ADSL engine technology. The diesel engine maps are new for this
analysis. The LD diesel engine maps and HD van engine maps are based on
a modern 3.0L turbo-diesel engine, and the HDPUV pickup truck engine
maps are based on a larger 6.7L turbo-diesel engine.
    Finally, compressed natural gas (CNG) systems are ICEs that run on
natural gas as a fuel source. The fuel storage and supply systems for
these engines differ tremendously from gasoline, diesel, and flex fuel
vehicles.\210\ The CNG engine option has been included in past
analyses; however, the LD and HDPUV baseline fleets do not include any
dedicated CNG vehicles. As with the last analyses, CNG engines are
included as a baseline-only technology and are not applied to any
vehicle that did not already include a CNG engine.
---------------------------------------------------------------------------

    \210\ Flexible fuel vehicles (FLEX) are designed to run on
gasoline or gasoline-ethanol blends of up to 85 percent ethanol.
---------------------------------------------------------------------------

    The first step in assigning engine technologies to vehicles in the
LD and HDPUV baseline fleets is to use data for each manufacturer to
determine which vehicle platforms share engines. Within each
manufacturer's fleet, we develop and assign unique engine codes based
on configuration, technologies applied, displacement, CR, and power
output. While the process for engine assignments is the same between
the LD and HDPUV analyses, engine codes are not shared between the two
fleets, and engine technologies are not shared between the fleets, for
the reasons discussed above. We also assign engine technology classes,
which are codes that identify engine architecture (e.g., how many
cylinders the engine has, whether it is a DOHC or SOHC, and so on) to
accurately account for engine costs in the analysis.
    When we assign engine technologies to vehicles in the baseline
fleets, we must consider the actual technologies on a manufacturer's
engine and compare those technologies to the engine technologies in our
analysis. We have just over 270 unique engine codes in the LD baseline
fleet and just over 20 unique engine codes in the HDPUV fleet, meaning
that for both analysis fleets, we must identify the technologies
present on those almost 300 unique engines in the real world, and make
decisions about which of our approximately 40 engine map models (and
therefore engine technology on the technology tree) \211\ best
represents those real-world engines. When we consider how to best fit
each of those 300 engines to our 40 engine technologies and engine map
models, we use specific technical elements contained in manufacturer
publications, press releases, vehicle benchmarking studies, technical
publications, manufacturer's specification sheets, and occasionally CBI
(like the specific technologies, displacement, CR, and power mentioned
above), and engineering judgment. For example, in the LD analysis, an
engine with a 13.0:1 CR is a good indication that an engine would be
considered an HCR engine in our analysis, and some engines that achieve
a slightly lower CR, e.g., 12.5, may be considered an HCR engine
depending on other technology on the engine, like inclusion of SGDI,
increased engine displacement compared to other competitors, a high
energy spark system, and/or reduction of engine parasitic losses
through variable or electric oil and water pumps. Importantly, we never
assign engine technologies based on one factor alone; we use data and
engineering judgment to assign complex real-world engines to their
corresponding engine technologies in the analysis. We believe that our
initial characterization of the fleet's engine technologies reasonably
captures the current state of the market while maintaining a reasonable
amount of analytical complexity. Also, as a reminder, in addition to
the 40 engine map models used in the Engine Paths Collection, we have
over 20 additional potential powertrain technology assignments
available in the Hybrid/Electric Paths Collection.
---------------------------------------------------------------------------

    \211\ We assign each engine code technology that most closely
corresponds to an engine map; for most technologies, one box on the
technology tree corresponds to one engine map that corresponds to
one engine code.
---------------------------------------------------------------------------

    Engine technology adoption in the model is defined through a
combination of technology path logic, refresh and redesign cycles,
phase-in capacity limits,\212\ and SKIP logic. How does technology path
logic define technology adoption? Once an engine design moves to the
advanced engine tree it is not allowed to move to alternate advanced
engine trees. For example, any LD basic engine can adopt one of the
TURBO engine technologies, but vehicles that have turbocharged engines
in the baseline fleet will stay on the Turbo Engine Path to prevent
unrealistic engine technology change in the short timeframe considered
in the rulemaking analysis. This represents the concept of stranded
capital, which as discussed above, is when manufacturers amortize
research, development, and tooling expenses over many years. Besides
technology path logic, which applies to all manufacturers and
technologies, we place additional constraints on the adoption of VCR
and HCR technologies.
---------------------------------------------------------------------------

    \212\ Although we did apply phase-in caps for this analysis, as
discussed in Chapter 3.1.1 of the Draft TSD, those phase-in caps are
not binding because the model has several other less advanced
technologies available to apply first at a lower cost, as well as
the redesign schedules. As discussed in Draft TSD Chapter 2.2, 100
percent of the analysis fleet will not redesign by 2023, which is
the last year that phase-in caps could apply to the engine
technologies discussed in this section. Please see the Draft TSD for
more information on engine phase-in caps.
---------------------------------------------------------------------------

    Basic and turbocharged engines in the LD analysis can adopt a VCR
engine if the engine is currently manufactured by a manufacturer or
partnered manufacturer that has already implemented the technology. VCR
technology requires a complete redesign of the engine, and in the
analysis fleet, only two models have incorporated this technology. VCR
engines are complex, costly by design, and address many of the same
efficiency losses as mainstream technologies like turbocharged
downsized engines, making it unlikely that a manufacturer that has
already started down an incongruent technology path would adopt VCR
technology. Because of these issues, we limited adoption of the VCR
engine technology to original equipment manufacturers (OEMs) that have
already employed the technology and their partners. We do not believe
any other manufacturers will invest to develop and market this
technology in their fleet in the rulemaking time frame.
    HCR engines are subject to three limitations. This is because, as
we have recognized in past analyses,\213\ HCR engines excel in lower
power applications for lower load conditions, such as driving around a
city or steady state highway driving without large payloads. Thus,
their adoption is more limited than some other technologies.
---------------------------------------------------------------------------

    \213\ The discussions at 83 FR 43038 (Aug. 24, 2018), 85 FR
24383 (April 30, 2020), 86 FR 49568 and 49661 (September 3, 2021),
and 87 FR 25786 and 25790 (May 2, 2022) are incorporated herein by
reference.
---------------------------------------------------------------------------

    First, we do not allow vehicles with 405 or more horsepower, and
(to simulate parts sharing) vehicles that share engines with vehicles
with 405 or more horsepower, to adopt HCR engines due to their
prescribed power needs being more demanding and likely not supported by
the lower power density found in HCR-based engines.\214\ Because

LIVC essentially reduces the engine's displacement, to make more power
and keep the same levels of LIVC, manufacturers would need to increase
the displacement of the engine to make the necessary power. We do not
believe manufacturers will increase the displacement of their engines
to accommodate HCR technology adoption. This bears out in industry
trends: total engine size (or displacement) is at an all-time low, and
trends show that industry focus on turbocharged downsized engine
packages are leading to their much higher market penetration.\215\
Separately, as seen in the baseline fleet, manufacturers generally use
HCR engines in applications where the vehicle's power requirements fall
significantly below our horsepower threshold. In fact, the horsepower
average for the sales weighted average of vehicles in the baseline
analysis fleet that use HCR Engine Path technologies is 179 hp,
demonstrating that HCR engine use has indeed been limited to lower-hp
applications, and well below our 405 hp threshold. In fringe cases
where a vehicle classified as having higher load requirements does have
an HCR engine, it is coupled to a hybrid system.\216\
---------------------------------------------------------------------------

    \214\ Heywood, John B. Internal Combustion Engine Fundamentals.
McGraw-Hill Education, 2018. Chapter 5.
    \215\ See 2022 EPA Trends Report at 46, 72.
    \216\ See the Market Data Input File. As an example, the
reported total system horsepower for the Ford Maverick HEV is also
191hp, well below our 405hp threshold. See also the Lexus LC/LS
500h: the Lexus LC/LS 500h also uses premium fuel to reach this
performance level.
---------------------------------------------------------------------------

    Secondly, to maintain a performance-neutral analysis,\217\ we
exclude pickup trucks and (to simulate parts sharing) \218\ vehicles
that share engines with pickup trucks from receiving HCR engines that
are not accompanied by an electrified powertrain. In other words,
pickup trucks and vehicles that share engines with pickup trucks can
receive HCR-based engine technologies in the Hybridization Paths
Collection of technologies. We exclude pickup trucks and vehicles that
share engines with pickup trucks from receiving HCR engines that are
not accompanied by an electrified powertrain because these often-
heavier vehicles have higher low speed torque needs, higher base road
loads, increased payload and towing requirements,\219\ and have
powertrains that are sized and tuned to perform this additional work
above what passenger cars are required to conduct. Again, vehicle
manufacturers' intended performance attributes for a vehicle--like
payload and towing capability, intention for off-road use, and other
attributes that affect frontal area and rolling resistance--dictate
whether an HCR engine can be a suitable technology choice for that
vehicle.220 221 For example, road loads are comprised of
aerodynamic loads which include frontal area vehicle design along with
rolling resistance that attribute to higher engine loads as vehicle
speed increases.\222\ We assume that a manufacturer intending to apply
HCR technology to their pickup truck or vehicle that shares an engine
with a pickup truck would do so in combination with an electric system
to assist with the vehicle's load needs, and indeed the only
manufacturer that has an HCR-like engine (in terms of how we model HCR
engines in this analysis) in its pickup truck in the baseline fleet has
done so.
---------------------------------------------------------------------------

    \217\ As discussed in detail in Section II.C.3 and II.C.6 above,
we maintain a performance-neutral analysis to capture only the costs
and benefits of manufacturers adding fuel-economy-improving
technology to their vehicles in response to CAFE standards.
    \218\ See Section II.C.6.
    \219\ See Society of Automotive Engineers Surface Vehicle
Recommended Practice J2807. Performance Requirements for Determining
Tow-Vehicle Gross Combination Weight Rating and Trailer Weight
Rating (issued April 2008, revised February 2020); Trevor Reed. SAE
J207 Tow Tests--The Standard, Motortrend (Jan 16, 2015). Available
at https://www.motortrend.com/how-to/1502-sae-j2807-tow-tests-the-standard/. (Accessed: May 31, 2023). When we say ``increased payload
and towing requirements,'' we are referring to a literal defined set
of requirements that manufacturers follow to ensure the
manufacturer's vehicle can meet a set of performance measurements
when building a tow-vehicle in order to give consumers the ability
to ``cross-shop'' between different manufacturer's vehicles. As
discussed in detail above in Section II.C.3 and II.C.6, we maintain
a performance neutral analysis to ensure that we are only accounting
for the costs and benefits of manufacturers adding technology in
response to CAFE standards. This means that we will apply adoption
features, like the HCR application restriction, to a vehicle that
begins the analysis with specific performance measurements, like a
pickup truck, where application of the specific technology would
likely not allow the vehicle to meet the manufacturer's baseline
performance measurements.
    \220\ See, e.g., Supplemental Comments of Toyota Motor North
America, Inc., Notice of Proposed Rulemaking: Safer Affordable Fuel-
Efficient Vehicles Rule, Docket ID Numbers: NHTSA-2018-0067 and EPA-
HQ-OAR-2018-0283. p.6.
    \221\ Supplemental Comments of Toyota Motor North America, Inc.,
Notice of Proposed Rulemaking: Safer Affordable Fuel-Efficient
Vehicles Rule, Docket ID Numbers: NHTSA-2018-0067 and EPA-HQ-OAR-
2018-0283. ``Tacoma has a greater coefficient of drag from a larger
frontal area, greater tire rolling resistance from larger tires with
a more aggressive tread, and higher driveline losses from 4WD.
Similarly, the towing, payload, and off road capability of pick-up
trucks necessitate greater emphasis on engine torque and horsepower
over fuel economy.
    This translates into engine specifications such as a larger
displacement and a higher stroke-to-bore ratio. . . . Tacoma's
higher road load and more severe utility requirements push engine
operation more frequently to the less efficient regions of the
engine map and limit the level of Atkinson operation . . . This
endeavor is not a simple substitution where the performance of a
shared technology is universal. Consideration of specific vehicle
requirements during the vehicle design and engineering process
determine the best applicable powertrain.''
    \222\ 2015 NAS Report, Chapter 6, p. 207-242.
---------------------------------------------------------------------------

