute costs to ensure the full cost of the IC engine is
removed when electrification technologies are applied, specifically for
transition to BEVs. In this analysis, we model the cost of adopting BEV
technology by first removing the costs associated with IC powertrain
systems, then applying the BEV systems costs. Relative costs can still
be determined through comparison of the absolute costs for the initial
technology combination and the new technology combination.
    As discussed in detail in TSD Chapter 3.1.8, we assigned engine
costs based on the number of cylinders in the engine and whether the
engine is naturally aspirated or turbocharged and downsized. Table III-
13 below shows an example of absolute costs for engine technologies in
2018$. The example costs are shown for a straight 4-cylinder DOHC
engine and V-6-cylinder DOHC engine. The table shows costs declining
across successive years due to the learning rate we applied to each
engine technology. For a full list of all absolute engine costs we used
in the analysis across all model years, see the Technologies file.

[GRAPHIC] [TIFF OMITTED] TR02MY22.073

    We received several comments regarding engine technology costs.
ICCT provided several cost comments for technologies including direct
injection, cool exhaust gas recirculation, cylinder deactivation and
turbo charging, that all took issue with the agency for not using cost
data from a 2015 FEV teardown study.\292\
---------------------------------------------------------------------------

    \292\ FEV 2015--David Blanco-Rodriguez, 2025 Passenger car and
light commercial vehicle powertrain technology analysis. FEV GmbH.
September 2015. https://theicct.org/sites/default/files/publications/PV-LCV-Powertrain-Tech-Analysis_FEV-ICCT_2015.pdf.
(Accessed: February 16, 2022).
---------------------------------------------------------------------------

    As we explained in the 2020 final rule, we do not believe that the
FEV report referenced by ICCT is an appropriate source to use for this
analysis for a few reasons. First, the primary focus of the FEV study
``is the European Market according to the EU6b regulation as well as
the consideration of emissions under both the NEDC and WLTP test
procedures.'' Components designed for use in Europe will have alternate
constraints from parts designed for use in the U.S., such as octane
limits, which can result in different designs and costs. This final
rule analysis specifically considered the U.S. automotive market during
the rulemaking timeframe based on U.S.-specific regulatory test cycles.
Accordingly, the costs reflect incremental technology effectiveness for
achieving improvements as measured through U.S. regulatory test
methods. We discuss these test cycles and methods further in Section
III.C.4.
    Second, FEV did not conduct original teardown studies for this
report, as indicated by project tasks, but rather used engineering
judgement and external studies in assessing incremental costs.\293\ The
FEV report did not provide sources for each individual cost and it is
unclear how costs in many scenarios were developed since no teardowns
were used. Note that for this final rule analysis, we used previously
conducted FEV cost teardown studies and the referenced 2015 NAS costs
that also references FEV teardowns. As a result of this assessment we
are not concluding that FEV as a whole is a source on which NHTSA
should not rely, but we do want to make sure the baseline assumptions
of costing data, and how they are collected, are consistent with the
baseline assumptions of our analysis.
---------------------------------------------------------------------------

    \293\ FEV EU Costs Tasks: ``Definition of reference hardware or
description made by experience of development and design engineers
as well as additional research as base for cost analysis (no
purchase of hardware).''
---------------------------------------------------------------------------

    Finally, the cost for different vehicle classes identified by the
FEV study does not line up with the vehicle classes discussed in the
NPRM and this final rule analysis. FEV stated specifically, ``the
configuration of the vehicles has not been optimized for the [U.S.]
market and may not be representative of this market.'' \294\ We have
discussed the importance of aligning the CAFE vehicle models with the
U.S. market earlier in

Sections III.C.2 and III.C.4. All of these factors make it difficult to
compare directly our estimates and estimates presented in the FEV
report cited by ICCT in their comments.
---------------------------------------------------------------------------

    \294\ Id. at p. 141.
---------------------------------------------------------------------------

    ICCT's comment regarding the cost of the HCR engine technology
costs, unlike the costs discussed above, did not originate with the
2015 FEV report. ICCT stated that ``DMC costs for HCR in the SAFE rule,
which are unchanged in NHTSA's proposed rule, were about $200 more than
in EPA's 2016 TAR. This is a clear case where the agencies appear to
have not used the best available data from EPA.''
    We used the same DMCs established by the 2015 NAS Report for the
Atkinson cycle technologies in both the NPRM analysis and the final
rule analysis. However, because there are many various engine
configurations in the market, we do not use the same fixed costs that
were set for each type of vehicle described in the 2015 NAS Report,
such as pickup and sedan. We have expanded costs by considering the
type of technology in the baseline, like SGDI, and the configuration of
the engine, such as SOHC versus DOHC. In addition, the cost used in the
NPRM also included updated dollar year, learning rate, and RPE in
comparison to the 2016 TAR. Although EPA also used costs from the 2015
NAS Report for the Proposed Determination analysis, they used a
different approach to account for components.
    After review of the provided comments, we continue to rely on the
costs developed from the data provided by NAS and used for the NPRM
analysis. Engine technology costs often exist as a range of values
across manufacturers, and we work to try and find the best
representative value of that range, avoiding either maximum or minimum
values.
Transmission Paths
    For this analysis, we classify all light duty vehicle transmission
technologies into discrete transmission technology paths. We use these
paths to model the most representative characteristics, costs, and
performance of the fuel-economy improving transmissions most likely
available during the rulemaking time frame, MYs 2024-2026.
    In the following sections we discuss how we define transmission
technologies in this analysis, the general technology categories we use
in the CAFE Model, and the transmission technologies' relative
effectiveness and costs. In the following sections we also provide an
overview of how we assign transmission technologies to the baseline
fleet, as well as the adoption features, we apply to the transmission
technologies.
    We only received comments regarding the costs assigned to eCVT
technology for power-split strong hybrid (i.e., SHEVPS) systems. Our
model only uses the eCVT technology as part of the SHEVPS technology
package, and the eCVT is not modeled as a standalone transmission
technology. As a result, we have responded to comments on eCVT costs in
Section III.D.3. For all other transmission technologies, we use the
same NPRM transmission technologies inputs and costs for the final rule
analysis.
(a) Transmission Modeling in the CAFE Model
    We model two categories of transmissions for this analysis:
Automatic and manual. We characterize automatic transmissions as
transmissions that automatically select and shift between transmission
gears for the driver during vehicle operation. We further subdivide
automatic transmissions into four subcategories: Traditional automatic
transmissions (AT), dual clutch transmissions (DCT), continuously
variable transmissions (CVT), and direct drive transmissions (DD).
    We model both the DD transmission and eCVT as part of electrified
powertrain technology packages, and not as independently selectable
technologies. As a result, we do not explicitly include either
technology in the transmission paths, and the technologies are
discussed further in Section III.D.3.
    We employ different levels of high efficiency gearbox (HEG)
technology in the ATs and CVTs. HEG improvements for transmissions
represent incremental advancement in technology that improve
efficiency, such as reduced friction seals, bearings and clutches,
super finishing of gearbox parts, and improved lubrication. These
advancements are aimed at reducing frictional and other parasitic loads
in transmissions, to improve efficiency. We consider three levels of
HEG improvements in this analysis, based on 2015 NAS Report and CBI
data.\295\ We apply HEG efficiency improvements to ATs and CVTs,
because those transmissions inherently have higher friction and
parasitic loads related to hydraulic control systems and greater
component complexity, compared to MTs and DCTs. We note HEG technology
improvements in the transmission technology pathways by increasing
``levels'' of a transmission technology; for example, the baseline 8-
speed automatic transmission is termed ``AT8'', while an AT8 with level
2 HEG technology is ``AT8L2'' and an AT8 with level 3 HEG technology is
``AT8L3.''
---------------------------------------------------------------------------

    \295\ 2015 NAS Report, at p. 191.
---------------------------------------------------------------------------

    AT: Conventional planetary gear automatic transmissions are the
most popular transmission.\296\ ATs typically contain three or four
planetary gear sets that provide the various gear ratios. Gear ratios
are selected by activating solenoids which engage or release multiple
clutches and brakes as needed. ATs are packaged with torque converters,
which provide a fluid coupling between the engine and the driveline and
provide a significant increase in launch torque. When transmitting
torque through this fluid coupling, energy is lost due to the churning
fluid. These losses can be eliminated by engaging the torque convertor
clutch to directly connect the engine and transmission (``lockup'').
For the Draft TAR and 2020 final rule, EPA and DOT surveyed automatic
transmissions in the market to assess trends in gear count and
purported fuel economy improvements.\297\ Based on that survey, and
also EPA's 2021 Automotive Trends Report,\298\ we concluded that
modeling ATs with a range of 5 to 10 gears, with three levels of HEG
technology for this analysis was reasonable.
---------------------------------------------------------------------------

    \296\ 2021 EPA Automotive Trends Report, at pp. 62-66.
    \297\ Draft TAR at 5-50, 5-51; Final Regulatory Impact Analysis
accompanying the 2020 final rule, at 549.
    \298\ 2021 EPA Automotive Trends Report, at pp. 62-66.
---------------------------------------------------------------------------

    CVT: Conventional continuously variable transmissions consist of
two cone-shaped pulleys, connected with a belt or chain. Moving the
pulley halves allows the belt to ride inward or outward radially on
each pulley, effectively changing the speed ratio between the pulleys.
This ratio change is smooth and continuous, unlike the step changes of
other transmission varieties.\299\ We include two types of CVT systems
in the selectable transmission paths, the baseline CVT and a CVT with
HEG technology applied.
---------------------------------------------------------------------------

    \299\ 2015 NAS Report, at p. 171.
---------------------------------------------------------------------------

    DCT: Dual clutch transmissions, like automatic transmissions,
automate shift and launch functions. DCTs use separate clutches for
even-numbered and odd-numbered gears, allowing the next gear needed to
be pre-selected, resulting in faster shifting. The use of multiple
clutches in place of a torque converter results in lower parasitic

losses than ATs.\300\ Because of a history of limited
appeal,301 302 we constrain application of additional DCT
technology to vehicles already using DCT technology, and only model two
types of DCTs in this analysis.
---------------------------------------------------------------------------

    \300\ 2015 NAS Report, at p. 170.
    \301\ 2020 EPA Automotive Trends Report, at p. 57.
    \302\ 2021 NAS Report, at 56.
---------------------------------------------------------------------------

    MT: Manual transmissions are transmissions that require direct
control by the driver to operate the clutch and shift between gears. In
a manual transmission, gear pairs along an output shaft and parallel
layshaft are always engaged. Gears are selected via a shift lever,
operated by the driver. The lever operates synchronizers, which speed
match the output shaft and the selected gear before engaging the gear
with the shaft. During shifting operations (and during idle), a clutch
between the engine and transmission is disengaged to decouple engine
output from the transmission. Automakers today offer a minimal
selection of new vehicles with manual transmissions.\303\ As a result
of reduced market presence, we only include three variants of manual
transmissions in the analysis.
---------------------------------------------------------------------------

    \303\ 2020 EPA Automotive Trends Report, at p. 61.
---------------------------------------------------------------------------

    The transmission model paths used in this analysis are shown in
Figure III-10. Baseline-only technologies (MT5, AT5, AT7L2, AT9L2, and
CVT) are grayed and can only be assigned as initial vehicle
transmission configurations. Further details about transmission path
modeling can be found in TSD Chapter 3.2.
[GRAPHIC] [TIFF OMITTED] TR02MY22.074

(b) Transmission Analysis Fleet Assignments
    The wide variety of transmissions on the market are classified into
discrete transmission technology paths for this analysis. These paths
are used to model the most representative characteristics, costs, and
performance of the fuel economy-improving technologies most likely
available during the rulemaking time frame.
    To generate the analysis fleet, we gather data on transmissions
from manufacturer mid-model year CAFE compliance submissions and
publicly available manufacturer specification sheets. We use the data
to assign transmissions in the analysis fleet and determine which
platforms share transmissions.
    We specify transmission type, number of gears, and high-efficiency
gearbox (HEG) level for the baseline fleet assignment. The number of
gears in the assignments for automatic and manual transmissions usually
match the number of gears listed by the data sources, with some
exceptions. We did not model four-speed transmissions in Autonomie for
this analysis due to their rarity and low likelihood of being used in
the future, so we assigned MY 2020 vehicles with an AT4 or MT4 to an
AT5 or MT5 baseline, respectively. Some dual-clutch transmissions were
also an exception; dual-clutch transmissions with seven gears were
assigned to DCT6.
    For automatic and continuously variable transmissions, the
identification of the most appropriate transmission path model required
additional steps; this is because high-efficiency gearboxes are
considered in the analysis but identifying HEG level from specification
sheets alone was not always straightforward. We conducted a review of
the age of the transmission design, relative performance versus
previous designs, and technologies incorporated and used the
information obtained to assign an HEG level. No automatic transmissions
in the analysis fleet were determined to be at HEG Level 3. In
addition, no six-speed automatic transmissions were assigned HEG Level
2. However, we found all 7-speed, all 9-speed, all 10-speed, and some
8-speed automatic transmissions to be advanced transmissions operating
at HEG Level 2 equivalence. Eight-speed automatic transmissions
developed after MY 2017 are assigned HEG Level 2. All other
transmissions are assigned to their respective transmission's baseline
level. The baseline (HEG level 1) technologies available include AT6,
AT8, and CVT.
    We assigned any vehicle in the analysis fleet with an electric
powertrain a direct drive (DD) transmission. This designation is for
informational purposes; if specified, the transmission will not be
replaced or updated by the model. Similarly, we assigned any power-
split hybrid vehicle

an eCVT transmission. As with the direct drive (DD) transmission, this
designation is for informational purposes.
    In addition to technology type, gear count, and HEG level,
transmissions are characterized in the analysis fleet by drive type and
vehicle architecture. Drive types considered in the analysis include
front-, rear-, all-, and four-wheel drive. Our definition of drive
types in the analysis does not always align with manufacturers' drive
type designations; see the end of this subsection for further
discussion. These characteristics, supplemented by information such as
gear ratios and production locations, showed that manufacturers use
transmissions that are the same or similar on multiple vehicle models.
Manufacturers have told the agency they do this to control component
complexity and associated costs for development, manufacturing,
assembly, and service. If multiple vehicle models share technology
type, gear count, drive configuration, internal gear rations, and
production location, the transmissions are treated as a single group
for the analysis. Vehicles in the analysis fleet with the same
transmission configuration adopt additional fuel-saving transmission
technology together, as described in Section III.C.2.a).
    Shared transmissions are designated and tracked in the CAFE Model
input files using transmission codes. Transmission codes are six-digit
numbers that are assigned to each transmission and encode information
about them. This information includes the manufacturer, drive
configuration, transmission type, and number of gears. TSD Chapter
3.2.4 includes more information on the transmission codes designated in
the analysis fleet.
    We assigned different transmission codes to variants of a
transmission that may have appeared to be similar based on the
characteristics considered in the analysis but are not mechanically
identical. We distinguish among transmission variants by comparing
their internal gear ratios and production locations. For example,
several Ford nameplates carry a rear-wheel drive, 10-speed automatic
transmission. These nameplates comprise a wide variety of body styles
and use cases, and so we assigned different transmission codes to these
different nameplates. Because we assigned different transmission codes,
we are not treating them as ``shared'' for the purposes of the analysis
and the transmission models have the opportunity to adopt transmission
technologies independently.
    Note that when we determine the drive type of a transmission, the
assignment of all-wheel drive (AWD) versus four-wheel drive (4WD) is
determined by vehicle architecture. Our assignment does not necessarily
match the drive type used by the manufacturer in specification sheets
and marketing materials. We assigned vehicles with a powertrain capable
of providing power to all wheels and a transverse engine (front-wheel
drive architecture), AWD. We assigned vehicles with power to all four
wheels and a longitudinal engine (rear-wheel drive architecture), 4WD.
(c) Transmission Adoption Features
    We designated transmission technology pathways to prevent ``branch
hopping''--changes in transmission type that would correspond to
significant changes in transmission architecture--for vehicles that are
relatively advanced on a given pathway. The CAFE Model prevents
``branch hopping'' recognizing that stranded capital associated with
moving from one transmission architecture to another is relevant and
not entirely feasible when making technology selections. Stranded
capital is discussed in Section III.C.6. For example, a vehicle with an
automatic transmission with more than five gears cannot adopt a dual-
clutch transmission. For a more detailed discussion of path logic
applied in the analysis, including technology supersession logic and
technology mutual exclusivity logic, please see CAFE Model
Documentation S4.5 Technology Constraints (Supersession and Mutual
Exclusivity).
    Some technologies modeled in the analysis are not yet in
production, and therefore are not assigned in the baseline fleet.
Nonetheless, we made these technologies available for future adoption
because, they are projected to be available in the analysis timeframe.
For instance, we did not observe an AT10L3 in the baseline fleet, but
it is plausible that manufacturers that employ AT10L2 technology may
improve the efficiency of those AT10L2s in the rulemaking timeframe.
    In the following sections we discuss specific adoption features
applied to each type of transmission technology.
    When we adopt electrification technologies, the transmissions
associated with those technologies will supersede the existing
transmission on a vehicle. We superseded the transmission technology
when P2 hybrids, plug-in hybrids, or battery electric vehicle
technologies are applied. For more information, see Section III.D.3.c).
    We preclude adoption of other transmission types once a platform
progresses past an AT6 on the automatic transmission path. We use this
restriction to avoid the significant level of stranded capital loss
that could result from adopting a completely different transmission
type shortly after adopting an advanced transmission, which would occur
if a different transmission type were adopted after AT6 in the
rulemaking timeframe.
    We do not allow vehicles that do not start with AT7L2 or AT9L2
transmissions to adopt those technologies during simulation. We
observed that MY 2020 vehicles with those technologies were primarily
luxury performance vehicles and concluded that other vehicles would
likely not adopt those technologies. We concluded that this was also a
reasonable assumption for the analysis fleet because vehicles that have
moved to more advanced automatic transmissions have overwhelmingly
moved to 8-speed and 10-speed transmissions.\304\
---------------------------------------------------------------------------

    \304\ 2020 EPA Automotive Trends Report, at 64, figure 4.18.
---------------------------------------------------------------------------