    Finally, we restrict HCR engine application for some manufacturers
that are heavily performance-focused and have demonstrated a
significant commitment to power dense technologies such as turbocharged
downsizing.\223\ When we say, ``significant commitment to power dense
technologies,'' we mean that their fleets use near 100% turbocharged
downsized engines. This means that no vehicle manufactured by these
manufacturers can receive an HCR engine. Again, we implement this
adoption feature to avoid an unquantified amount of stranded capital
that would be realized if these manufacturers switched from one
technology to another.
---------------------------------------------------------------------------

    \223\ There are three manufacturers that met the criteria (near
100 percent turbo downsized fleet, and future hybrid systems are
based on turbo-downsized engines) described and were excluded: BMW
of North America, LLC, Daimler, and Jaguar Land Rover.
---------------------------------------------------------------------------

    Note, however, that these adoption features only apply to vehicles
that receive HCR engines that are not accompanied by an electrified
powertrain. A P2 hybrid system that uses an HCR engine overcomes the
low-speed torque needs using the electric motor and thus has no
restrictions or SKIPs applied.
    How effective an engine technology is at improving a vehicle's fuel
economy depends on several factors such as the vehicle's technology
class and any additional technology that is being added or removed from
the vehicle in conjunction with the new engine technology, as discussed
in Section II.C, above. The Autonomie model's full vehicle simulation
results provide most of the effectiveness values that we use as inputs
to the CAFE Model. For a full discussion of the Autonomie modeling see
Chapter 2.4 of the Draft TSD and the CAFE Analysis Autonomie
Documentation. The Autonomie modeling uses engine map models as the
primary inputs for simulating the effects of different engine
technologies.
    Engine maps provide a three-dimensional representation of engine
performance characteristics at each engine speed and load point across
the operating range of the engine. Engine maps have the appearance of
topographical maps, typically with engine speed on the horizontal axis
and engine torque, power, or BMEP on the vertical axis. A third engine
characteristic, such as brake-specific fuel consumption (BSFC), is
displayed

using contours overlaid across the speed and load map. The contours
provide the values for the third characteristic in the regions of
operation covered on the map. Other characteristics typically overlaid
on an engine map include engine emissions, engine efficiency, and
engine power. We refer to the engine maps developed to model the
behavior of the engines in this analysis as engine map models.
    The engine map models we use in this analysis are representative of
technologies that are currently in production or are expected to be
available in the rulemaking timeframe. We develop the engine map models
to be representative of the performance achievable across industry for
a given technology, and they are not intended to represent the
performance of a single manufacturer's specific engine. We target a
broadly representative performance level because the same combination
of technologies produced by different manufacturers will have
differences in performance, due to manufacturer-specific designs for
engine hardware, control software, and emissions calibration.
Accordingly, we expect that the engine maps developed for this analysis
will differ from engine maps for manufacturers' specific engines.
However, we intend and expect that the incremental changes in
performance modeled for this analysis, due to changes in technologies
or technology combinations, will be similar to the incremental changes
in performance observed in manufacturers' engines for the same changes
in technologies or technology combinations.
    IAV developed most of the LD engine map models we use in this
analysis. 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.\224\ Southwest Research Institute (SwRI) developed the
LD diesel and HDPUV engine maps for this analysis. SwRI has been
providing automotive science, technology, and engineering services for
over 70 years.\225\ Both IAV and SwRI developed our engine maps using
the GT-POWER(copyright) Modeling tool (GT-POWER). GT-POWER is a
commercially available, industry standard, engine performance
simulation tool. GT-POWER can be used to predict detailed engine
performance characteristics such as power, torque, airflow, volumetric
efficiency, fuel consumption, turbocharger performance and matching,
and pumping losses.\226\
---------------------------------------------------------------------------

    \224\ IAV Automotive Engineering. Available at: https://www.iav.com/en. (Accessed: May 31, 2023).
    \225\ Southwest Research Institite. Available at: https://www.swri.org. (Accessed: May 31, 2023).
    \226\ For additional information on the GT-POWER tool please see
https://www.gtisoft.com/gt-suite-applications/propulsion-systems/gt-power-engine-simulation-software.
---------------------------------------------------------------------------

    Just like ANL optimizes a single vehicle model in Autonomie
following the addition of a singular technology to the vehicle model,
our engine map models were built in GT-POWER by incrementally adding
engine technology to a baseline engine--built using engine test data,
component test data, and manufacturers' and suppliers' technical
publications--and then optimizing the engine to consider real-world
constraints like heat, friction, and knock. We use a small number of
baseline engine configurations with well-defined BSFC maps, and then,
in a very systematic and controlled process, add specific well-defined
technologies to create a BSFC map for each unique technology
combination. This could theoretically be done through engine or vehicle
testing, but we would need to conduct tests on a single engine, and
each configuration would require physical parts and associated engine
calibrations to assess the impact of each technology configuration,
which is impractical for the rulemaking analysis because of the
extensive design, prototype part fabrication, development, and
laboratory resources that are required to evaluate each unique
configuration. We and the automotive industry use modeling as an
approach to assess an array of technologies with more limited testing.
Modeling offers the opportunity to isolate the effects of individual
technologies by using a single or small number of baseline engine
configurations and incrementally adding technologies to those baseline
configurations. This provides a consistent reference point for the BSFC
maps for each technology and for combinations of technologies that
enables us to carefully identify and quantify the differences in
effectiveness among technologies.
    Before use in the Autonomie analysis, both IAV and SwRI validated
the generated engine maps against a global database of benchmarked
data, engine test data, single cylinder test data, prior modeling
studies, technical studies, and information presented at
conferences.\227\ IAV and SwRI also validated the effectiveness values
from the simulation results against detailed engine maps produced from
the ANL engine benchmarking programs, as well as published information
from industry and academia.228 229 This ensures reasonable
representation of simulated engine technologies. Additional details and
assumptions that we use in the engine map modeling are described in
detail in Chapter 3.1 of the Draft TSD and the CAFE Analysis Autonomie
Model Documentation chapter titled ``Autonomie--Engine Model.''
---------------------------------------------------------------------------

    \227\ Friedrich, I. et al. 2006. Automatic Model Calibration for
Engine-Process Simulation with Heat-Release Prediction. SAE
Technical Paper. Available at: https://doi.org/10.4271/2006-01-0655.
(Accessed: May 31, 2023); Rezaei, R. et al. 2012. Zero-Dimensional
Modeling of Combustion and Heat Release Rate in DI Diesel Engines.
SAE International Journal Of Engines 5(3): pp. 874-885. Available
at: https://doi.org/10.4271/2012-01-1065. (Accessed: May 31, 2023);
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.
    \228\ Bottcher, L., & Grigoriadis, P. 2019. ANL--BSFC Map
Prediction Engines 22-26. IAV.
    \229\ Reinhart, T. 2022. Engine Efficiency Technology Study.
Final Report. SwRI Project No. 03.26457.
---------------------------------------------------------------------------

    Note that we never apply absolute BSFC levels from the engine maps
to any vehicle model or configuration for the rulemaking analysis. We
only use the absolute fuel economy values from the full vehicle
Autonomie simulations to determine incremental effectiveness for
switching from one technology to another technology. The incremental
effectiveness is then applied to the absolute fuel economy or fuel
consumption value of vehicles in the analysis fleet, which are based on
CAFE or FE compliance data. For subsequent technology changes, we apply
incremental effectiveness changes 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.
    While the fuel economy improvements for most engine technologies in
the analysis are derived from the database of Autonomie full-vehicle
simulation results, the analysis incorporates a handful of what we
refer to as analogous effectiveness values. We use these when we do not
have an engine map model for a particular technology combination. To
generate an analogous effectiveness value, we use data from analogous
technology combinations for which we do have engine map models and
conduct a pairwise comparison to generate a data set of emulated
performance values for adding technology to a baseline application. We
only use analogous

effectiveness values for four technologies that are all SOHC
technologies. We determined that the effectiveness results using these
analogous effectiveness values provided reasonable results. This
process is discussed further in Chapter <IP_ADDRESS> of the Draft TSD.
    Figure II-13, Figure II-14, and Figure II-15 show the engine
technology effectiveness values for all vehicle technology classes.
These values show the calculated improvement 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.
    These values are derived from the ANL Autonomie simulation dataset
and the righthand side Y-axis shows the number of Autonomie simulations
that achieve each percentage effectiveness improvement point. The
dashed line and grey shading indicate the median and 1.5X interquartile
range (IQR), which is a helpful metric to use to identify outliers.
Comparing these histograms to the box and whisker plots presented in
prior CAFE program rule documents, it is much easier to see that the
number of effectiveness outliers is extremely small.
    Some advanced engine technologies have values that indicate low
effectiveness. We determined the low effectiveness resulted from the
application of advanced engines to existing P2 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 P2 powertrain
improves fuel economy, in part, by allowing the engine to spend more
time operating at efficient engine speed and load conditions. This
reduces the advantage of adding advanced engine technologies, which
also improve fuel economy, by broadening the range of speed and load
conditions for the engine to operate at high efficiency. This
redundancy in fuel savings mechanism results in a lower effectiveness
when the technologies are added to each other.
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[GRAPHIC] [TIFF OMITTED] TP17AU23.023

[GRAPHIC] [TIFF OMITTED] TP17AU23.024

    The engine costs in our analysis are the product of engine DMCs,
RPE, the LE, and updating to a consistent dollar year. We sourced
engine DMCs from multiple sources, but primarily from the 2015 NAS
report.\230\ For VTG and VTGE technologies (i.e., Miller Cycle), we
used cost data from a FEV technology cost assessment performed for
ICCT,\231\ aggregated using individual component and system costs from
the 2015 NAS report. We considered costs from the 2015 NAS report that
referenced a Northeast States Center for a Clean Air Future (NESCCAF)
2004 report,\232\ but believe the reference material from the FEV
report provides more updated cost estimates for the VTG technology.
---------------------------------------------------------------------------

    \230\ 2015 NAS Report, Table S.2, p. 7-8.
    \231\ Isenstadt, A. et al. 2016. Downsized, Boosted Gasoline
Engines. Working Paper. ICCT 2016-22: p. 28.
    \232\ NESCCAF. 2004. Reducing Greenhouse Gas Emissions from
Light-Duty Motor Vehicles. Available at: http://www.nesccaf.org/documents/rpt040923ghglightduty.pdf. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    All engine technology costs start with a base engine cost, and then
additional technology costs are based on cylinder and bank count and
configuration; the DMC for each engine technology is a function of unit
cost times either the number of cylinders or number of banks, based on
how the technology is applied to the system. The total costs for all
engine technologies in all MYs across all vehicle classes can be found
in the Technologies Input file.
2. Transmission Paths
    Transmissions transmit torque generated by the engine from the
engine to the wheels. Transmissions primarily use two mechanisms to
improve fuel efficiency: (1) a wider gear range, which allows the
engine to operate longer at higher efficiency speed-load points; and
(2) improvements in friction or shifting efficiency (e.g., improved
gears, bearings, seals, and other components), which reduce parasitic
losses.
    We only model automatic transmissions in both the LD and HDPUV
analyses. The four subcategories of automatic transmissions that we
model in the LD analysis include traditional automatic transmissions
(AT), dual clutch transmissions (DCT), continuously variable
transmissions (CVT and eCVT), and direct drive (DD) transmissions.\233\
We also include high efficiency gearbox (HEG) technology improvements
as options to the transmission technologies (designated as L2 or L3 in
our analysis to indicate level of technology improvement).\234\ There
has been a significant reduction in manual transmissions over the years
and they made up less than 1% of the vehicles produced in MY 2021.\235\
Due to the trending decline of manual transmissions and their current
low production volumes, we have removed

manual transmissions from this analysis.
---------------------------------------------------------------------------

    \233\ Note that eCVT and DD transmissions are only coupled with
electrified drivetrains and are therefore not included as a
standalone transmission option on the CAFE Model's technology
pathways.
    \234\ See 2015 NAS Report, at 191. HEG improvements for
transmissions represent incremental advancements in technology that
improve efficiency, such as reduced friction seals, bearings and
clutches, super finishing of gearbox parts, and improved
lubrication. These advancements are all 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
the National Academy of Sciences (NAS) 2015 recommendations, and CBI
data.
    \235\ 2022 EPA Automotive Trends Report.
---------------------------------------------------------------------------

    We only model ATs in the HDPUV analysis because, except for DD
transmissions that are only included as part of an electrified
drivetrain, all HDPUV fleet baseline vehicles use ATs. In addition,
from an engineering standpoint, DCTs and CVTs are not suited for HDPUV
work requirements, as discussed further below. The HDPUV automatic
transmissions work in the same way as the LD ATs and are labeled the
same, but they are sized and mapped, in the Autonomie effectiveness
modeling,\236\ to account for the additional work, durability, and
payload these vehicles are designed to conduct. The HDPUV transmissions
are sized with larger clutch packs, higher hydraulic line pressures,
different shift schedules, larger torque converter and different lock
up logic, and stronger components when compared to their LD
counterparts. Chapter 3.2.1 of the Draft TSD discusses the technical
specifications of the four different AT subtypes in more detail. Figure
II-16 and Figure II-17 show the LD and HDPUV transmission technology
paths.
---------------------------------------------------------------------------

    \236\ ANL--All Assumptions_Summary_NPRM_2206.xlsx, ANL--Data
Dictionary_NPRM_2206.xlsx, ANL--Summary of Main Component
Performance, Assumptions_NPRM_2206.xlsx. ANL--All Assumptions
Summary--(2b-3) FY22 NHTSA--220811.xlsx, ANL--Data Dictionary--(2b-
3) FY22 NHTSA--2200811.xlsx, ANL--Summary of Main Component
Performance Assumptions--(2b-3) FY22 NHTSA--220811.xlsx.
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[GRAPHIC] [TIFF OMITTED] TP17AU23.026

    To assign transmission technologies to vehicles in the baseline
fleets, we identify which Autonomie transmission model is most like a
vehicle's real-world transmission, considering the transmission's
configuration, costs, and effectiveness. Like with engines, we use
manufacturer CAFE compliance submissions and publicly available
information to assign transmissions to vehicles and determine which
platforms share transmissions. To link shared transmissions in a
manufacturer's fleet, we use transmission codes that include
information about the manufacturer, drive configuration, transmission
type, and number of gears. Just like manufacturers share transmissions
in multiple vehicles, the CAFE Model will treat transmissions as
``shared'' if they share a transmission code and transmission
technologies will be adopted together.
    While identifying an ATs gear count is fairly easy, identifying HEG
levels for ATs and CVTs is more difficult. We reviewed the age of the
transmission design, relative performance versus previous designs, and
technologies incorporated to assign an HEG level. There are no HEG
Level 3 automatic transmissions in either the LD or the HDPUV baseline
fleets. For the LD analysis 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. We assigned eight-
speed automatic transmissions and CVTs newly introduced for the LD
market in MY 2016 and later as HEG Level 2. All other automatic
transmissions are assigned to their respective transmissions baseline
level (i.e., AT6, AT8, and CVT). For DCTs, the number of gears in the
assignments for DCTs usually match the number of gears listed by the
data sources, with some exceptions (we assign dual-clutch transmissions
with seven and nine gears to DCT6 and DCT8 respectively). We assigned
vehicles in either the LD or HDPUV analyses fleets with a fully
electric powertrain a DD transmission. We assigned any vehicle in the
LD analysis fleet with a power-split hybrid (SHEVPS) powertrain an
electronic continuously variable transmission (eCVT). Finally, we
assigned the limited number of manual transmissions in the LD fleet as
DCTs, as we did not model manual transmissions in Autonomie for this
analysis.
    Most transmission adoption features are instituted through
technology path logic (i.e., decisions about how less advanced
transmissions of the same type can advance to more advanced
transmissions of the same type). Technology pathways are designed 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. For example,
any automatic transmission with more than five gears cannot move to a
dual-clutch transmission. We also prevent ``branch hopping'' as a proxy
for stranded capital, which is discussed in more detail in Section II.C
and Chapter 2.5 of the Draft TSD. The LD and HDPUV transmission paths
are shown above in Figure II-16 and Figure II-17.
    For the LD analysis, the automatic transmission path precludes
adoption of other transmission types once a platform progresses past an
AT8. 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
AT8 in the rulemaking timeframe. Vehicles that did not start out with
AT7L2 transmissions cannot adopt that technology in the model. It is
likely that other vehicles will not adopt the AT7L2 technology, as
vehicles that have moved to more advanced automatic transmissions have
overwhelmingly moved to 8-speed and 10-speed transmissions.\237\
---------------------------------------------------------------------------

    \237\ 2022 EPA Automotive Trends Report, at p. 66, Figure 4.21.
---------------------------------------------------------------------------

    CVT adoption is limited by technology path logic and is only
available in the LD fleet analysis and therefore, not in the technology
path for the HDPUV analysis. Vehicles that do not originate with a CVT
or vehicles

with multispeed transmissions beyond AT8 in the baseline fleet cannot
adopt CVTs. Vehicles with multispeed transmissions greater than AT8
demonstrate increased ability to operate the engine at a highly
efficient speed and load. Once on the CVT path, the platform is only
allowed to apply improved CVT technologies. Due to the limitations of
current CVTs, discussed in Draft TSD Chapter 3.2, this analysis
restricts the application of CVT technology on LDVs with greater than
300 lb.-ft of engine torque. This is because of the higher torque
(load) demands of those vehicles and CVT torque limitations based on
durability constraints. We believe the 300 lb.-ft restriction
represents an increase over current levels of torque capacity that is
likely to be achieved during the rule making timeframe. This
restriction aligns with CVT application in the baseline fleet, in that
CVTs are only witnessed on vehicles with under 280 lb.-ft of
torque.\238\ Additionally, this restriction is used to avoid stranded
capital. Finally, the analysis allows 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.\239\
DCTs are not a selectable technology for the HDPUV analysis.
---------------------------------------------------------------------------

    \238\ Market Data Input File.
    \239\ 2022 EPA Automotive Trends Report, at p. 66, Figure 4.21.
---------------------------------------------------------------------------

    Autonomie models transmissions as a sequence of mechanical torque
gains. The torque and speed are multiplied and divided, respectively,
by the current ratio for the selected operating condition. Furthermore,
torque losses corresponding to the torque/speed operating point are
subtracted from the torque input. Torque losses are defined based on a
three-dimensional efficiency lookup table that has the following
inputs: input shaft rotational speed, input shaft torque, and operating
condition. We populate transmission template models in Autonomie with
characteristics data to model specific transmissions.\240\
Characteristics data are typically tabulated data for transmission gear
ratios, maps for transmission efficiency, and maps for torque converter
performance, as applicable. Different transmission types require
different quantities of data. The characteristics data for these models
come from peer-reviewed sources, transmission and vehicle testing
programs, results from simulating current and future transmission
configurations, and confidential data obtained from OEMs and
suppliers.\241\ We model HEG improvements by modeling improvements to
the efficiency map of the transmission. As an example, the baseline AT8
model data comes from a transmission characterization study.\242\ The
AT8L2 has the same gear ratios as the AT8, however, we improve the gear
efficiency map to represent application of the HEG level 2
technologies. The AT8L3 models the application of HEG level 3
technologies using the same principle, further improving the gear
efficiency map over the AT8L2 improvements. Each transmission (15 for
the LD analysis and 6 for the HDPUV analysis) is modeled in Autonomie
with defined gear ratios, gear efficiencies, gear spans, and unique
shift logic for the technology configuration the transmission is
applied to. These transmission maps are developed to represent the gear
counts and span, shift and torque converter lockup logic, and
efficiencies that can be seen in the fleet, along with upcoming
technology improvements, all while balancing key attributes such as
drivability, fuel economy, and performance neutrality. This modeling is
discussed in detail in Chapter 3.2 of the Draft TSD and the CAFE
Analysis Autonomie Documentation chapter titled ``Autonomie--
Transmission Model.''
---------------------------------------------------------------------------

    \240\ ANL--All Assumptions_Summary_NPRM_2206.xlsx, ANL--Data
Dictionary_NPRM_2206.xlsx, ANL--Summary of Main Component
Performance, Assumptions_NPRM_2206.xlsx. ANL--All Assumptions
Summary--(2b-3) FY22 NHTSA--220811.xlsx, ANL--Data Dictionary--(2b-
3) FY22 NHTSA--2200811.xlsx, ANL--Summary of Main Component
Performance Assumptions--(2b-3) FY22 NHTSA--220811.xlsx.
    \241\ Downloadable Dynamometer Database.: https://www.anl.gov/energy-systems/group/downloadable-dynamometer-database. (Accessed:
May 31, 2023); Kim, N. et al. 2014. Advanced Automatic Transmission
Model Validation Using Dynamometer Test Data. SAE 2014-01-1778. SAE
World Congress: Detroit, MI.; Kim, N. et al. 2014. Development of a
Model of the Dual Clutch Transmission in Autonomie and Validation
With Dynamometer Test Data. International Journal of Automotive
Technologies Volume 15, Issue 2: pp 263-71.
    \242\ CAFE Analysis Autonomie Documentation chapter titled
``Autonomie--Transmission Model.''
---------------------------------------------------------------------------

    The effectiveness values for the transmission technologies, for all
LD and HDPUV technology classes, are shown in Figure II-18, Figure II-
19, and Figure II-20. Note that the effectiveness for the AT5, eCVT and
DD technologies is not shown. The DD and eCVT transmissions do not have
standalone effectiveness values because those technologies are only
implemented as part of electrified powertrains. The AT5 has no
effectiveness values because it is a baseline technology against which
all other transmission technologies are compared.

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[GRAPHIC] [TIFF OMITTED] TP17AU23.028

[GRAPHIC] [TIFF OMITTED] TP17AU23.029

BILLING CODE 4910-59-C
    Our transmission DMCs come from the 2015 NAS report and studies
cited therein. The LD costs are taken almost directly from the 2015 NAS
report adjusted to the current dollar year or for the appropriate
number of gears. We applied a 20% cost increase for HDPUV transmissions
based on comparing the additional weight, torque capacity, and
durability required in the HDPUV segment. Chapter 3.2 of the Draft TSD
discusses the specific 2015 NAS report costs used to generate our
transmission cost estimates, and all transmission costs across all MYs
can be found in CAFE Model's Technologies Input file. We have used the
2015 NAS report transmission costs for the last several LD CAFE Model
analyses (since reevaluating all transmission costs for the 2020 final
rule) and have received no comments or feedback on these costs. We seek
comment on our approach to estimating all transmission costs, but in
particular on HDPUV transmission costs for this analysis, in addition
to any publicly available data from manufacturers or reports on the
cost of HDPUV transmissions.
3. Electrification Paths
    The electrification paths include a set of technologies that share
common electric powertrain components, like batteries and electric
motors, for certain vehicle functions that were traditionally powered
by combustion engines. While all vehicles (including conventional ICE
vehicles) use batteries and electric motors in some form, some
component designs and powertrain architectures contribute to greater
levels of electrification than others--allowing the vehicle to be less
reliant on gasoline or other fuel.
    Unlike other technologies in the analysis, including other
electrification technologies, Congress placed specific limitations on
how we consider the fuel economy of PHEVs and BEVs when setting CAFE
standards.\243\ We implement these restrictions in the CAFE Model by
using fuel economy values that assume ``charge sustaining'' (gasoline-
only) PHEV operation,\244\ and by restricting technologies that convert
a vehicle to a BEV or a FCEV from being applied during ``standard-
setting'' years.\245\ However, there are several reasons why we must
still accurately model PHEVs and BEVs in the analysis; these reasons
are discussed in detail throughout this preamble and, in particular, in
Sections III and V. In brief: we must consider the existing fleet fuel
economy level in calculating the maximum feasible fuel economy level
that manufacturers can achieve in future years. Accurately calculating
the pre-existing fleet fuel economy level is crucial because it marks
the starting point for determining what further efficiency gains will
be feasible during the rulemaking timeframe. As discussed in detail
above and in Chapter 2.2 of the Draft TSD, PHEVs, BEVs, and FCEVs
currently exist in manufacturer's fleets

and count towards manufacturer's compliance fuel economy values.
---------------------------------------------------------------------------

    \243\ 49 U.S.C. 32902(h)(1), (2). In determining maximum
feasible fuel economy levels, ``the Secretary of Transportation--(1)
may not consider the fuel economy of dedicated automobiles; [and]
(2) shall consider dual fueled automobiles to be operated only on
gasoline or diesel fuel.''
    \244\ We have two sets of fuel consumption improvement data from
ANL: one that does not include charge depleting and charge
sustaining for PHEVS, and one with both.
    \245\ CAFE Model Documentation at S4.6 Technology Fuel Economy
Improvements.
---------------------------------------------------------------------------