    We limited CVT adoption by technology path logic. We do not allow
CVTs to be adopted by vehicles that do not originate with a CVT or by
vehicles with multispeed transmissions beyond AT6 in the baseline
fleet. Once on the CVT path, we only allow the platform to apply
improved CVT technologies. We restrict application of CVT technology on
larger vehicles because of the higher torque (load) demands of those
vehicles and CVT torque limitations based on durability constraints.
Additionally, we use this restriction to avoid the loss of significant
level of stranded capital.
    We allow vehicles in the baseline fleet that have DCTs to apply an
improved DCT and allows vehicles with an AT5 to consider DCTs.
Drivability and durability issues with some DCTs have resulted in a low
relative adoption rate over the last decade; this is also broadly
consistent with manufacturers' technology choices.\305\
---------------------------------------------------------------------------

    \305\ Ibid.
---------------------------------------------------------------------------

    We only allow vehicles with MTs to adopt more advanced manual
transmissions for this analysis, because other transmission types do
not provide a similar driver experience (utility). We do not allow
vehicles with MTs to adopt ATs, CVTs, or DCT technologies under any
circumstance. We do not allow vehicles with other transmissions to
adopt MTs in recognition of the low

customer demand for manual transmissions.\306\
---------------------------------------------------------------------------

    \306\ Ibid.
---------------------------------------------------------------------------

 (d) Transmission Effectiveness Modeling
    For this analysis, we use the Autonomie full vehicle simulation
tool to model the interaction between transmissions and the full
vehicle system to improve fuel economy, and how changes to the
transmission subsystem influence the performance of the full vehicle
system. Our full vehicle simulation approach clearly defines the
contribution of individual transmission technologies and separates
those contributions from other technologies in the full vehicle system.
Our modeling approach follows the recommendations of the 2015 NAS
Report to use full vehicle modeling supported by application of
collected improvements at the sub-model level.\307\ See TSD Chapter
3.2.4 for more details on transmission modeling inputs and results.
---------------------------------------------------------------------------

    \307\ 2015 NAS Report, at p. 292.
---------------------------------------------------------------------------

    The only technology effectiveness results that were not directly
calculated using the Autonomie simulation results were for the AT6L2.
We determined the model for this specific technology was inconsistent
with the other transmission models and overpredicted effectiveness
results. Evaluation of the AT6L2 transmission model revealed an
overestimated efficiency map was developed for the AT6L2 model. The
high level of efficiency assigned to the transmission surpassed
benchmarked advanced transmissions.\308\ To address the issue, we
replaced the effectiveness values of the AT6L2 model. We replaced the
effectiveness for the AT6L2 technology with analogous effectiveness
values from the AT7L2 transmission model. For additional discussion on
how analogous effectiveness values are determined please see Section
III.D.1.d)(2).
---------------------------------------------------------------------------

    \308\ Autonomie model documentation, Chapter 5.3.4, Transmission
Performance Data.
---------------------------------------------------------------------------

    The effectiveness values for the transmission technologies, for all
ten vehicle technology classes, are shown in Figure III-11. Each of the
effectiveness values shown is representative of the improvements seen
for upgrading only the listed transmission technology for a given
combination of other technologies. In other words, the range of
effectiveness values we show for each specific technology, e.g.,
AT10L3, represents the addition of the AT10L3 technology to every
technology combination that could select the addition of AT10L3. We
must emphasize that the graph shows the change in fuel consumption
values between entire technology keys,\309\ and not the individual
technology effectiveness values. Using the change between whole
technology keys captures the complementary or non-complementary
interactions among technologies. In the graph, the box shows the inner
quartile range (IQR) of the effectiveness values and whiskers extend
out 1.5 x IQR. The dots outside of the whiskers show values for
effectiveness that are outside these bounds.
---------------------------------------------------------------------------

    \309\ Technology key is the unique collection of technologies
that constitutes a specific vehicle, see Section III.C.4.c).
---------------------------------------------------------------------------

BILLING CODE 4510-59-P
[GRAPHIC] [TIFF OMITTED] TR02MY22.075

    We also  want to note the effectiveness for the MT5, AT5, eCVT and
DD technologies are not shown. The DD and eCVT do not have standalone
effectiveness values because they are only implemented as part of
electrified powertrains. The MT5 and AT5 also have no effectiveness
values because both technologies are baseline technologies against
which all other technologies are compared.
---------------------------------------------------------------------------

    \310\ The data used to create this figure can be found the FE_1
Improvements file.
---------------------------------------------------------------------------

 (e) Transmission Costs
    We use transmission costs drawn from several sources, including the
2015 NAS Report and NAS-cited studies for this analysis. TSD Chapter
3.2.7 provides a detailed description of the cost sources used for each
transmission technology. In Table III-14 we show an example of absolute
costs for transmission technologies in 2018$ across select model years,
which demonstrates how we applied cost learning to the transmission
technologies over time. Note, because transmission hardware is often
shared across vehicle classes, transmission costs are the same for all
vehicle classes. For a full list of all absolute transmission costs
used in the analysis across all model years, see the Technologies file.
[GRAPHIC] [TIFF OMITTED] TR02MY22.076

 3. Electrification Paths
    The electric paths include a large set of technologies that share
the common element of using electrical power for certain vehicle
functions that were traditionally powered mechanically by IC engines.
Electrification technologies thus can range from electrification of
specific accessories (for example, electric power steering to reduce
engine loads by eliminating parasitic losses) to electrification of the
entire powertrain (as in the case of a battery electric vehicle).
    The following subsections discuss how we define each
electrification technology in the CAFE Model and the electrification
pathways down which a vehicle can travel in the compliance simulation.
The subsections also discuss how we assigned electrified vehicle
technologies to vehicles in the analysis fleet, any limitations on
electrification technology adoption, and the specific effectiveness and
cost assumptions that we use in the Autonomie and CAFE Model analysis.
    We received many comments on electrification technologies, and
specifically on technology costs. Commenters were generally supportive
of our use of Argonne's BatPaC battery cost model to determine costs of
batteries for different electrified powertrains.\311\ In contrast, we
received several comments indicating that we overstated the cost for
hybrid vehicles and batteries,\312\ in particular due to non-battery
electrification component costs. These comments and our approach to
addressing them for this final rule are discussed in the following
sections.
---------------------------------------------------------------------------

    \311\ Auto Innovators, Docket No. NHTSA-2021-0053-0021, at 55;
Kia, Docket No. NHTSA-2021-0053-1525, at p. 5.
    \312\ Tesla, Inc. (Tesla), Docket No. NHTSA-2021-0053-1480, at
9-10; Toyota, Docket No. NHTSA-2021-0053-1568, at 7; ICCT, Docket
No. NHTSA-2021-0053-1581, at p. 10.
---------------------------------------------------------------------------

    Electrification technologies are a complex set of systems that each
manufacturer individually optimizes based on cost, performance,
reliability, durability, customer acceptance and other metrics. We
attempted to capture these complexities to provide a reasonable
assessment of the costs and

benefits of more stringent fuel economy standards. We expect that there
will be future opportunities to improve upon this work as more
substantiated data on electrification technologies becomes available.
(a) Electrification Modeling in the CAFE Model
    The CAFE Model defines the technology pathway for each type of
electrification grouping in a logical progression. Whenever the CAFE
Model converts a vehicle model to one of the available electrified
systems, both effectiveness and costs are updated according to the
specific components' modeling algorithms. Additionally, all
technologies on the electrification paths are mutually exclusive and
are evaluated in parallel. For example, the model may evaluate PHEV20
technology prior to having to apply SS12V or strong hybrid technology.
The specific set of algorithms and rules are discussed further in the
sections below, and more detailed discussions are included in the CAFE
Model Documentation. The specifications for each electrification
technology that we include in the analysis is discussed below.
    The technologies that we include on the three vehicle-level paths
pertaining to the electrification and electric improvements defined
within the modeling system are illustrated in Figure III-12. As shown
in the Electrification path, the baseline-only CONV technology is
grayed out. This technology is used to denote whether a vehicle comes
in with a conventional powertrain (i.e., a vehicle that does not
include any level of hybridization) and to allow the model to properly
map to the Autonomie vehicle simulation database results. If multiple
technologies from different pathways come together on single technology
set, then those previous technology pathways are disabled. This avoids
unrealistic adoption of legacy technologies as the simulation
progresses from model year to model year. For example, in the Figure
III-12 PHEVs converge on to BEVs then all the PHEVs are disabled from
adoption.
[GRAPHIC] [TIFF OMITTED] TR02MY22.077