    In addition to accurately capturing the ``baseline fleet'' of
vehicles in a given MY, we must capture a regulatory ``no action''
baseline in each MY; that is, the regulatory baseline captures what the
world will be like if our rule is not adopted, to accurately capture
the costs and benefits of CAFE standards. The ``no-action'' baseline
includes our representation of the existing fleet of vehicles (i.e.,
the LD and HDPUV baseline fleets) and (with some restrictions) our
representation of manufacturer's fleets in the absence of our
standards. Specifically, we assume that in the absence of CAFE and
HDPUV FE standards, manufacturers will produce certain BEVs to comply
with California's ACCs and ACT program. Accounting for electrified
vehicles that manufacturers produced in response to state regulatory
requirements improves the accuracy of the analysis of the costs and
benefits of additional technology added to vehicles in response to CAFE
standards, while adhering to the statutory prohibition against
considering the fuel economy gains that could be achieved if
manufacturers create new dedicated automobiles to comply with the CAFE
standards.
    Next, the costs and benefits of CAFE standards do not end in the
MYs for which we are setting standards. Vehicles produced in standard-
setting years, e.g., MYs 2027 and beyond in this analysis, will
continue to have effects for years after they are produced as the
vehicles are sold and driven. To accurately capture the costs and
benefits of vehicles subject to the standards in future years, the CAFE
Model projects compliance through MY 2050. Outside of the standard-
setting years, we model the extent to which manufacturers could produce
electrified vehicles, in order to improve the accuracy and realism of
our analysis in situations where statute does not prevent us from doing
so. Finally, we do consider the effects of electrified vehicle adoption
in the CAFE Model under a ``real-world'' scenario where we lift EPCA/
EISA's restrictions on our decision-making. This ``real-world''
analysis forms the basis of our NEPA analysis, so that we can consider
the actual environmental impacts of our actions in the decision-making
process.\246\
---------------------------------------------------------------------------

    \246\ 40 CFR 1500.1(a).
---------------------------------------------------------------------------

    For those reasons, we must still accurately model electrified
vehicles. That said, PHEVs, BEVs, and FCEVs only represent a portion of
the electrified technologies that we include in the analysis. We
discuss the range of modeled electrified technologies below and in
detail in Chapter 3.3.1 of the Draft TSD.
    Among the simpler configurations with the fewest electrification
components, micro HEV technology (SS12V) uses a 12-volt system that
simply restarts the engine from a stop. Mild HEVs use a 48-volt belt
integrated starter generator (BISG) system that restarts the vehicle
from a stop and provides some regenerative braking functionality.\247\
Mild HEVs are often also capable of minimal electric assist to the
engine on take-off.
---------------------------------------------------------------------------

    \247\ See 2015 NAS Report, at 130. (``During braking, the
kinetic energy of a conventional vehicle is converted into heat in
the brakes and is thus lost. An electric motor/generator connected
to the drivetrain can act as a generator and return a portion of the
braking energy to the battery for reuse. This is called regenerative
braking. Regenerative braking is most effective in urban driving and
in the urban dynamometer driving schedule (UDDS) cycle, in which
about 50 percent of the propulsion energy ends up in the brakes (NRC
2011, 18).'').
---------------------------------------------------------------------------

    Strong hybrid-electric vehicles (SHEVs) have higher system
voltages, compared to mild hybrids with BISG systems, and are capable
of engine start/stop and regenerative braking, electric motor assist of
the engine at higher speeds and power demands, and can provide limited
all-electric propulsion. Common SHEV powertrain architectures,
classified by the interconnectivity of common electrified vehicle
components, include both a series-parallel architecture by power-split
device (SHEVPS) as well as a parallel architecture (P2 \248\). P2s--
although enhanced by the electrification components, including just one
electric motor--remains fundamentally similar to a conventional
powertrain.\249\ In contrast, SHEVPS is considerably different than a
conventional powertrain; SHEVPSs use two electric motors, which allows
the use of a lower-power-density engine. This results in a higher
potential for fuel economy improvement compared to a P2, although the
SHEVPS' engine power density is lower.\250\ Or, put another way, ``[a]
disadvantage of the power split architecture is that when towing or
driving under other real-world conditions, performance is not
optimum.'' \251\ In contrast, ``[o]ne of the main reasons for using
parallel hybrid architecture is to enable towing and meet maximum
vehicle speed targets.'' \252\ This is an important distinction to
comprehend to understand why we allow certain types of vehicles to
adopt P2 powertrains and not SHEVPS powertrains, and to understand why
we include only P2 architectures in the HDPUV analysis. Both concepts
are discussed further below.
---------------------------------------------------------------------------

    \248\ Readers familiar with the last CAFE Model analysis may
remember this category of powertrains referred to as ``SHEVP2s.''
Now that the SHEVP2 pathway has been split into three pathways based
on the paired ICE technology, we refer to this broad category of
technologies as ``P2s.''
    \249\ Kapadia, J. et al. 2017. Powersplit or Parallel--Selecting
the Right Hybrid Architecture. SAE International Journal of
Alternative Power 6(1). Available at: https://doi.org/10.4271/2017-01-1154. (Accessed: May 31, 2023) (Parallel hybrids architecture
typically adds the electrical system components to an existing
conventional powertrain).
    \250\ Kapadia, J. et al. 2017. Powersplit or Parallel--Selecting
the Right Hybrid Architecture. SAE International Journal of
Alternative Power 6(1). Available at: https://doi.org/10.4271/2017-01-1154. (Accessed: May 31, 2023).
    \251\ 2015 NAS report, at 134.
    \252\ Kapadia, J. et al. 2017. Powersplit or Parallel--Selecting
the Right Hybrid Architecture. SAE International Journal of
Alternative Power 6(1). Available at: https://doi.org/10.4271/2017-01-1154. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    PHEVs utilize a combination gasoline-electric powertrain, like that
of a SHEV, but have the ability to plug into the electric grid to
recharge the battery, like that of a BEV; this contributes to all-
electric mode capability in both blended and non-blended PHEVs.\253\
The analysis includes PHEVs with an AER of 20 and 50 miles to encompass
the range of PHEV AER in the market today. BEVs have an all-electric
powertrain and use only batteries for the source of propulsion energy.
BEVs with ranges of 200 to more than 350 miles are used in the
analysis. Finally, FCEVs are another form of electrified vehicle that
have a fully electric powertrain that uses a fuel cell system to
convert hydrogen fuel into electrical energy. Table II-9 and Table II-
10 list every electrification technology considered in the analysis,
including its acronym and a brief description. For brevity, we refer to
technologies by their acronyms in this section.
---------------------------------------------------------------------------

    \253\ Some PHEVs operate in charge-depleting mode (i.e.,
``electric-only'' operation--depleting the high-voltage battery's
charge) before operating in charge-sustaining mode (similar to
strong hybrid operation, the gasoline and electric powertrains work
together), while other (blended) PHEVs switch between charge-
depleting mode and charge-sustaining mode during operation.

        Table II-9--Light-Duty Electrification Path Technologies
------------------------------------------------------------------------
            Technology                           Description
------------------------------------------------------------------------
SS12V.............................  12-Volt Stop-Start (Micro Hybrid-
                                     Electric Vehicle).
BISG..............................  48V Belt Mounted Integrated Starter/
                                     Generator (Mild Hybrid-Electric
                                     Vehicle).
SHEV-P2SGDID......................  P2 Strong Hybrid-Electric Vehicle
                                     with a Dual Over-Head Cam Engine
                                     and Gasoline Direct Injection.
SHEV-P2SGDIS......................  P2 Strong Hybrid-Electric Vehicle
                                     with a Single Over-Head Cam Engine
                                     and Gasoline Direct Injection.
SHEV-P2TRB1.......................  P2 Strong Hybrid-Electric Vehicle
                                     with a TURBO1 Powertrain.
SHEV-P2TRB2.......................  P2 Strong Hybrid-Electric Vehicle
                                     with a TURBO2 Powertrain.
SHEV-P2TRBE.......................  P2 Strong Hybrid-Electric Vehicle
                                     with a TURBOE Powertrain.
SHEV-P2HCR........................  P2 Strong Hybrid-Electric Vehicle
                                     with a High Compression Ratio
                                     Powertrain.
SHEV-P2HCRE.......................  P2 Strong Hybrid-Electric Vehicle
                                     with an E-High Compression Ratio
                                     Powertrain.
SHEV-PS...........................  Power Split (PS) Strong Hybrid/
                                     Electric Vehicle.
PHEV20PS..........................  Plug-In Hybrid with Power-Split
                                     device and 20-mile All Electric
                                     Range.
PHEV50PS..........................  Plug-In Hybrid with Power-Split
                                     device and 50-mile All Electric
                                     Range.
PHEV20T...........................  PHEV20 with Turbo Engine and 20-mile
                                     All Electric Range.
PHEV50T...........................  PHEV50 with Turbo Engine and 50-mile
                                     All Electric Range.
PHEV20H...........................  PHEV20 with High Compression Ratio
                                     Engine and 20-mile All Electric
                                     Range.
PHEV50H...........................  PHEV50 with High Compression Ratio
                                     Engine and 50-mile All Electric
                                     Range.
BEV1..............................  ~200-mile Battery Electric Vehicle
                                     BEV1LD <= 250 miles.
BEV2..............................  ~250-mile Battery Electric Vehicle
                                     225 miles < BEV2LD <= 275 miles.
BEV3..............................  ~300-mile Battery Electric Vehicle
                                     275 miles < BEV3LD <= 350 miles.
BEV4..............................  ~400-mile Battery Electric Vehicle
                                     350 miles < BEV3LD.
FCEV..............................  Fuel Cell Electric Vehicle.
------------------------------------------------------------------------

          Table II-10--HDPUV Electrification Path Technologies
------------------------------------------------------------------------
            Technology                           Description
------------------------------------------------------------------------
SS12V.............................  12-Volt Stop-Start (Micro Hybrid-
                                     Electric Vehicle).
BISG..............................  48V Belt Mounted Integrated Starter/
                                     Generator (Mild Hybrid-Electric
                                     Vehicle).
SHEV-P2SGDIS......................  P2 Strong Hybrid-Electric Vehicle
                                     with a Single Over-Head Cam Engine
                                     and Gasoline Direct Injection.
PHEV50H \254\.....................  PHEV50 with a Single Over-Head Cam
                                     Engine and Gasoline Direct
                                     Injection and 50-mile All Electric
                                     Range.
BEV1..............................  Battery Electric Vehicle: 150-mile
                                     Range for Vans and 200-mile Range
                                     for Pickups.
BEV2..............................  Battery Electric Vehicle: 250-mile
                                     Range for Vans and 300-mile Range
                                     for Pickups.
FCEV..............................  Fuel Cell Electric Vehicle.
------------------------------------------------------------------------

    Readers familiar with previous LD CAFE analyses will notice that we
have increased the number of engine options available for strong
hybrid-electric vehicles and plug-in hybrid-electric vehicles. As
discussed above, this better represents the diversity of different
hybrid architectures and engine options available in the real world for
strong and PHEVs, while still maintaining a reasonable level of
analytical complexity. In addition, we now refer to the BEV options as
BEV1, 2, 3, and 4, rather than by their range assignments as in the
previous analysis, to accommodate using the same model code for the LD
and HDPUV analyses. Note that BEV1 and BEV2 have different range
assignments in the LD and HDPUV analyses; further, within the HDPUV
fleet, different range assignments exist for HD pickups and HD vans.
---------------------------------------------------------------------------

    \254\ Note that the HDPUV PHEV is labeled ``PHEV50H'' but that
is only so it can use the designated PHEV50H ``box'' on the
technology tree. The HDPUV PHEV engine is a basic single overhead
cam engine with GDI, as described in the table.
---------------------------------------------------------------------------

    In the CAFE Model, HDPUVs only have one strong hybrid engine/
powertrain option, and one PHEV option.\255\ The P2 architecture
supports high payload and high towing requirements versus other types
of hybrid architecture,\256\ which are important considerations for
HDPUV commercial operations. The mechanical connection between the
engine, transmission, and P2 hybrid systems enables continuous power
flow to be able to meet high towing weights and loads at the cost of
system efficiency. We do not allow engine downsizing in this setup in
so that when the battery storage system is depleted, the vehicle is
still able to operate. We picked the P2 strong hybrid architecture for
HDPUV PHEVs because although there are currently no PHEV HDPUVs in the
market to base a technology choice, we believe that the P2 strong
hybrid architecture would more likely be picked than other architecture
options. This is because, as discussed above, the P2 architecture ``can
be integrated with existing conventional powertrain systems that
already meet the additional attribute requirements of these large
vehicle segments.'' \257\
---------------------------------------------------------------------------

    \255\ Note that while the HDPUV PHEV option is labeled
``PHEV50H'' in the technology pathway, it actually uses a basic
engine. This is so the same technology pathway can be used in the LD
and HDPUV CAFE Model analyses.
    \256\ Kapadia, J. et al. 2017. Powersplit or Parallel--Selecting
the Right Hybrid Architecture. SAE International Journal of
Alternative Power 6(1): pp. 68-76. Available at: https://doi.org/10.4271/2017-01-1154. (Accessed: May 31, 2023) (Using current
powersplit design approaches, critical attribute requirements of
larger vehicle segments, including towing capability, performance
and higher maximum vehicle speeds, can be difficult and in some
cases impossible to meet. Further work is needed to resolve the
unique challenges of adapting powersplit systems to these larger
vehicle applications. Parallel architectures provide a viable
alternative to powersplit for larger vehicle applications because
they can be integrated with existing conventional powertrain systems
that already meet the additional attribute requirements of these
large vehicle segments).
    \257\ Kapadia, J. et al. 2017. Powersplit or Parallel--Selecting
the Right Hybrid Architecture. SAE International Journal of
Alternative Power 6(1): pp. 68-76. Available at: https://doi.org/10.4271/2017-01-1154. (Accessed: May 31, 2023).

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

    We only include one HDPUV PHEV option as there are no PHEVs in the
baseline HDPUV fleet, and there are no announcements from major
manufacturers that indicate this a pathway that they will pursue in the
short term.\258\ We believe this is in part because PHEVs, which are
essentially two separate powertrains combined, can decrease HDPUV
capability by increasing the curb weight of the vehicle and reducing
cargo capacity. A manufacturer's ability to use PHEVs in the HDPUV
segment is highly dependent on the load requirements and the duty cycle
of the vehicle. However, in the right operation, HDPUV PHEVs can have a
cost-effective advantage over their conventional
counterparts.259 260 261 More specifically, there would be a
larger fuel economy benefit the more the vehicle could rely on its
electric operation, with partial help from the ICE; examples of duty
cycles where this would be the case include short delivery applications
or construction trucks that drive between work sites in the same city.
Accordingly, we do think that PHEVs can be a technology option for
adoption in the rulemaking timeframe. We picked a 50-mile AER for this
segment based on discussions with experts at ANL, who were also
involved in DOE projects and provided guidance for this
segment.262 263
---------------------------------------------------------------------------

    \258\ We recognize that there are some third-party companies
that have converted HDPUVs into PHEVs, however, HDPUV incomplete
vehicles that are retrofitted with electrification technology in the
aftermarket are not regulated under this rulemaking unless the
manufacturer optionally chooses to certify them as a complete
vehicle. See 49 CFR 523.7.
    \259\ National Renewable Energy Laboratory. 2023. Electric and
Plug-in Hybrid Electric Vehicle Publications. Available at: https://www.nrel.gov/transportation/fleettest-publications-electric.html.
(Accessed: May 31, 2023).
    \260\ For the purpose of the Fuel Efficiency regulation, HDPUVs
are assessed on the 2-cycle test procedure similar to the LDVs. The
GVWR does not exceed 14,000 lbs in this segment.
    \261\ Birky, A. et al. 2017. Electrification Beyond Light Duty:
Class 2b-3 Commercial Vehicles. Final Report. ORNL/TM-2017/744.
Available at: https://doi.org/10.2172/1427632. (Accessed: May 31,
2023).
    \262\ DOE, Vehicle Technologies Office. 2023. 21st Century Truck
Partnership. Available at: https://www.energy.gov/eere/vehicles/21st-century-truck-partnership. (Accessed: May 31, 2023).
    \263\ Islam, E. et al. 2022. A Comprehensive Simulation Study to
Evaluate Future Vehicle Energy and Cost Reduction Potential. Final
Report. ANL/ESD-22/6.
---------------------------------------------------------------------------

    Additional information about each technology we considered is
located in Chapter 3.3.1 of the Draft TSD. We seek comment on the range
of HDPUV electrification path technologies.
    The full set of LD and HDPUV Electrification Path and Hybrid/
Electric Paths Collection technologies are shown in Figure II-21 and
Figure II-22 below, respectively.
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[GRAPHIC] [TIFF OMITTED] TP17AU23.031

BILLING CODE 4910-59-C
    We assigned electrification technologies to vehicles in the
baseline LD and HDPUV fleets using manufacturer-submitted CAFE
compliance information, publicly available technical specifications,
marketing brochures, articles from reputable media outlets, and data
from Wards Intelligence.\264\ Table II-11 and Table II-12 below show
the baseline penetration rates of electrification technologies in the
LD and HDPUV fleets, respectively. Over half the LD fleet has some
level of electrification, with the vast majority--over 50 percent of
the fleet--being micro hybrids; BEV3 (>275 miles; <=350 miles) is the
most common LD BEV technology. The HDPUV fleet has 6.22 percent level
of electrification with BEV2s (>150 miles; <=250 miles) representing
all of the electrified vehicles in that fleet, with the remaining
having a conventional non-electrified powertrain.
---------------------------------------------------------------------------

    \264\ Wards Intelligence. 2022. U.S. Car and Light Truck
Specifications and Prices, '22 Model Year. Available at: https://wardsintelligence.informa.com/WI966023/US-Car-and-Light-Truck-Specifications-and-Prices-22-Model-Year. (Accessed: May 31, 2023).

                Table II-11--Electrification Technology Penetration Rates in the MY 2022 LD Fleet
----------------------------------------------------------------------------------------------------------------
                                                         Sales volume  with this      Penetration rate  in 2022
             Electrification technology                        technology                baseline fleet (%)
----------------------------------------------------------------------------------------------------------------
None................................................                     4,244,826                         29.52
SS12V...............................................                     7,569,293                         52.63
BISG................................................                       521,786                          3.63
P2..................................................                       245,778                          1.71
SHEVPS..............................................                       745,535                          5.18
PHEV20PS............................................                        31,966                          0.22
PHEV20H.............................................                        50,643                          0.35
PHEV20T.............................................                       132,181                          0.92
PHEV50PS............................................                             0                         0.000
PHEV50H.............................................                        27,776                          0.19
PHEV50T.............................................                           200                         0.001
BEV1................................................                        45,754                          0.32
BEV2................................................                       233,631                          1.62
BEV3................................................                       335,244                          2.33
BEV4................................................                       129,860                          0.90
FCEV................................................                         4,419                          0.03
                                                     -----------------------------------------------------------
    Total...........................................                    14,380,891                           100
----------------------------------------------------------------------------------------------------------------

              Table II-12--Electrification Technology Penetration Rates in the Baseline HDPUV Fleet
----------------------------------------------------------------------------------------------------------------
                                                         Sales volume  with this        Penetration rate  in
             Electrification technology                        technology                baseline fleet  (%)
----------------------------------------------------------------------------------------------------------------
None................................................                       822,409                         93.78
SS12V...............................................                             0                          0.00
BISG................................................                             0                          0.00
P2..................................................                             0                          0.00
PHEV50H.............................................                             0                          0.00
BEV1................................................                             0                          0.00
BEV2................................................                        54,508                          6.22
FCEV................................................                             0                          0.00
                                                     -----------------------------------------------------------
    Total...........................................                       876,917                           100
----------------------------------------------------------------------------------------------------------------

    Like the other technology pathways, as the CAFE Model adopts
electrification technologies for vehicles, more advanced levels of
hybridization or electrification technologies will supersede all prior
levels, while certain technologies within each level are mutually
exclusive. The only adoption feature applicable to micro (SS12V) and
mild (BISG) hybrid technology is path logic; vehicles can only adopt
micro and mild hybrid technology if the vehicle did not already have a
more advanced level of electrification.
    The adoption features that we apply to strong hybrid technologies
include path logic, powertrain substitution, and vehicle class
restrictions. Per the defined (applicable) technology pathways, SHEVPS,
P2x, P2TRBx, and the P2HCRx 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 technologies, when
applicable in the modeling scenario (i.e., allowed in the model).
    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 advanced 8-
speed automatic transmission (AT8L2), and PS hybrids adopt a
continuously variable transmission via power-split device (eCVT). When
the model applies the P2 technology, the model can consider various
engine options to pair with the P2 architecture according to existing
engine path constraints--taking into account relative cost
effectiveness. For SHEVPS technology, the existing engine is replaced
with a full time Atkinson cycle engine.\265\ For P2s, we picked the 8-
speed automatic transmission to supersede the vehicle's incoming
transmission technology. This is because most P2s in the market use an
8-speed automatic transmission,\266\ therefore it is representative of
the fleet now. We also think that 8-speed transmissions are
representative of the transmissions that will continue to be used in
these hybrid vehicles, as we anticipate manufacturers will continue to
use these ``off the shelf'' transmissions based on availability and
ease of incorporation in the powertrain. The eCVT (power-split device)
is the transmission for SHEVPSs and is therefore the technology we
picked to supersede the vehicle's prior transmission when adopting the
SHEVPS powertrain.
---------------------------------------------------------------------------

    \265\ Designated Eng26 in the list of engine map models used in
the analysis. See Draft TSD Chapter <IP_ADDRESS>.3 for more information.
    \266\ We are aware that some Hyundai vehicles use a 6-speed
transmission and some Ford vehicles use a 10-speed transmission, but
on balance we have observed that the majority of P2s use an 8-speed
transmission.
---------------------------------------------------------------------------

    SKIP logic is also used to constrain adoption for SHEVPS and
PHEV20/50PS technologies. These technologies are ``skipped'' for
vehicles with engines \267\ that meet one of the following conditions:
the engine belongs to an excluded manufacturer; \268\ 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. The reasons for these conditions are similar to those for the
SKIP logic that we apply to HCR engine technologies, discussed in more
detail in Section II.D.1. In the real world, performance vehicles with
certain powertrain configurations cannot adopt the technologies listed
above and maintain vehicle performance without redesigning the entire
powertrain. It may be helpful to understand why we do not apply SKIP
logic to P2s and to understand why we do apply SKIP logic to SHEVPSs.
Remember the difference between P2 and SHEVPS architectures: P2
architectures are better for ``larger vehicle applications because they
can be integrated with existing conventional powertrain systems that
already meet the additional attribute requirements'' of large vehicle
segments.\269\ No SKIP logic applies to P2s because we believe that
this type of electrified powertrain is sufficient to meet all of the
performance requirements for all types of vehicles. Manufacturers have
proven this now with vehicles like the Ford F-150 Hybrid and Toyota
Tundra Hybrid.270 271 In contrast, ``[a] disadvantage of the
power split architecture is that when towing or driving under other
real-world conditions, performance is not optimum.'' \272\ If we were
to size (in the Autonomie simulations) the PS motors and engines to
achieve not ``not optimum'' performance, the electric motors would be
unrealistically large (on both a size and cost basis), and the
accompanying engine would also have to be a very large displacement
engine, which is not characteristic of how vehicle manufacturers apply
PS IC engines in the real-world. Instead, for vehicle applications that
have particular performance requirements--defined in our analysis as
vehicles with engines that belong to an excluded

manufacturer, engines belonging to a pickup truck or shared with a
pickup truck, or the engine's peak horsepower is more than 405hp--those
vehicles can adopt P2 architectures that should be able to handle the
vehicle's performance requirements.
---------------------------------------------------------------------------

    \267\ This refers to the engine assigned to the vehicle in the
2022 baseline fleet.
    \268\ Excluded manufacturers included BMW, Daimler, and Jaguar
Land Rover.
    \269\ Kapadia, J. et al. 2017. Powersplit or Parallel--Selecting
the Right Hybrid Architecture. SAE International Journal of
Alternative Power 6(1). Available at: https://doi.org/10.4271/2017-01-1154. (Accessed: May 31, 2023).
    \270\ SAE International. 2021. 2022 Toyota Tundra: V8 Out, Twin-
Turbo Hybrid Takes Over. Last revised: September 22, 2021. Available
at: https://www.sae.org/news/2021/09/2022-toyota-tundra-gains-twin-turbo-hybrid-power. (Accessed: May 30, 2023).
    \271\ SAE International. 2020. Hybridization the Highlight of
Ford's All-New 2021 F-150. Last revised: June 30, 2020. Available
at: https://www.sae.org/news/2020/06/2021-ford-f-150-reveal.
(Accessed: May 30, 2023).
    \272\ 2015 NAS report, at 134.
---------------------------------------------------------------------------