    SS12V: 12-volt stop-start (SS12V), sometimes referred to as start-
stop, idle-stop, or a 12-volt micro hybrid system, is the most basic
hybrid system that facilitates idle-stop capability. In this system,
the integrated starter generator is coupled to the internal combustion
(IC) engine. When the vehicle comes to an idle-stop the IC engine
completely shuts off, and, with the help of the 12-volt battery, the
engine cranks and starts again in response to throttle to move the
vehicle, application or release of the brake pedal to move the vehicle.
The 12-volt battery used for the start-stop system is an improved unit
compared to a traditional 12-volt battery, and is capable of higher
power, increased life cycle, and capable of minimizing voltage drop on
restart. This technology is beneficial to reduce fuel consumption and
emissions when the vehicle frequently stops, such as in city driving
conditions or in stop and go traffic. SS12V can be applied to all
vehicle technology classes. As discussed further below, for this final
rule analysis we lowered the cost of the battery used in the SS12V
system to reflect a more widely utilized SS12V battery chemistry.
    Next, mild and strong hybrid systems, discussed in the following
paragraphs, can be classified based on the location of the electric
motor in the system. Depending on the location of the electric machine,
the hybrid technologies are classified as follows:
     P0: Motor located at the primary side of the engine,
     P1: Motor located at the flywheel side of the engine,
     P2: Motor located between engine and transmission,
     P3: Motor located at the transmission output, and
     P4: Motor located on the axle.
    BISG: The belt integrated starter generator, sometimes referred to
as a mild hybrid system or P0 hybrid, provides idle-stop capability and
uses a higher voltage battery with increased energy capacity over
conventional automotive batteries. These higher voltages allow the use
of a smaller, more powerful, and efficient electric motor/generator to
replace the standard alternator. In BISG systems, the motor/generator
is coupled to the engine via belt (similar to a standard alternator).
In addition, these motor/generators can assist vehicle braking and
recover braking energy while the vehicle slows down (regenerative
braking) and in turn can propel the vehicle at the beginning of launch,
allowing the engine to be restarted later. Some limited electric assist
is also provided during acceleration to improve engine efficiency. Like
micro hybrids, BISG can be applied to all vehicles in the analysis
except for Engine 26a (VCR). We assume all mild hybrids are fixed
battery capacity 48-volt systems with engine belt-driven motor/
generators.
    ICCT commented that we should consider another type of mild hybrid
system that has a higher power output, which leads to an increased
efficiency compared to the 48V mild hybrid assumed in the NPRM
analysis. The increased benefit from this higher power output mild
hybrids is due to its placement in the powertrain in P1 and P2
positions rather than P0.313 314
---------------------------------------------------------------------------

    \313\ ICCT, at p. 2.
    \314\ Autonomie assumes a P0 position for mild hybrid 48-volt
systems.
---------------------------------------------------------------------------

    We agree with ICCT that mild hybrids in configurations other than
the P0 position offer higher improvements compared to mild hybrids
configured in the P0 position. However, this inherently increases the
cost of the system and makes the system less cost effective compared to
traditional strong hybrids for a few reasons. First, like a mild hybrid
CISG system,\315\ non-P0 mild hybrid architecture requires significant
changes to the area of the powertrain where the electric machine
components are installed compared to P0 BISG systems. Second, these
system's higher power output will also require a higher battery pack
capacity, which could also increase costs. Separately, no manufacturer
has indicated that they will adopt this type of mild hybrid
configuration in the rulemaking time frame. For MYs 2024-2026, the CAFE
Model estimates that a significant penetration of strong hybrids and
plug-in hybrids is required to meet the analyzed alternatives. Similar
to what we observed in past rulemakings with the CISG system, the non-
P0 mild hybrid is not a cost-effective way for manufacturers to meet
standards in the rulemaking time frame. Accordingly, we did not add an
additional mild hybrid technology for this final rule.
---------------------------------------------------------------------------

    \315\ We discuss challenges with CISG mild hybrids, a system
that is similar to the P2 hybrid system, further in TSD Chapter
<IP_ADDRESS>.
---------------------------------------------------------------------------

    SHEVP2/SHEVPS: A strong hybrid vehicle is a vehicle that combines
two or more propulsion systems, where one uses gasoline (or diesel),
and the other captures energy from the vehicle during deceleration or
braking, or from the engine and stores that energy for later used by
the vehicle. This analysis evaluated the following strong hybrid
systems: hybrids with P2 parallel drivetrain architectures (SHEVP2),
and hybrids with power-split architectures (SHEVPS). Both strong hybrid
types provide start-stop or idle-stop functionality, regenerative
braking capability, and vehicle launch assist. A SHEVPS has a higher
potential for fuel economy improvement than a SHEVP2, although it costs
more and has a lower power density.\316\
---------------------------------------------------------------------------

    \316\ Kapadia, J., Kok, D., Jennings, M., Kuang, M. et al.,
``Powersplit or Parallel--Selecting the Right Hybrid Architecture,''
SAE Int. J. Alt. Power. 6(1):2017, doi:10.4271/2017-01-1154.
---------------------------------------------------------------------------

    P2 parallel hybrids (SHEVP2) are a type of hybrid vehicle that use
a transmission-integrated electric motor placed between the engine and
a gearbox or CVT, with a clutch that allows decoupling of the motor/
transmission from the engine. Disengaging the clutch allows all-
electric operation and more efficient brake-energy recovery. Engaging
the clutch allows coupling of the engine and electric motor and, when
combined with a transmission, reduces gear-train losses relative to
power-split or 2-mode hybrid systems. P2 hybrid systems typically rely
on the internal combustion engine to deliver high, sustained power
levels. Electric-only mode is used when power demands are low or
moderate.
    An important feature of the SHEVP2 system is that it can be applied
in conjunction with most engine technologies. Accordingly, once a
vehicle is converted to a SHEVP2 powertrain in the compliance
simulation, the CAFE Model allows the vehicle to adopt the conventional
engine technology that is most cost effective, regardless of relative
location of the existing engine on the engine technology path. This
means a vehicle could adopt a lower technology engine when the CAFE
Model converts it to a SHEVP2 strong hybrid. For example, a vehicle in
the analysis fleet that starts with a TURBO2 engine could adopt a
TURBO1 engine with the SHEVP2 system, if that TURBO1 engine allows the
vehicle to meet fuel economy standards more cost effectively.
    The power-split hybrid (SHEVPS) is a more advanced electrified
system than SHEVP2 hybrid. The SHEVPS electric drive replaces the
traditional transmission with a single planetary gear set (the power-
split device) and a motor/generator.\317\
---------------------------------------------------------------------------

    \317\ For more discussion of SHEVPS operation and
characteristics, see TSD Section 3.3.
---------------------------------------------------------------------------

    Table III-15 below shows the configuration of conventional engines
and transmissions used with strong hybrids for this analysis. The
SHEVPS powertrain configuration is paired with a planetary transmission
(eCVT) and Atkinson engine (Eng26). This configuration is designed to
maximize efficiency at the cost of reduced towing

capability and real-world acceleration performance.\318\ In contrast,
SHEVP2 powertrains are paired with an advanced 8-speed automatic
transmission (AT8L2) and can be paired with most conventional
engines.\319\
---------------------------------------------------------------------------

    \318\ Kapadia, J., D, Kok, M. Jennings, M. Kuang, B. Masterson,
R. Isaacs, A. Dona. 2017. Powersplit or Parallel--Selecting the
Right Hybrid Architecture. SAE International Journal of Alternative
Powertrains 6 (1): 68-76. https://doi.org/10.4271/2017-01-1154
(accessed: Feb. 11, 2022).
    \319\ We did not model SHEVP2s with VTGe (Eng23c) and VCR
(Eng26a).
[GRAPHIC] [TIFF OMITTED] TR02MY22.078

    PHEV: Plug-in hybrid  electric vehicles are hybrid electric
vehicles with the means to charge their battery packs from an outside
source of electricity (usually the electric grid). These vehicles have
larger battery packs than strong HEVs with more energy storage and a
greater capability to be discharged than other non-plug-in hybrid
electric vehicles. PHEVs also generally use a control system that
allows the battery pack to be substantially depleted under electric-
only or blended mechanical/electric operation and batteries that can be
cycled in charge-sustaining operation at a lower state of charge than
non-plug-in hybrid electric vehicles. These vehicles generally have a
greater all-electric range than typical strong HEVs. Depending on how
these vehicles are operated, they can use electricity exclusively,
operate like a conventional hybrid, or operate in some combination of
these two modes.
---------------------------------------------------------------------------

    \320\ Twenty-one different engines are evaluated with SHEVP2
hybrid architecture: Engine 01, 02, 03, 04, 5b, 6a, 7a, 8a, 12, 12-
DEAC, 13, 14, 17, 18, 19, 20, 21, 22b, 23b, 24, 24-Deac. See Section
III.D.1 for these engine specifications.
---------------------------------------------------------------------------

    There are four PHEV architectures included in this analysis that
reflect combinations of two levels of all-electric range (AER) and two
engine types. We use 20 miles AER and 50 miles AER to reasonably span
the various PHEV AERs in the market, and their effectiveness and cost.
We use an Atkinson engine and a turbocharged downsized engine to span
the variety of engines available in the market.
    PHEV20/PHEV20H and PHEV50/PHEV50H are essentially a SHEVPS with a
larger battery and the ability to drive with the engine turned off. In
the CAFE Model, the designation ``H'' in PHEVxH could represent another
type of engine configuration, but for this analysis we use the same
effectiveness values as PHEV20 and PHEV50 to represent PHEV20H and
PHEV50H, respectively. The PHEV20/PHEV20H represents a ``blended-type''
plug-in hybrid that can operate in all-electric (engine off) mode only
at light loads and low speeds, and must blend electric motor and engine
power together to propel the vehicle at medium or high loads and
speeds. The PHEV50/PHEV50H represents an extended range electric
vehicle (EREV) that can travel in all-electric mode even at higher
speeds and loads. Engine sizing, batteries, and motors for these PHEVs
are discussed further in Section III.D.3.d).
    PHEV20T and PHEV50T are 20 mile and 50 mile AER vehicles based on
the SHEVP2 engine architecture. The PHEV versions of these
architectures include larger batteries and motors to meet performance
metrics in charge sustaining mode at higher speeds and loads as well as
similar performance and range in all electric mode in city driving and
at higher speeds and loads. For this analysis, the CAFE Model considers
these PHEVs to have an advanced 8-speed automatic transmission (AT8L2)
and TURBO1 (Eng12) in the powertrain configuration. Further discussion
of engine sizing, batteries, and motors for these PHEVs is discussed in
Section III.D.3.d).
    Table III-16 shows the different PHEV configurations used in this
analysis.

[GRAPHIC] [TIFF OMITTED] TR02MY22.079

    BEV: Battery electric vehicles are equipped with all-electric drive
systems powered by energy-optimized batteries charged primarily by
electricity from the grid. BEVs do not have a combustion engine or
traditional transmission. Instead, BEVs rely on all electric
powertrains with a single speed gear reduction in place of an advanced
transmission. Battery electric vehicle range varies by vehicle and
battery pack size.
    We simulate BEVs with ranges of 200, 300, 400 and 500 miles in the
CAFE Model. BEV range is measured pursuant to EPA test procedures and
guidance.\321\ The CAFE Model assumes a BEV direct drive transmission
is unique to each vehicle (i.e., the transmissions are not shared by
any other vehicle) and that no further improvements to the transmission
are available.
---------------------------------------------------------------------------

    \321\ BEV electric ranges are determined per EPA guidance
Document. ``EPA Test Procedure for Electric Vehicles and Plug-in
Hybrids.'' https://fueleconomy.gov/feg/pdfs/EPA%20test%20procedure%20for%20EVs-PHEVs-11-14-2017.pdf. November
14, 2017. (Accessed: May 3, 2021)
---------------------------------------------------------------------------

    An important note about the BEVs offered in this analysis is that
the CAFE Model does not account for vehicle range when considering
additional BEV technology adoption. That is, the CAFE Model does not
have an incentive to build BEV 300, 400, and 500s, because the BEV200
is just as efficient as those vehicles and counts the same toward
compliance, but at a significantly lower cost because of the smaller
battery.\322\ While manufacturers have been building 200-mile range
BEVs, those vehicles have generally been passenger cars. Manufacturers
have told us that greater range is important for meeting the needs of
broader range of consumers and to increase consumer demand. More
recently, there has been a trend towards manufacturers building higher
range BEVs in the market, and manufacturers building CUV/SUV and pickup
truck BEVs.\323\ To simulate the potential relationship of BEV range to
consumer demand, we have included several adoption features for BEVs.
These are discussed further in Section III.D.3.c).
---------------------------------------------------------------------------

    \322\ See section III.D.3.d Electrification Effectiveness
Modeling for effectiveness of different rage BEVs.
    \323\ 2021 EPA Automotive Trends Report, at p. 58.
---------------------------------------------------------------------------

    FCEV: Fuel cell electric vehicles are equipped with an all-electric
drivetrain, but unlike BEVs, FCEVs do not solely rely on batteries;
rather, electricity to run the FCEV electric motor is mainly generated
by an onboard fuel cell system. FCEV architectures are similar to
series hybrids,\324\ but with the engine and generator replaced by a
fuel cell. Commercially available FCEVs consume hydrogen to generate
electricity for the fuel cell system, with most automakers using high
pressure gaseous hydrogen storage tanks. FCEVs are currently produced
in limited numbers and are available in limited geographic areas where
hydrogen refueling stations are accessible. For reference, in MY 2020,
only four FCEV models were offered for sale, and since 2014 only 12,081
FCEVs have been sold.325 326 327
---------------------------------------------------------------------------

    \324\ Series hybrid architecture is a strong hybrid that has the
engine, electric motor and transmission in series. The engine in a
series hybrid drives a generator that charges the battery.
    \325\ Argonne National Laboratory, ``Light Duty Electric Drive
Vehicles Monthly Sales Update.'' Energy Systems Division, https://www.anl.gov/es/light-duty-electric-drive-vehicles-monthly-sales-updates. (Accessed: Dec. 15, 2021)
    \326\ See the MY 2020 Market Data file. The four vehicles are
the Honda Clarity, Hyundai Nexo and Nexo Blue, and Toyota Mirai.
    \327\ These are majority leased vehicles that are returned back
to the manufacturer rather than resold as a used vehicle.
---------------------------------------------------------------------------

    For this analysis, the CAFE Model simulates a FCEV with a range of
320 miles. Any powertrain type can adopt a FCEV powertrain; however, to
account for limited market penetration and unlikely increased adoption
in the rulemaking timeframe, technology phase in caps are used to
control how many FCEVs a manufacturer can build. The details of this
concept are further discussed in Section III.D.3.c).
(b) Electrification Analysis Fleet Assignments
    We use electrification technologies assigned in the baseline fleet
as the starting point for regulatory analysis. These assignments are
based on manufacturer-submitted CAFE compliance information, publicly
available technical specifications, marketing brochures, articles from
reputable media outlets, and data from Wards Intelligence.\328\
---------------------------------------------------------------------------

    \328\ ``U.S. Car and Light Truck Specifications and Prices, '20
Model Year.'' Wards Intelligence, 3 Aug. 2020,
wardsintelligence.informa.com/WI964244/US-Car-and-Light-Truck-Specifications-and-Prices-20-Model-Year (accessed: Feb. 11, 2022).
---------------------------------------------------------------------------

    Table III-17 gives the penetration rates of electrification
technologies eligible to be assigned in the baseline fleet. Over half
of the fleet had some level of electrification, with the vast majority
of these being micro hybrids. PHEVs represented 0.5 percent of the MY
2020 baseline fleet. BEVs represented less than 2 percent of MY 2020
baseline fleet; BEV300 was the most common BEV technology, while no
BEV500s were observed.

[GRAPHIC] [TIFF OMITTED] TR02MY22.080

BILLING CODE 4910-59-C
    Micro and mild hybrids refer to the presence of SS12V and BISG,
respectively. The data sources discussed above are used to identify the
presence of these technologies on vehicles in the fleet. Vehicles are
assigned one of these technologies only if its presence can be
confirmed with manufacturer brochures or technical specifications.
    Strong hybrid technologies include SHEVPS and SHEVP2. Note that
P2HCR0, P2HCR1, P2HCR1D, and P2HCR2 are not assigned in the fleet and
are only available to be applied by the model. When possible,
manufacturer specifications are used to identify the strong hybrid
architecture type. In the absence of more sophisticated information,
hybrid architecture is determined by number of motors. Hybrids with one
electric motor are assigned P2, and those with two motors are assigned
PS. We sought comment in the NPRM on additional ways the agency could
perform initial hybrid assignments based on publicly available
information or technical publications. We did not receive any
substantive comments regarding baseline fleet strong hybrid
assignments. Accordingly, this final rule analysis uses the same
approach to assigning SHEVPS and SHEVP2 in the baseline fleet.
    Plug-in hybrid technologies PHEV20/20T and PHEV50/50T are assigned
in the baseline fleet. PHEV20H and PHEV50H are not assigned in the
fleet and are only available to be applied by the model. Vehicles with
an electric-only range of 40 miles or less are assigned PHEV20;
vehicles with a range above 40 miles are assigned PHEV50. They are
respectively assigned PHEV20T/50T if the engine is turbocharged (i.e.,
if it would qualify for one of technologies on the turbo engine
technology pathway). We also calculate baseline fuel economy values for
PHEV technologies as part of the PHEV analysis fleet assignments; that
process is described in detail in TSD Chapter 3.3.2.
    Battery electric vehicle and fuel cell technologies include BEV200/
300/400/500 and FCEV with a 320-mile range. The BEV technologies are
assigned to vehicles based on range thresholds that best account for
vehicles' existing range capabilities while allowing room for the model
to potentially apply more advanced electrification technologies.
Vehicles with all-electric powertrains that use hydrogen fuel are
assigned FCEV.
    For more detail about the electrification analysis fleet assignment
process, see TSD Chapter 3.3.2.
(c) Electrification Adoption Features
    Multiple types of adoption features apply to the electrification
technologies. The hybrid/electric technology path logic dictates how
different vehicle types can adopt different levels of electrification
technology. Broadly speaking, more advanced levels of hybridization or
electrification supersede all prior levels, with certain technologies
within each level being mutually exclusive.
    As discussed further below, SKIP logic--restrictions on the
adoption of certain technologies--apply to plug-in (PHEV) and strong
hybrid vehicles (SHEV). Some technologies on these pathways are
``skipped'' if a vehicle is high performance, requires high towing
capabilities as a pickup truck, or belongs to certain manufacturers who
have demonstrated that their future product plans will more than likely
not include the technology. The specific criteria for SKIP logic for
each applicable electrification technology is expanded on later in this
section.
    This section also discusses the supersession of engines and
transmissions on vehicles that adopt SHEV or PHEV powertrains. To
manage the complexity of the analysis, these types of hybrid
powertrains are modeled with several specific engines and
transmissions, rather than in multiple configurations. Therefore, the
cost and effectiveness values SHEV and PHEV technologies consider these
specific engines and transmissions.
    Finally, phase-in caps limit the adoption rates of battery electric
(BEV) and fuel cell electric vehicles (FCEV). We set the phase-in caps
to account for current market share, scalability, and reasonable
consumer adoption rates of each technology. TSD Chapter 3.3.3 discusses
the electrification phase-in caps and the reasoning behind them in
detail.