    LD PHEV adoption is limited only by technology path logic; however,
in the HDPUV analysis, PHEV technology is not available in the model
until MY 2025 for HD vans and MY 2027 for HD pickups. As discussed
above, there are no PHEVs in the baseline HDPUV fleet and there are no
announcements from major manufacturers that indicate this a pathway
that they will pursue in the short term; that said, we do believe this
is a technology that could be beneficial for very specific HDPUV
applications. However, the technology is fully available for adoption
by HDPUVs in the rulemaking timeframe (i.e., MYs 2030 and beyond). Note
that we also conducted two sensitivity cases varying the year that
HDPUV PHEVs are available in the model, allowing them to be introduced
in MYs 2025 and 2030. PRIA Chapter 9 shows that under the ``PHEV
available in MY 2025'' sensitivity case, there are approximately double
(19.6 percent versus 9.1%) the number of PHEVs in the no-action
sensitivity case compared to the no-action central case and no-action
``PHEV available in MY 2030'' case by MY 2038. However, in response to
CAFE standards, PHEVs increase in all three cases by 1.5 percent. This
results in functionally no difference in total SCs, total social
benefits, and accordingly net social benefits from varying the HDPUV
PHEV availability year, in addition to functionally no difference in
gasoline consumption, CO2 emissions, and other economic and
environmental parameters. We seek comment on this assumption, and any
other information available from manufacturers or other stakeholders on
the potential that original equipment manufacturers will implement PHEV
technology prior to MY 2025 for HD vans, and prior to MY 2027 for HD
pickups.
    The engine and transmission technologies on a vehicle are
superseded when PHEV technologies are applied. For example, the model
applies an AT8L2 transmission with all PHEV20T/50T plug-in
technologies, and the model applies an eCVT transmission for all
PHEV20PS/50PS and PHEV20H/50H plug-in technologies. A vehicle adopting
PHEV20PS/50PS receives a hybrid full Atkinson cycle engine, and a
vehicle adopting PHEV20H/PHEV50H receives an HCR engine. For PHEV20T/
50T, the vehicle receives a TURBO1 engine.
    Adoption of BEVs and FCEVs is limited by both path logic and phase-
in caps. They are applied as end-of-path technologies that supersede
previous levels of electrification. Phase-in caps, which are defined in
the CAFE Model Input Files, are 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 BEV1 costs less and has slightly higher effectiveness
values than other advanced electrification technologies,\273\ the model
will have vehicles adopt it first, until it is restricted by the phase-
in cap. Table II-13 shows the phase-in caps, phase-in year, and maximum
penetration rate through 2050 for BEV and FCEV technologies. For
comparison, we also list the actual penetration rate of each technology
in the 2022 baseline fleet in the fourth column from the left.
---------------------------------------------------------------------------

    \273\ This is because BEV1 uses fewer batteries and weighs less
than BEVs with greater ranges.
[GRAPHIC] [TIFF OMITTED] TP17AU23.032

    The LD BEV1 phase-in cap is informed by manufacturers' tendency to
move away from low-range passenger vehicle offerings in part because of
potential consumer concern with range anxiety.274 275 276 In
some cases, the advertised range on EVs may not reflect the actual
real-world range in cold and hot ambient temperatures and real-world
driving conditions, affecting the utility of these lower range
vehicles.\277\ Many manufacturers have told us that the portion of
consumers willing to accept a vehicle with the lowest modeled range is
small, with manufacturers targeting range values above BEV1 range.
---------------------------------------------------------------------------

    \274\ Pratt, D. 2021. How Much Do Cold Temperatures Affect an
Electric Vehicle's Driving Range? Last Revised: Dec. 19, 2021.
Available at: https://www.consumerreports.org/hybrids-evs/how-much-do-cold-temperatures-affect-an-evs-driving-range-a5751769461.
(Accessed: May 31, 2023).
    \275\ 2022 EPA Trends Report at page 60.
    \276\ IEA. 2022. Trends in Electric Light-Duty Vehicles.
Available at: https://www.iea.org/reports/global-ev-outlook-2022/trends-in-electric-light-duty-vehicles. (Accessed: May 31, 2023).
    \277\ AAA. 2019. AAA Electric Vehicle Range Testing. Last
Revised: Feb. 2019. Available at: https://www.aaa.com/AAA/common/AAR/files/AAA-Electric-Vehicle-Range-Testing-Report.pdf. (Accessed:
May 31, 2023).
---------------------------------------------------------------------------

    Furthermore, the average BEV range has steadily increased over the
past decade,\278\ due to battery technological progress increasing
energy density as well as batteries becoming more cost effective. EPA
observed in its 2022 Automotive Trends Report that ``the average range
of new EVs has climbed substantially. In MY 2021, the average new EV is
projected to have a 298-mile range, or about four times the range of an
average EV in 2011.'' \279\ Based on the cited examples and basis
described in this section, the maximum growth rate for LD BEV1s 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 BEV1 phase-in cap allows the penetration rate of low-
range BEVs to grow by a multiple of what is currently observed in the
market.
---------------------------------------------------------------------------

    \278\ 2022 EPA Automotive Trends Report, at p. 62, Figure 4.17.
See also United States DOE Vehicle Technologies Office Fact of the
Week (FOTW) #1290, In Model Year 2022, the Longest-Range EV Reached
520 Miles on a Single Charge (May 15, 2023). Available at https://www.energy.gov/eere/vehicles/articles/fotw-1290-may-15-2023-model-year-2022-longest-range-ev-reached-520-miles. (Accessed: May 31,
2023).
    \279\ 2021 EPA Automotive Trends Report, at p. 58 (citing DOE,
Vehicle Technologies Office. FOTW #1234, April 18, 2022: Volumetric
Energy Density of Lithium-ion Batteries Increased by More than Eight
Times Between 2008 and 2020. Available at: https://www.energy.gov/eere/vehicles/articles/fotw-1234-april-18-2022-volumetric-energy-density-lithium-ion-batteries. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    For higher BEV ranges (such as that for BEV2 for both LD and
HDPUVs), 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 material acquisition and manufacturing.\280\ Incorporating
battery packs with the capacity to provide greater electric range also
poses its own engineering challenges. Heavy batteries and large packs
may be difficult to integrate for many vehicle configurations and
require vehicle structure 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. In the LD analysis, we use the LD BEV3 and BEV4 phase-in
caps to reflect these transitional challenges and use similar phase-in
caps for the HDPUV analysis.
---------------------------------------------------------------------------

    \280\ See, e.g., Henze, V. 2022. China's Battery Supply Chain
Tops BNEF Ranking for Third Consecutive Time, with Canada a Close
Second. Bloomberg New Energy Finance. Last Revised: Nov. 12, 2022.
Available at: https://about.bnef.com/blog/chinas-battery-supply-chain-tops-bnef-ranking-for-third-consecutive-time-with-canada-a-close-second/. (Accessed: May 31, 2023).
---------------------------------------------------------------------------

    We seek comment on the BEV phase-in caps for the LD and HDPUV
analyses. Remember when submitting comments that BEV phase-in caps are
a tool that we use in the model to allow the model to build higher-
range BEVs (when the modeling scenario allows, as in outside of
standard-setting years), because if we did not, the model would only
build BEV1s, as they are the most cost-effective BEV technology. Based
on the analysis provided above, we believe there is a reasonable
justification for different BEV phase-in caps based on expected BEV
ranges in the future.
    The phase-in cap for FCEVs is assigned based on existing market
share as well as historical trends in FCEV production for LD and HDPUV.
FCEV production share in the past five years has been extremely low and
the lack of fueling infrastructure remains a limiting factor \281\--we
set the phase-in cap accordingly.\282\ As with BEV1, however, the
phase-in cap still allows for the market share of FCEVs to grow several
times over.
---------------------------------------------------------------------------

    \281\ DOE, Alternative Fuels Data Center. Hydrogen Refueling
Infrastructure Development. Available at: https://afdc.energy.gov/fuels/hydrogen_infrastructure.html. (Accessed: May 31, 2023).
    \282\ 2022 EPA Automotive Trends Report, at p. 60, Figure 4.14.
---------------------------------------------------------------------------

    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. The
components, or building blocks, that contribute to the effectiveness of
an electrified powertrain in the analysis include the vehicle's
battery, electric motors, power electronics, and accessory loads.
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 architecture.
For example, Autonomie determines a BEV's overall efficiency by
considering the efficiencies of the battery (including charging
efficiency), the electric traction drive system (the electric machine
and power electronics), and mechanical power transmission devices.\283\
Or, for a PHEV, Autonomie combines a very similar set of components to
model the electric portion of the hybrid powertrain and then also
includes the ICE and related power for transmission components.\284\
ANL 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 act as surrogates predicting representative levels of
effectiveness for each electrification technology. We discuss the
procedures for modeling each of these sub-systems in detail in the
Draft TSD and in the CAFE Analysis Autonomie Documentation and include
a brief summary below.
---------------------------------------------------------------------------

    \283\ Iliev, S. et al. 2023. Vehicle Technology Assessment,
Model Development, and Validation of a 2021 Toyota RAV4 Prime.
Report No. DOT HS 813 356. National Highway Traffic Safety
Administration.
    \284\ See the CAFE Analysis Autonomie Documentation.
---------------------------------------------------------------------------

    The fundamental components of an electrified powertrain's
propulsion system--the electric motor and inverter--ultimately
determine the vehicle's performance and efficiency. For this analysis,
Autonomie employed a set of electric motor efficiency maps created by
Oak Ridge National Laboratory (ORNL), one for a traction motor and an
inverter, the other for a motor/generator and inverter.\285\ Autonomie
also uses test data validations from technical publications to
determine the peak efficiency of BEVs

and FCEVs. The electric motor efficiency maps, created from production
vehicles like the 2007 Toyota Camry hybrid, 2011 Hyundai Sonata hybrid,
and 2016 Chevrolet Bolt, represent electric motor efficiency as a
function of torque and motor Rotations Per Minute (RPM). These
efficiency maps provide nominal and maximum speeds, as well as a
maximum torque curve. ANL uses the maps to determine the efficiency
characteristics of the motors, which includes some of the losses due to
power transfer through the electric machine.\286\ Specifically, ANL
scales the efficiency maps, specific to powertrain type, to have total
system peak efficiencies ranging from 96-98 percent \287\--such that
their peak efficiency value corresponds to the latest state-of-the-art
technologies, opposed to retaining dated system efficiencies (90-93
percent).\288\
---------------------------------------------------------------------------

    \285\ Oak Ridge National Laboratory. 2008. Evaluation of the
2007 Toyota Camry Hybrid Synergy Drive System; Oak Ridge National
Laboratory. 2011. Annual Progress Report for the Power Electronics
and Electric Machinery Program.
    \286\ CAFE Analysis Autonomie Documentation chapter titled
``Vehicle and Component Assumptions--Electric Machines--Electric
Machine Efficiency Maps.''
    \287\ CAFE Analysis Autonomie Documentation chapter titled
``Vehicle and Component Assumptions--Electric Machines--Electric
Machine Peak Efficiency Scaling.''
    \288\ Oak Ridge National Laboratory. 2008. Evaluation of the
2007 Toyota Camry Hybrid Synergy Drive System; Oak Ridge National
Laboratory. 2011. Annual Progress Report for the Power Electronics
and Electric Machinery Program.
---------------------------------------------------------------------------

    Beyond the powertrain components, Autonomie also considers electric
accessory devices that consume energy and affect overall vehicle
effectiveness, such as headlights, radiator fans, wiper motors, engine
control units, transmission control units, cooling systems, and safety
systems. In real-world driving and operation, the electrical accessory
load on the powertrain varies depending on how the driver uses certain
features and the condition in which the vehicle is operating, such as
for night driving or hot weather driving. However, for regulatory test
cycles related to fuel economy, the electrical load is repeatable
because the fuel economy regulations control for these factors.
Accessory loads during test cycles do vary by powertrain type and
vehicle technology class, since distinctly different powertrain
components and vehicle masses will consume different amounts of energy.
    The baseline fleet consists of different vehicle types with varying
accessory electrical power demand. For instance, vehicles with
different motor and battery sizes will require different sizes of
electric cooling pumps and fans to optimally manage component
temperatures. Autonomie has built-in models that can simulate these
varying sub-system electrical loads. However, for this analysis, we use
a fixed (by vehicle technology class and powertrain type), constant
power draw to represent the effect of these accessory loads on the
powertrain on the 2-cycle test. We intend and expect that fixed
accessory load values will, on average, have similar impacts on
effectiveness as found on actual manufacturers' systems. This process
is in line with the past analyses.289 290 For this analysis,
we aggregate electrical accessory load modeling assumptions for the
different powertrain types (electrified and conventional) and
technology classes (both LD and HDPUV) from data from the Draft TAR,
EPA Proposed Determination,\291\ data from manufacturers,\292\ research
and development data from DOE's Vehicle Technologies
Office,293 294 295 and DOT-sponsored vehicle benchmarking
studies completed by ANL's AMTL.
---------------------------------------------------------------------------