    The only adoption feature applicable to micro and mild hybrid
technologies is path logic. The pathway consists of a linear
progression starting with a conventional powertrain with no
electrification at all, which is superseded by SS12V, which in turn is
superseded by BISG. Vehicles can only adopt micro and mild hybrid
technology if the vehicle does not already have a more advanced level
of electrification.
    The adoption features that apply to strong hybrid technologies
include path logic, powertrain substitution, and vehicle class
restrictions. Per the defined technology pathways, SHEVPS, SHEVP2, and
the P2HCR technologies are considered mutually exclusive. In other
words, when the model applies one of these technologies, the others are
immediately disabled from future application. However, all vehicles on
the strong hybrid pathways can still advance to one or more of the
plug-in hybrid technologies.
    When the model applies any strong hybrid technology to a vehicle,
the transmission technology on the vehicle is superseded. Regardless of
the transmission originally present, P2 hybrids adopt an 8-speed
automatic transmission (AT8L2), and PS hybrids adopt an electronic
continuously variable transmission (eCVT).
    When the model applies SHEVP2 technology, the model can consider
various engine options to pair with the SHEVP2 architecture according
to existing engine path constraints, considering relative cost
effectiveness. For SHEVPS technology, the existing engine is replaced
with Eng26, which is a full Atkinson cycle engine.
    SKIP logic is also used to constrain adoption for SHEVPS, P2HCR0,
P2HCR1, and P2HCR1D. These technologies are ``skipped'' for vehicles
with engines \329\ that met one of the following conditions:
---------------------------------------------------------------------------

    \329\ This refers to the engine assigned to the vehicle in the
2020 baseline fleet.
---------------------------------------------------------------------------

    The engine belongs to an excluded manufacturer; \330\
---------------------------------------------------------------------------

    \330\ Excluded manufacturers included BMW, Daimler, and Jaguar
Land Rover.
---------------------------------------------------------------------------

    The engine belongs to a pickup truck (i.e., the engine is on a
vehicle assigned the ``pickup'' body style);
    The engine's peak horsepower is more than 405 HP; or if
    The engine is on a non-pickup vehicle but is shared with a pickup.
    No SKIP logic is applied to SHEVP2, however P2HCR2 is not used in
this analysis, as discussed further in Section III.D.1.
    The reasons for these conditions are similar to those applied to
HCR engine technologies, discussed in more detail above. In the real
world, pickups and performance vehicles with certain powertrain
configurations cannot adopt the technologies listed above and maintain
vehicle performance without redesigning the entire powertrain. SKIP
logic is put in place to prevent the model from pursuing compliance
pathways that are ultimately unrealistic.
    Auto Innovators in their comments for the NPRM, also to the 2018
NPRM, discussed issues with HCR technologies.\331\ Ford had similarly
provided comments in opposition of high dependency on HCR
technologies.\332\ For further discussion of HCR, see Section
III.D.1.c).
---------------------------------------------------------------------------

    \331\ Auto Innovators, Docket No. NHTSA-2018-0067-12073-A1, at
p. 139.
    \332\ Ford, Docket No. NHTSA-2018-0067-11928-A1, at p. 8.
---------------------------------------------------------------------------

    PHEV technologies supersede the micro, mild, and strong hybrids,
and can only be replaced by full electric technologies. Plug-in hybrid
technology paths are also mutually exclusive, with the PHEV20
technologies able to progress to the PHEV50 technologies.
    The engine and transmission technologies on a vehicle are
superseded when PHEV technologies are applied to a vehicle. For all
plug-in technologies, the model applies an AT8L2 transmission. For
PHEV20/50 and PHEV20H/50H, the vehicle receives a full Atkinson cycle
engine, Eng26, and for PHEV20T/50T, the vehicle receives a TURBO1
engine, Eng12.
    SKIP logic applies to PHEV20/20H and PHEV50/50H under the same four
conditions listed for the strong hybrid technologies in the previous
section, for the same reasons previously discussed.
    The adoption of BEVs and FCEVs is limited by both path logic and
phase in caps. BEV200/300/400/500 and FCEV are applied as end-of-path
technologies that superseded previous levels of electrification.
    The main adoption feature applicable to BEVs and FCEVs is phase-in
caps, which are defined in the CAFE Model input files as percentages
that represent the maximum rate of increase in penetration rate for a
given technology. They are accompanied by a phase-in start year, which
determines the first year the phase-in cap applies. Together, the
phase-in cap and start year determine the maximum penetration rate for
a given technology in a given year; the maximum penetration rate equals
the phase-in cap times the number of years elapsed since the phase-in
start year. Note that phase-in caps do not inherently dictate how much
a technology is applied by the model. Rather, they represent how much
of the fleet could have a given technology by a given year. Because
BEV200 costs less and has higher effectiveness values than other
advanced electrification technologies,\333\ the model will have
vehicles adopt it first, until it is restricted by the phase-in cap.
---------------------------------------------------------------------------

    \333\ This is because BEV200 uses fewer batteries and weighs
less than BEVs with greater ranges.
---------------------------------------------------------------------------

    Table III-18 shows the phase-in caps, phase-in year, and maximum
penetration rate through 2050 for BEV and FCEV technologies. For
comparison, the actual penetration rate of each technology in the
baseline fleet is also listed in the fourth column from the left.

[GRAPHIC] [TIFF OMITTED] TR02MY22.081

    The BEV200 phase-in cap is informed by manufacturers' tendency to
move away from low-range vehicle offerings, in part because of consumer
hesitancy to adopt this technology. The advertised range on most
electric vehicles does not reflect extreme cold and hot real-world
driving conditions that affect the utility of already low-range
vehicles.\334\ Many manufacturers have told us that the portion of
consumers willing to accept a vehicle with our lowest range model which
is less than 250 miles of electric range is small, and many
manufacturers do not plan to offer vehicles with less 250 miles of
electric range.\335\
---------------------------------------------------------------------------

    \334\ AAA. ``AAA Electric Vehicle Range Testing.'' February
2019. https://www.aaa.com/AAA/common/AAR/files/AAA-Electric-Vehicle-Range-Testing-Report.pdf (accessed: Feb. 11, 2022).
    \335\ See also, e.g., Baldwin, Roberto. ``Tesla Model Y Standard
Range Discontinued; CEO Musk Tweets Explanation.'' Car and Driver,
30 Apr. 2021, www.caranddriver.com/news/a35602581/elon-musk-model-y-discontinued-explanation. (Accessed: May 20, 2020)
---------------------------------------------------------------------------

    Furthermore, the average BEV range has steadily increased over the
past decade,\336\ perhaps in part as batteries have become more cost
effective. EPA observed in its 2021 Automotive Trends Report that ``the
average range of new EVs has climbed substantially. In model year 2020
the average new EV is projected to have a 286-mile range, or about four
times the range of an average EV in 2011. This difference is largely
attributable to higher production of new EVs with much longer ranges.''
\337\ The maximum growth rate for BEV200 in the model is set
accordingly low to less than 0.1 percent per year. While this rate is
significantly lower than that of the other BEV technologies, the BEV200
phase-in cap allows the penetration rate of low-range BEVs to grow by a
multiple of what is currently observed in the market.
---------------------------------------------------------------------------

    \336\ 2021 EPA Automotive Trends Report, at 56, figure 4.17.
    \337\ 2021 EPA Automotive Trends Report, at p. 58.
---------------------------------------------------------------------------

    For BEV300, 400, and 500, phase-in caps are intended to
conservatively reflect potential challenges in the scalability of BEV
manufacturing, and implementing BEV technology on many vehicle
configurations, including larger vehicles. In the short term, the
penetration of BEVs is largely limited by battery availability. For
example, Tesla is not yet producing electric vans because of cell
production constraints, and it remains a bottleneck in the company's
expansion into new product lines.\338\ Incorporating battery packs that
provide greater amounts of electric range into vehicles also poses its
own engineering challenges. Heavy batteries and large packs may be
difficult to integrate for many vehicle configurations, and require
structural vehicle modifications. Pickup trucks and large SUVs, in
particular, require higher levels of energy as the number of passengers
and/or payload increases, for towing and other high-torque
applications. The BEV400 and 500 phase-in caps reflect these
transitional challenges.
---------------------------------------------------------------------------

    \338\ Hyatt, Kyle. ``Tesla Will Build an Electric Van
Eventually, Elon Musk Says.'' Roadshow, CNET, 28 Jan. 2021,
www.cnet.com/roadshow/news/tesla-electric-van-elon-musk/. (Accessed
May 20, 2021)
---------------------------------------------------------------------------

    The phase-in cap for FCEVs is based on existing market share as
well as historical trends in FCEV production. FCEV production share in
the past five years has been extremely low, and we set the phase-in cap
accordingly.\339\ As with BEV200, however, the phase-in cap still
allows for the market share of FCEVs to grow several times over.
---------------------------------------------------------------------------

    \339\ 2020 EPA Automotive Trends Report, at 52, figure 4.13.
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    We received limited comments on the NPRM referring to how we apply
electrification adoption features for the analysis. In its comments to
EPA's NPRM, submitted to our docket as a courtesy, Auto Innovators
stated they expect that consumers are likely to be more accepting of
longer BEV ranges,\340\ which generally agrees with our expectations
and reasoning in support of why we set the BEV200 phase-in cap.
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    \340\ Auto Innovators, at p. 56.
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    In contrast, ICCT stated that ``there is no engineering or
technical reason to limit application of strong hybrids in the fleet.
Powersplit hybrids may have torque limits, but there is no limitation
for parallel hybrid systems, whether P0, P1, P2, P3, or P4
architecture, as the engine output is routed separately from the motor
output. This is demonstrated by the 2021 Ford F150 pickup truck with a
P2 strong hybrid and the upcoming 2022 Toyota Tundra full-size pickup
truck with a strong hybrid and a conventional 10-speed automatic.''
\341\ ICCT also included examples of hybrid applications in support of
its comment that all vehicles can benefit from hybrid technology that
included the Porsche 918 plug-in hybrid, 2019 Dodge Ram 1500 pickup
truck, and 2021 Ford F150 pickup truck. Similarly, Tesla stated that we
artificially constrained the level of electrification, pointing to the
phase-in caps placed on BEVs.
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    \341\ ICCT, at p. 10.

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    Regarding ICCT's comment, the NPRM analysis only limited adoption
of SHEVPS and P2HCR combinations for a small number of applications
like pickups, large SUVs that shared pickup engines, and performance-
oriented vehicles. All other conventional vehicles can adopt P2 hybrid
powertrains; for example, the Toyota Tundra, which has a turbocharged
engine paired with a 10-speed automatic transmission is allowed to
adopt P2 hybrid. Additionally, most vehicles can adopt a PS hybrid
system, like the Toyota Highlander. ICCT's other example, the Porsche
918, an $845,000 4.6 liter V8 plug-in P2 hybrid with total 887 hp and
944 lb.-ft of torque, is an example of a vehicle that we could model in
our analysis as a SHEVP2 plug-in hybrid.\342\ However, it is unclear to
what extent the hybrid technology on the Porsche 918 could apply to the
mass market fleet. Other U.S. market Porsche plug-in hybrids, like the
Cayenne E-Hybrid and Panamera E-Hybrid, are modeled as SHEVP2 plug-
hybrids in our analysis.\343\ In all cases, the examples provided by
ICCT were modeled in accordance with their comments.344 345
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    \342\ Porsche. ``The Super Sportscar.'' https://newsroom.porsche.com/en/products/918-spyder-10713.html. (Accessed:
Dec. 17, 2021); Cnet Road and Show. ``Porsche 918 Spyder: Plug-in
hybrid does 94mpg, 198mph.'' https://www.cnet.com/roadshow/pictures/porsche-918-spyder-plug-in-hybrid-does-94mpg-198mph/. (Accessed:
Dec. 17, 2021)
    \343\ See the market_data file vehicle codes 4212003, 4212004,
4212009, 4212010, 4222003, 4222004, 4222005, 4222015, 4222016, and
4222017 in the vehicles tab.
    \344\ 2022 Toyota Tundra Product Information.
2022_Toyota_Tundra_Product_Information_FINAL.pdf; Buchholz, K.,
``2022 Toyota Tundra: V8 out, twin-turbo hybrid takes over'', SAE.
September 22, 2021. https://www.sae.org/news/2021/09/2022-toyota-tundra-gains-twin-turbo-hybrid-power. (Accessed: Dec. 20, 2021);
Macaulay, S., ``Engineering the 2022 Toyota Tundra'', SAE. October
10, 2021. https://www.sae.org/news/2021/10/engineering-the-2022-toyota-tundra. (Accessed: Dec. 20, 2021)
    \345\ ICCT, at p. 8.
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    For both the NPRM and the final rule analysis, BEVs have phase-in
cap limitations applied based on an analysis market availability,
battery costs, and consumer acceptance in the rule making time
frame.\346\ The BEV200 is limited to a greater extent than the BEV300
and BEV400 to account for anticipated market demand for shorter-range
BEVs. As discussed earlier, the 2021 EPA Trends Report that showed that
the average range of BEVs has increased beyond 200 miles to an average
of 286 miles. As such, 300-mile range BEVs and up will most likely
become the status quo for the fleet in the rulemaking time frame.\347\
In addition, the BEV300 and BEV400 caps were not met in either the NPRM
or this final rule analysis for any of the alternatives considered.
This means that even with the market caps in place, the alternatives
did not require manufacturers to increase BEV production because the
standards were met with other cost-effective technologies. Accordingly,
for the final rule analysis, we continued to use the same adoption
features as used in the NPRM to reflect what we believe will
foreseeably occur in the market in the rulemaking time frame.
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    \346\ John Elkin, MIT finds that it might take a long time for
EVs to be as affordable as you want, Digital Trends (November 23,
2019), https://www.digitaltrends.com/cars/mit-study-finds-ev-market-will-stall-in-the-2020s/.
    \347\ 20210 EPA Automotive Trends Report, at 536, figure 4.174.
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(d) Electrification Effectiveness Modeling
    For this analysis, we consider a range of electrification
technologies which, when modeled, result in varying levels of
effectiveness at reducing fuel consumption. As discussed above, the
modeled electrification technologies include micro hybrids, mild
hybrids, two different strong hybrids, two different plug-in hybrids
with two separate all electric ranges, full battery electric vehicles,
and fuel cell electric vehicles. Each electrification technology
consists of many complex sub-systems with unique component
characteristics and operational modes. As discussed further below, the
systems that contribute to the effectiveness of an electrified
powertrain in the analysis include the vehicle's battery, electric
motors, power electronics, and accessory loads. Procedures for modeling
each of these sub-systems are broadly discussed in this section and the
Autonomie model documentation.
    Argonne uses data from their Advanced Mobility Technology
Laboratory (AMTL) to develop Autonomie's electrified powertrain models.
The modeled powertrains are not intended to represent any specific
manufacturer's architecture but are intended to act as surrogates
predicting representative levels of effectiveness for each
electrification technology.
    Autonomie determines the effectiveness of each electrified
powertrain type by modeling the basic components, or building blocks,
for each powertrain, and then combining the components modularly to
determine the overall efficiency of the entire powertrain. Autonomie
identifies components for each electrified powertrain type, and then
interlinks those components to create a powertrain architecture.
Autonomie then models each electrified powertrain architecture and
provides an effectiveness value for each. For example, Autonomie
determines a BEV's overall efficiency by considering the efficiencies
of the battery, the electric traction drive system (the electric
machine and power electronics), and mechanical power transmission
devices. Or, for a SHEVP2, Autonomie combines a very similar set of
components to model the electric portion of the hybrid powertrain, and
then also includes the combustion engine and related power for
transmission components. See TSD Chapter 3.3.4 and the Autonomie model
documentation for a complete discussion of electrification component
modeling.
    As discussed earlier in Section III.C.4, Autonomie applies
different powertrain sizing algorithms depending on the type of vehicle
considered because different types of vehicles not only contain
different powertrain components to be optimized, but they must also
operate in different driving modes. While the conventional powertrain
sizing algorithm must consider only the power of the engine, the more
complex algorithm for electrified powertrains must simultaneously
consider multiple factors, which could include the engine power,
electric machine power, battery power, and battery capacity. Also,
while the resizing algorithm for all vehicles must satisfy the same
performance criteria, the algorithm for some electric powertrains must
also allow those electrified vehicles to operate in certain driving
cycles, like the US06 cycle, without assistance of the combustion
engine, and ensure the electric motor/generator and battery can handle
the vehicle's regenerative braking power, all-electric mode operation,
and intended range of travel.
    To establish the effectiveness of the technology packages,
Autonomie simulates the vehicles' performance on compliance test
cycles, as discussed in Section III.C.4.348 349 350 The
range of effectiveness for the electrification technologies in this
analysis is a result of the interactions between the components listed
above and how the modeled vehicle operates on its respective test
cycle.
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    \348\ See U.S. EPA, ``How Vehicles are Tested.'' https://www.fueleconomy.gov/feg/how_tested.shtml. (Accessed: May 6, 2021)
    \349\ See Autonomie model documentation, Chapter 6, Test
Procedures and Energy Consumption Calculations.
    \350\ EPA Guidance Letter. ``EPA Test Procedures for Electric
Vehicles and Plug-in Hybrids.'' Nov. 14, 2017. https://www.fueleconomy.gov/feg/pdfs/EPA%20test%20procedure%20for%20EVs-PHEVs-11-14-2017.pdf. (Accessed: May 6, 2021)

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This range of values will result in some modeled effectiveness values
being close to real-world measured values, and some modeled values that
will depart from real-world measured values, depending on the level of
similarity between the modeled hardware configuration and the real-
world hardware and software configurations. This modeling approach
comports with NAS's 2015 recommendation to use full vehicle modeling
supported by application of lumped improvements at the sub-model
level.\351\ In addition, the more recent 2021 NAS Report modeled
electrification technologies with Argonne's Autonomie model using a
similar approach to our analysis.\352\
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    \351\ 2015 NAS Report, at p. 292.
    \352\ 2021 NAS Report, at p. 189.
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    We received limited comments regarding electrification
effectiveness modeling. ICCT commented that the agency's strong hybrid
effectiveness data are outdated, because we rely on older powertrain
data like engine maps from the 2010 Toyota Prius, and we do not allow
this engine and other hybrid technologies to improve.\353\ Similarly,
ICCT recommended that further research should be considered to improve
hybrid power management and engines for strong hybrids.\354\ Another
commenter, Walter Kreucher, stated that the electric ranges for
electrified vehicles are lower than what we are modeling. Specifically,
Mr. Kreucher stated that extreme cold, hot, and aggressive driving
conditions have reduced all-electric range anywhere from 39 to 51
percent, based on a study from AAA.\355\
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    \353\ ICCT, at p. 5.
    \354\ ICCT, in Appendices at p. 2.
    \355\ Walt Kreucher, Docket No. NHTSA-2021-0053-0015, at p. 6.
---------------------------------------------------------------------------