    \289\ Technical Assessment Report (July 2016), Chapter 5.
    \290\ EPA Proposed Determination TSD (November 2016), at pp. 2-
270.
    \291\ EPA Proposed Determination TSD (November 2016), at pp. 2-
270.
    \292\ Alliance of Automobile Manufacturers (now Alliance for
Automotive Innovation) Comments on Draft TAR, at p. 30.
    \293\ DOE, Vehicle Technologies Office. Electric Drive Systems
Research and Development. Available at: https://www.energy.gov/eere/vehicles/vehicle-technologies-office-electric-drive-systems.
(Accessed: May 31, 2023).
    \294\ ANL. 2023. Advanced Mobility Technology Laboratory (AMTL).
Available at: https://www.anl.gov/es/advanced-mobility-technology-laboratory. (Accessed: May 31, 2023).
    \295\ DOE's lab years are ten years ahead of manufacturers'
potential production intent (e.g., 2020 Lab Year is MY 2030).
---------------------------------------------------------------------------

    Certain technologies' effectiveness for reducing fuel consumption
requires optimization through the appropriate sizing of the powertrain.
Autonomie uses sizing control algorithms based on data collected from
vehicle benchmarking,296 297 and the modeled electrification
components are sized based on performance neutrality considerations.
This analysis iteratively minimizes the size of the powertrain
components to maximize efficiency while enabling the vehicle to meet
multiple performance criteria. The Autonomie simulations use a series
of resizing algorithms that contain ``loops,'' such as the acceleration
performance loop (0-60 mph), which automatically adjusts the size of
certain powertrain components until a criterion, like the 0-60 mph
acceleration time, is met. As the algorithms examine different
performance or operational criteria that must be met, no single
criterion can degrade; once a resizing algorithm completes, all
criteria will be met, and some may be exceeded as a necessary
consequence of meeting others.
---------------------------------------------------------------------------

    \296\ CAFE Analysis Autonomie Documentation chapter titled
``Vehicle Sizing Process--Vehicle Powertrain Sizing Algorithms--
Light-Duty Vehicles--Conventional Vehicle Sizings Algorithm.''
    \297\ CAFE Analysis Autonomie Documentation chapter titled
``Vehicle Sizing Process--Vehicle Powertrain Sizing Algorithms--
Heavy-Duty Pickups and Vans--Conventional Vehicle Sizings
Algorithm.''
    \298\ Enviromental Protection Agency. 2023. How Vehicles are
Tested. Available at: https://www.fueleconomy.gov/feg/how_tested.shtml. (Accessed: May 31, 2023).
    \299\ CAFE Analysis Autonomie Documentation, Chapter titled
`Test Procedure and Energy Consumption Calculations'.
    \300\ EPA. 2017. EPA Test Procedures for Electric Vehicles and
Plug-in Hybrids. Draft Summary. Available at: https://www.fueleconomy.gov/feg/pdfs/EPA%20test%20procedure%20for%20EVs-PHEVs-11-14-2017.pdf. (Accessed: May 31, 2023).
    \301\ 40 CFR part 600.
---------------------------------------------------------------------------

    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.298 299 300 For vehicles with conventional
powertrains and micro hybrid powertrains, Autonomie simulates the
vehicles using the 2-cycle test procedures and guidelines.\301\ For
mild HEVs, strong HEVs, and FCEVs, Autonomie simulates the same 2-cycle
test, with the addition of repeating the drive cycles until the final
State of charge (SOC) is approximately the same as the initial SOC, a
process described in SAE J1711. For PHEVs, Autonomie simulates vehicles
performing the test cycles per

guidance provided in SAE J1711.\302\ PHEVs have a different range of
modeled effectiveness during ``standard setting'' CAFE Model runs, in
which the PHEV operates under a ``charge sustaining'' mode (similar to
how SHEVs function) compared to ``EIS'' runs, in which the same PHEV
operates under a ``charge depleting'' mode (similar to how BEVs
function). For BEVs and FCEVs, Autonomie simulates vehicles performing
the test cycles per guidance provided in SAE J1634.\303\
---------------------------------------------------------------------------

    \302\ PHEV testing is broken into several phases based on SAE
J1711: charge-sustaining on the city and HWFET cycle, and charge-
depleting on the city and HWFET cycles.
    \303\ SAE J1634. Battery Electric Vehicle Energy Consumption and
Range Test Procedure. July 12, 2017.
---------------------------------------------------------------------------

    Chapters 2.4 and 3.3 of the Draft TSD and the CAFE Analysis
Autonomie Documentation chapter titled ``Test Procedure and Energy
Consumption Calculations'' discuss the components and test cycles used
to model each electrified powertrain type; please refer to those
chapters for more technical details on each of the modeled technologies
discussed in this section.
    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. 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 measured values, depending on
the level of similarity between the modeled hardware configuration and
the real-world hardware and software configurations. The range of
effectiveness values for the electrification technologies applied in
the LD fleets are shown in Figure II-23 and Figure II-24. Effectiveness
values for electrification technologies in the HDPUV fleet are shown in
Figure II-25.
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BILLING CODE 4910-59-C
    When the CAFE Model turns a vehicle powered by an ICE into an
electrified vehicle, it must remove the parts and costs associated with
the ICE (and, potentially, the transmission) and add the costs of a
battery pack and other non-battery electrification components, such as
the electric motor and power inverter. To estimate battery pack costs
for this analysis, we need an estimate of how much battery packs cost
now (i.e., a ``base year'' cost), and estimates of how that cost could
reduce over time (i.e., the ``learning effect.''). The general concept
of learning effects is discussed in detail in Section II.C and in
Chapter 2 of the Draft TSD, while the specific LE we applied to battery
pack costs in this analysis is discussed below. We estimate base year
battery pack costs for most electrification technologies using BatPaC,
which is an ANL model designed to calculate the cost of EV battery
packs.
    Traditionally, a user would use BatPaC to cost a battery pack for a
single vehicle, and the user would vary factors such as battery cell
chemistry, battery power and energy, battery pack interconnectivity
configurations, battery pack production volumes, and/or charging
constraints, just to name a few, to see how those factors would
increase or decrease the cost of the battery pack. However, several
hundreds of thousands of simulated vehicles in our analysis have
electrified powertrains, meaning that we would have to run individual
BatPaC simulations for each full vehicle simulation that requires a
battery pack. This would have been computationally intensive and
impractical. Instead, ANL staff builds ``lookup tables'' with BatPaC
that provide battery pack manufacturing costs, battery pack weights,
and battery pack cell capacities for vehicles with varying power
requirements modeled in our large-scale simulation runs.
    Just like with other vehicle technologies, the specifications of
different vehicle manufacturer's battery packs are extremely diverse.
We, therefore, endeavored to develop battery pack costs that reasonably
encompass the cost of battery packs for vehicles in each technology
class. Two BatPaC assumptions are of note when generating base year
battery costs: (1)

battery cell chemistry and (2) battery plant production volume.
    In conjunction with our partners at ANL working on the CAFE
analysis Autonomie modeling, we referenced EV outlook
reports,304 305 306 vehicle teardown
reports,307 308 and stakeholder discussions \309\ to
determine common battery pack chemistries for each modeled
electrification technology. The CAFE Analysis Autonomie Documentation
chapter titled ``Battery Performance and Cost Model--BatPac Examples
from Existing Vehicles in the Market'' includes more detail about the
reports referenced for this analysis.\310\ For mild hybrids, we used
the LFP-G \311\ chemistry because power and energy requirements for
mild hybrids are very low, the charge and discharge cycles (or need for
increased battery cycle life) are high, and the battery raw materials
are much less expensive than a nickel manganese cobalt (NMC)-based cell
chemistry. We used NMC622-G \312\ for all other electrified vehicle
technology initial battery pack cost calculations. While we made this
decision at the time of modeling based on the best available
information, while also considering feedback on prior rules,\313\ more
recent data affirms that EV batteries using NMC622 cathode chemistries
are still a significant part of the market.\314\ We recognize there is
ongoing research and development with battery cathode chemistries that
may have the potential to reduce costs and increase battery
capacity.315 316 317 318 In particular, we are aware of a
recent shift by manufacturers to transition to lithium iron phosphate
(LFP) chemistry-based battery packs as prices for materials used in
battery cells fluctuate (see additional discussion below); however, we
believe that based on available data,\319\ NMC622 is more
representative for our MY 2022 base year battery costs than LFP, and
any additional cost reductions from manufacturers switching to LFP
chemistry-based battery packs in years beyond 2022 are accounted for
through LEs. As a reminder, in this analysis, we account for the
potential cost savings for future battery cell chemistries using a
learning rate applied to the battery pack DMC. As discussed above, the
battery chemistry we use is intended to reasonably represent what is
used in U.S. battery manufacturing in MY 2022, the DMC base year for
our BatPaC calculations.
---------------------------------------------------------------------------

    \304\ RhoMotion. 2023. Emerging Battery Technology Forum.
Available at: https://rhomotion.com/rho-motion-seminar-series-live-q1-2023-seminar-recordings. (Accessed: May 31, 2023).
    \305\ Bibra, E. et al. 2022. Global EV Outlook 2022--Securing
Supplies For an Electric Future. International Energy Agency.
Available at: https://iea.blob.core.windows.net/assets/ad8fb04c-4f75-42fc-973a-6e54c8a4449a/GlobalElectricVehicleOutlook2022.pdf.
(Accessed: May 31, 2023).
    \306\ Bloomberg New Energy Finance. 2023. Electric Vehicle
Outlook 2023. Available at: https://about.bnef.com/electric-vehicle-outlook/. (Accessed: May 31, 2023).
    \307\ Hummel, P. et al. 2017. UBS Evidence Lab Electric Car
Teardown--Disruption Ahead?. UBS. Available at: https://neo.ubs.com/shared/d1ZTxnvF2k. (Accessed: May 31, 2023).
    \308\ A2Mac1: Automotive Benchmarking. (Proprietary data).
Available at: https://portal.a2mac1.com/. (Accessed: May 31, 2023).
    \309\ See Docket Submission of Ex Parte Meetings Prior to
Publication of the Corporate Average Fuel Economy Standards for
Passenger Cars and Light Trucks for Model Years 2027-2032 and Fuel
Efficiency Standards for Heavy-Duty Pickup Trucks and Vans for Model
Years 2030-2035 Notice of Proposed Rulemaking memorandum, which can
be found under References and Supporting Material in the rulemaking
Docket No. NHTSA-2023-0022.
    \310\ CAFE Analysis Autonomie Documentation chapter titled
``Battery Performance and Cost Model--BatPac Examples from Existing
Vehicles in the Market.''
    \311\ Lithium Iron Phosphate (LiFePO4) cathode and
Graphite anode.
    \312\ Lithium Nickel Manganese Cobalt Oxide
(LiNiMnCoO2) cathode and Graphite anode.
    \313\ Stakeholders had commented on both the 2020 and 2022 final
rules that batteries using NMC811 chemistry had either recently come
into or were imminently coming into the market, and therefore we
should have selected NMC811 as the appropriate chemistry for
modeling battery pack costs.
    \314\ Rho Motion. Seminar Series Live, Q1 2023--Seminar
Recordings. ``Emerging Battery Technology Forum'' February 7, 2023.
Available at: https://rhomotion.com/rho-motion-seminar-series-live-q1-2023-seminar-recordings. (Accessed: May 31, 2023). More
specifically, the monthly weighted average global EV battery cathode
chemistry across all vehicle classes shows that 19% use NMC622 and
20% use NMC811+, representing a fairly even split. Even though we
considered domestic battery production rather than global battery
production for the analysis supporting this proposal, NMC622 is
still prevalent even at a global level. Note that this seminar video
is no longer publicly available to non-subscribers.
    \315\ Slowik, P. et. al. 2022. Assessment of Light-Duty Electric
Vehicle Costs and Consumer Benefits in the United States in the
2022-2035 Time Frame. International Council on Clean Transportation.
Available at: https://theicct.org/wp-content/uploads/2022/10/ev-cost-benefits-2035-oct22.pdf. (Accessed: May 31, 2023).
    \316\ Batteries News. 2022. ``Solid-State NASA Battery Beats The
Model Y 4680 Pack at Energy Density by Stacking all Cells in One
Case.'' October 20, 2022. Available at: https://batteriesnews.com/solid-state-nasa-battery-beats-model-y-4680-pack-energy-density-stacking-cells-one-case/. (Accessed: February 1, 2023).
    \317\ Sagoff, J. 2023. Scientists develop more humane,
environmentally friendly battery material. ANL. Available at:
https://www.anl.gov/article/scientists-develop-more-humane-environmentally-friendly-battery-material. (Accessed: May 31, 2023).
    \318\ International Energy Agency. Global EV Outlook 2023. April
2023. Available at https://www.iea.org/reports/global-ev-outlook-2023. (Accessed: May 31, 2023).
    \319\ International Energy Agency. Global EV Outlook 2023. April
2023. Available at https://www.iea.org/reports/global-ev-outlook-2023. (Accessed: May 31, 2023). As of IEA's 2023 Global EV Outlook
report, ``around 95% of the LFP batteries for electric LDVs went to
vehicles produced in China, and BYD [a Chinese EV manufacturer]
alone represents 50% of demand. Tesla accounted for 15%, and the
share of LFP batteries used by Tesla increased from 20% in 2021 to
30% in 2022. Around 85% of the cars with LFP batteries manufactured
by Tesla were manufactured in China, with the remainder being
manufactured in the United States with cells imported from China. In
total, only around 3% of electric cars with LFP batteries were
manufactured in the United States in 2022.'' This is not to say that
as of 2022 there were no current production or use of vehicle
battery packs with LFP-based chemistries in the U.S., but rather
that based on available data, we are more certain that NMC622 was a
reasonable chemistry selection for our 2022 base year battery costs.
---------------------------------------------------------------------------