    We disagree with ICCT that the electrification technology
represented in this analysis is outdated. The majority of the
technologies were developed specifically to support analysis for this
rulemaking time frame. For example, the hybrid Atkinson engine peak
thermal efficiency was updated based on 2017 Toyota Prius engine
data.356 357 Toyota stated that their current hybrid engines
achieve 41 percent thermal efficiency for their current product line up
which aligns with our modeling.\358\ Similarly, the electric machine
peak efficiency for FCEVs and BEVs is 98 percent and based on the 2016
Chevy Bolt.\359\ Accordingly, we have made no changes to the electric
machine efficiency maps for this final rule analysis.
---------------------------------------------------------------------------

    \356\ Atkinson Engine Peak Efficiency is based on 2017 Prius
Peak Efficiency and scaled up to 41 percent. Autonomie Model
Documentation at p. 138.
    \357\ Docketed supporting material. ANL--All
Assumptions_Summary_NPRM_022021.xlsx, ANL--Summary of Main Component
Performance Assumptions_NPRM_022021.xlsx, Argonne Autonomie Model
Documentation_NPRM.pdf and ANL--Data Dictionary_NPRM_022021.XLSX.
    \358\ Carney, D. ``Toyota unveils more new gasoline ICEs with
40% thermal efficiency''. SAE. April 4, 2018. https://www.sae.org/news/2018/04/toyota-unveils-more-new-gasoline-ices-with-40-thermal-efficiency. (Accessed Dec. 21, 2021)
    \359\ F. Momen, K. Rahman, Y. Son and P. Savagian, ``Electrical
propulsion system design of Chevrolet Bolt battery electric
vehicle,'' 2016 IEEE Energy Conversion Congress and Exposition
(ECCE), 2016, pp. 1-8, doi: 10.1109/ECCE.2016.7855076.
---------------------------------------------------------------------------

    We agree with Mr. Kreucher that extreme cold and hot conditions
impact electrified vehicle range. We use the latest compliance testing
procedures to appropriately evaluate the effectiveness and range of
electrified technologies, as discussed earlier in this section.
However, there are some extreme conditions, which may impact electric
vehicle range, which may not be captured by the Federal test cycle. The
selection of a phase-in cap for BEV200 is based in part on
consideration of differences in utility, including the potential for
temperature-based (among other things) variations in driving range,
that may affect consumer adoption of shorter-range BEVs. For more
details, see Section III.D.3.c) of this preamble, Electrification
Adoption Features.
    The range of effectiveness values for the electrification
technologies, for all ten vehicle technology classes, is shown in
Figure III-13. In the graph, the box shows the inner quartile range
(IQR) of the effectiveness values and whiskers extend out 1.5 x IQR.
The dots outside of the whiskers show values outside these bounds.
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(e) Electrification Costs
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    \360\ The data used to create this figure can be found in the
FE_1 Adjustments file.
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    The total cost to electrify a vehicle in this analysis is based on
the battery the vehicle requires, the non-battery electrification
component costs the vehicle requires, and the traditional powertrain
components that must be added or removed from the vehicle to build the
electrified powertrain.
    We work collaboratively with the experts at Argonne National
Laboratory to generate battery costs using BatPaC, which is a model
designed to calculate the cost of a vehicle battery for a specified
battery power, energy, and type. For this analysis, Argonne used BatPaC
v4.0 (October 2020 release) to create lookup tables for battery cost
and mass that the Autonomie simulations reference when a vehicle
receives an electrified powertrain. The BatPaC battery cost estimates
for mild hybrids, strong hybrids, plug-in hybrids, and full battery
electric vehicles are generated for a base year, in this case for MY
2020. Accordingly, our BatPaC inputs characterize the state of the
market in MY 2020 and employ a widely utilized cell chemistry
(NMC622),\361\ average estimated battery pack production volume per
plant (25,000), and a plant efficiency or plant cell yield value of 95
percent.
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    \361\ Autonomie model documentation, Chapter 5.9. Argonne
surveyed A2Mac1 and TBS teardown reports for electrified vehicle
batteries and of the five fully electrified vehicles surveyed, four
of those vehicles used NMC622 and one used NMC532. See also Georg
Bieker, A Global Comparison of the Life-Cycle Greenhouse Gas
Emissions of Combustion Engine and Electric Passenger Cars,
International Council on Clean Transportation (July 2021), https://theicct.org/sites/default/files/publications/Global-LCA-passenger-cars-jul2021_0.pdf (``For cars registered in 2021, the GHG emission
factors of the battery production are based on the most common
battery chemistry, NMC622-graphite batteries . . . .''); 2021 NAS
Report, at 87 (``. . . NMC622 is the most common cathode chemistry
in 2019. . . .'').
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    For this final rule, we use a lower SS12V micro hybrid battery cost
that was not developed in BatPaC. The NPRM SS12V fixed battery pack
direct manufacturing cost was $237, across all vehicle classes. For
this final rule analysis, the agency conducted additional research
regarding battery types used in typical SS12V systems yielding a
battery cost that reflects the cost of a more common battery chemistry.
Specifically, absorbed-glass-mat (AGM) batteries are more common in
SS12V systems than the Li-ion-based chemistry used in the NPRM
analysis.362 363 364 The battery pack direct manufacturing
cost for SS12V systems is now $113, across all vehicle classes. This
cost also more closely aligns with the estimated cost of the SS12V
system presented in the 2015 NAS Report.\365\
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    \362\ EPA-HQ-OAR-2021-0208-0144, p. 5-73.
    \363\ USABC, ``United States Advanced Battery Consortium Battery
Test Manual For 12 Volt Start/Stop Vehicles.'' January 2018.
Revision 2. Contract DE-AC07-05ID14517.
    \364\ H. Tataria; O. Gross; C. Bae; B. Cunningham; J.A. Barnes;
J. Deppe; J. Neubauer. ``USABC Development of 12 Volt Battery for
Start-Stop Application: Preprint'': 10 pp. 2015. https://www.nrel.gov/docs/fy15osti/62680.pdf.
    \365\ 2015 NAS Report, at 158.
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    For BEV400 and BEV500, we did not use BatPaC to generate battery
pack costs. Rather, we scaled the BatPaC-generated BEV300 costs to
match the range of BEV400 and BEV500 vehicles to compute a direct
manufacturing cost for those vehicles' batteries. We explained in the
NPRM that we initially examined using BatPaC to model the

cost and weight of BEV400 and BEV500 packs, however, initial values
from the model could not be validated and were based on assumptions for
smaller sized battery packs. We stated that the initial results
provided cost and weight estimates for BEV400 battery packs out of
alignment with current examples of BEV400s in the market, and there are
currently no examples of BEV500 battery packs in the market against
which to validate the pack results.
    Although one example of a BEV500 has entered the market since
publication of the NPRM, it is for a low volume passenger vehicle, and
it is not representative of some pack characteristics and costs for
vehicles in this analysis.366 367 In particular, BatPaC
weights for the BEV400 and BEV500 pickup truck classes often made the
vehicle exceed the light duty 8,500 lb. curb weight threshold for light
duty vehicles, pushing the vehicles into the next weight class. While
this may be representative of what could happen with vehicles that have
more significant range and towing requirements (for example, the 2022
GMC Hummer EV will be a class 2b vehicle \368\), we also believe that
manufacturers will employ different weight saving strategies to keep
heavier vehicles in the light-duty fleet. For this final rule analysis,
we determined that keeping the battery pack mass a more consistent
percentage of vehicle curb weight using the scaling method was a
reasonable assumption, and we will explore how to model this concept
more in future analyses.
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    \366\ CarAndDriver. ``2022 Lucid Air Lucid Air EV's Battery Will
Be a Big 113.0 kWh, Topping Tesla's Best.'' September 2, 2020.
https://www.caranddriver.com/news/a33797162/2021-lucid-air-517-mile-range-113-kwh-battery. Last accessed March 28, 2022.
    \367\ Fueleconomy.gov, 2022 Lucid Air. https://www.fueleconomy.gov/feg/Find.do?action=sbs&id=44495&id=44493 (last
accessed: January 23, 2022).
    \368\ CarAndDriver. ``2022 GMC Hummer EV EPA Documents Reveal
MPGe, Weight, Other Details.'' Feb 15, 2022. https://www.caranddriver.com/news/a39049358/2022-gmc-hummer-ev-pickup-epa-specs. Last accessed March 28, 2022.
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    Finally, we apply a learning rate to the direct manufacturing cost
to reflect how we expect battery costs could fall over the timeframe
considered in the analysis. For most electrification technologies, the
learning rate that we apply reflects ``midrange'' year-over-year
improvements until MY 2032. Post 2032, the learning rates incrementally