    We also looked at vehicle sales volumes in MY 2022 to determine a
reasonable base production volume assumption.\320\ In practice, a
single battery plant can produce packs using different cell chemistries
with different power and energy specifications, as well as battery pack
constructions with varying battery pack designs--different cell
interconnectivities (to alter overall pack power end energy) and
thermal management strategies--for the same base chemistry. However, in
BatPaC, a battery plant is assumed to manufacture and assemble a
specific battery pack design, and all cost estimates are based on one
single battery plant manufacturing only that specific battery pack. For
example, if a manufacturer has more than one BEV and each uses a
specific battery pack design, a BatPaC user would include manufacturing
volume assumptions for each design separately to represent each plant
producing each specific battery pack. As a consequence, we examined
battery pack designs for vehicles sold in MY 2022 to determine a
reasonable manufacturing plant production volume assumption. We
considered each assembly line designed for a specific battery pack and
for a specific BEV as an individual battery plant. Since battery
technologies are still evolving, it is likely to be some time before
battery cells can be treated as commodity where the specific numbers of
cells are used for varying battery pack applications and all other
metrics remain the same.
---------------------------------------------------------------------------

    \320\ See Chapter <IP_ADDRESS> of the Draft TSD for more information
on data we use for MY 2022 sales volumes.
---------------------------------------------------------------------------

    Similar to previous rulemakings, we used BEV sales as a starting
point to analyze potential base modeled battery manufacturing plant
production volume assumptions. Since actual production data for
specific battery manufacturing plants are extremely hard to obtain and
the battery cell manufacturer is not always the battery pack
manufacturer,\321\ we calculated an

average production volume per manufacturer metric to approximate BEV
production volumes for this analysis. This metric was calculated by
taking an average of all manufacturer's battery energy across all BEVs
reported in vehicle manufacturer's PMY 2022 reports \322\ and dividing
by the averaged sales-weighted energy per-vehicle; the resulting volume
was then rounded to the nearest 5,000. Manufacturers are not required
to report gross battery pack sizes for the PMY report, so we estimated
pack size for each vehicle based on publicly available data, like
manufacturer's announcements. This process was repeated for all other
electrified vehicle technologies. We believe this gave us a reasonable
base year plant production volume--especially in the absence of actual
production data--since the PMY data from manufacturers already includes
accurate related data, such as vehicle model and sales information
metrics.\323\ An example calculation below, in Table II-14 and Equation
II-7 and Equation II-8, outline how the sales-weighted energy per
vehicle production volume estimates are calculated with Table II-14
showing several example BEV models, their production volumes, and pack
energy that are representative of industry today.
---------------------------------------------------------------------------

    \321\ Lithium-Ion Battery Supply Chain for E-Drive Vehicles in
the United States: 2010-2020, ANL/ESD-21/3.
    \322\ 49 CFR 537.7.
    \323\ NHTSA used publicly available range and pack size
information and linked the information to vehicle models.

                                  Table II-14--Example BEV Model Battery Packs
----------------------------------------------------------------------------------------------------------------
                                                                                Production      Battery
      Electrification level              Vehicle make            Vehicle model           volume      pack energy
----------------------------------------------------------------------------------------------------------------
BEV..............................  Make A.................  Model A1...............          70,000        80kWh
BEV..............................  Make A.................  Model A2...............           3,000       100kWh
BEV..............................  Make B.................  Model B1...............           4,000        90kWh
BEV..............................  Make C.................  Model C1...............          18,000        70kWh
----------------------------------------------------------------------------------------------------------------

    The average energy (Eavg) across all BEVs in the fleet
is initially found. In this example, the average energy is calculated
as the sum of the pack energy divided by the number of vehicle models:
[GRAPHIC] [TIFF OMITTED] TP17AU23.036

    Next, the average production volume (Pavg) for this
example was found via the sales weighted energy per vehicle by taking
the product of a model's pack energy (EModel xn) and
production volume (PModel xn) across all example vehicle
models--with the sum of all models then divided by the average pack
energy (Eavg), found from the previous equation:

[GRAPHIC] [TIFF OMITTED] TP17AU23.037

    Once the average BEV production (Pavg) was found, it is
rounded to the nearest 5,000; for this example, the production volume
is rounded up from 104,235.3 to 105,000 vehicles. This process was used
to determine production volumes for each of the electrified powertrain
technologies in the fleet. Our final battery manufacturing plant
production volume assumptions for different electrification
technologies are as follows: mild hybrid and strong hybrids are
manufactured assuming 200,000 packs, PHEVs are manufactured assuming
20,000 packs, and BEVs are manufactured assuming 60,000 packs.
    We believe it was reasonable to consider U.S. sales for purposes of
this calculation rather than global sales based on the best available
data we had at the time of modeling and based on our understanding of
how manufacturers design BEVs for particular markets.\324\ That said,
we are interested in comments from manufacturers and other stakeholders
on how vehicle and battery manufacturers take advantage of design
overlap across markets to maintain cost reduction progress in battery
technology. A manufacturer may have previously sold the same vehicle
with different battery packs in two different markets, but as the
outlook for battery materials and global economic events dynamically
shift, manufacturers could take advantage of significant design overlap
and other synergies like from vertical integration to introduce lower-
cost battery packs in markets that it previously perceived had
different design requirements.\325\ To the extent that manufacturers'
costs are based more closely on global volumes of battery packs
produced, our base year battery pack production volume assumption could
potentially be conservative; however, as discussed further below, our
base year MY 2022 battery pack costs fall well within the range of
reasonable estimates based on 2023 data. Again, we seek comment on this
approach and the resulting base year cost estimates.
---------------------------------------------------------------------------

    \324\ As an example, a manufacturer might design a BEV to suit
local or regional duty cycles (i.e., how the vehicle is driven day-
to-day) due to local geography and climate, customer preferences,
affordability, supply constraints, and local laws. This is one
factor that goes into chemistry selection, as different battery
chemistries affect a vehicle's range capability, rate of
degradation, and overall vehicle mass.
    \325\ As an example, some U.S. Tesla Model 3 and Model Y battery
packs use a nickel cobalt aluminum (Lithium Nickel Manganese Cobalt
Aluminum Oxide cathode with Graphite anode, commonly abbreviated as
NCA)-based cell, while the same vehicles for sale in China use LFP-
based packs. However, Tesla has introduced LFP-based battery packs
to some Model 3 vehicles sold in the U.S., showing how manufacturers
can take advantage of experience in other markets to introduce
different battery technology in the United States. See Electric
Vehicle Database. Tesla Model 3 Standard Range Plus LFP. Available
at: https://ev-database.uk/car/1320/Tesla-Model-3-Standard-Range-Plus-LFP. (Accessed: May 31, 2023). See the Tesla Model 3 Owner's
Manual for additional considerations regarding LFP-based batteries,
at https://www.tesla.com/ownersmanual/model3/en_jo/GUID-7FE78D73-0A17-47C4-B21B-54F641FFAEF4.html.
---------------------------------------------------------------------------

    As mentioned above, our BatPaC lookup tables provide $/kWh battery
pack costs based on vehicle power and energy requirements. As an
example, a midsized SUV with mid-level road load reduction technologies
(MASS, ROLL, and AERO), like the vehicle in the example in Section
II.C, might require a 110-120kWh energy and 200-210kW power battery
pack. From our base year BatPaC cost estimates, that vehicle might have
a battery pack that costs around $123/kWh. Note that the total cost of
a battery pack goes up the higher the power/energy requirements,
however the cost per kWh goes down. This represents the cost of
hardware that is needed in all battery packs but is deferred across
more kW/kWh in larger packs, which reduces the per kW/kWh cost. Table
II-15 shows an example of the BatPaC-based lookup tables for the BEV3
SUV through pickup technology classes.

[GRAPHIC] [TIFF OMITTED] TP17AU23.038

    Note that the values in the table above should not be considered
the total battery $/kWh costs that are used for vehicles in the
analysis in future MYs. As detailed below, battery costs are also
projected to decrease over time as manufacturers improve production
processes, shift battery chemistries, or make other technological
advancements. In addition, select modeled tax credits further reduce
our estimated costs; additional discussion of those tax credits is
located throughout this preamble, the Draft TSD, and PRIA.
    The CAFE Analysis Autonomie Documentation details other specific
assumptions that ANL used to simulate battery packs and their
associated base year costs for the full vehicle simulation modeling,
including updates to the battery management unit costs, and the range
of power and energy requirements used to bound the lookup tables.\326\
Please refer to the CAFE Analysis Autonomie Documentation and Chapter
3.3 of the Draft TSD for further information about how we used BatPaC
to estimate base year battery costs. The full range of BatPaC-generated
battery DMCs is located in ANL--Summary of Main Component Performance
Assumptions_NPRM_2206. Note again that these charts represent the DMC
using a dollar per kW/kWh metric; battery absolute costs used in the
analysis by technology key can be found in the CAFE Model Battery Costs
File.
---------------------------------------------------------------------------

    \326\ CAFE Analysis Autonomie Documentation chapter titled
``Battery Performance and Cost Model--Use of BatPac in Autonomie.''
---------------------------------------------------------------------------

    For this analysis, our method of estimating future battery costs
has three fundamental components: (1) an estimate of MY 2022 battery
pack costs (i.e., our base year costs generated in the BatPaC 5.0 model
to estimate battery pack costs for specific vehicles, depending on
factors such as pack size and power requirements, discussed above), and
(2) future learning rates through 2050, and (3) the effect of changes
in the cost of key minerals on battery pack costs, which are discussed
below.
    The concept of a learning curve was initially developed to describe
cost reduction due to improvements in manufacturing processes from
knowledge gained through experience in production; however, it has
since been recognized that other factors make important contributions
to cost reductions associated with cumulative production.\327\ We
discuss this concept further, in Section II.C.
---------------------------------------------------------------------------

    \327\ Wene, C. 2000. Experience Curves for Energy Technology
Policy. International Energy Agency. Paris.
---------------------------------------------------------------------------

    For the last CAFE Model analysis, we estimated potential future
reductions in battery pack costs,\328\ based on an assessment of cost
reductions due to battery pack production volume increases.\329\ This
production-volume-based learning rate clearly fell within the meaning
of a ``learning curve'' because the cost reductions were based on
improvements in manufacturing processes due to knowledge gained through
experience in production. We also used BatPaC to examine how battery
pack costs might change due to factors other than production volume
increases, including chemistry changes and changes in manufacturing
plant efficiency, while recognizing that BatPaC does include some cost
reductions due to improvements in manufacturing processes, in
particular

through assumed increases in the degree of plant automation.\330\
Recognizing that battery pack costs for future years are inherently
uncertain, we sought comment on our learning rates and also provided
cost estimates from other sources against which to compare our
estimates.331 332 Our conclusion after considering comments
and publicly available information was that our estimates of how
battery pack costs could reduce over time fell reasonably within the
estimates of potential future battery pack cost estimates from other
sources. However, we also received valuable information and feedback
from commenters on sources of information about future battery costs
estimates,\333\ and concerns about factors that could potentially drive
the future cost of battery packs up or down.\334\