WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES ONLY.

DO NOT QUOTE OR CITE BECAUSE RESULTS ARE SUBJECT TO CHANGE.

October 20, 2021

MEMORANDUM FOR: Angelina LaRose

Assistant Administrator for Energy Analysis

FROM: Jim Diefenderfer

Director, Office of Long-Term Energy Modeling

SUBJECT: Summary of AEO2022 Transportation Working Group held on Thursday, 

September 30, 2021

This memorandum summarizes our presentation and discussion at the second Annual Energy Outlook 

2022 (AEO2022) Transportation Working Group meeting. The Transportation Working Group 

presentation covered preliminary results from the AEO2022 Reference case transportation projections. 

It also highlighted historical transportation data and modeling updates in progress for the 

Transportation Demand Module (TDM) for the AEO2022 Reference case, as set up in our National 

Energy Modeling System (NEMS). After the presentation, meeting participants commented on additional

model and data topics. The presentation for this meeting is available in a separate document on our 

website.

Preliminary Model Updates (AEO2022)

Highlights from the presentation primarily relate to preliminary AEO2022 Reference case model results. 

These results include:

• Macroeconomic assumptions and projections

• Light-duty vehicle (LDV) travel demand projections

• Medium- and heavy-duty vehicle (HDV) energy consumption

• Aviation model updates

We presented updates on progress in the following model areas:

• Light-duty vehicles—Light-duty vehicle stock data update, new battery model integration,

regional sales and stock distribution revision, and pending policy changes under consideration

• Heavy-duty vehicles—Regional travel and stock data update, new electric vehicle (EV) 

powertrain, and battery model integration

• Public transit—Bus and passenger rail travel update, travel demand equation re-estimation, and

transit bus fuel choice update
WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES ONLY.

DO NOT QUOTE OR CITE BECAUSE RESULTS ARE SUBJECT TO CHANGE.

Discussion

During the discussion, participants’ questions primarily focused on potential policy changes, EV topics 

including battery technology, charging infrastructure, and battery model updates, as well as model 

technologies for freight and heavy-duty vehicles. 

Potential policy changes

Participants asked if the LDV fuel economy projections account for the U.S. Environmental Protection 

Agency’s (EPA) proposed rule to revise existing national greenhouse gas emissions standards for 

passenger cars and light trucks through model year 2026. We answered that the AEO2022 Reference 

case reflects current laws and regulations and does not account for proposed rulemaking. Therefore, the 

AEO2022 Reference case includes the Safer Affordable Fuel-Efficient (SAFE) Vehicles Rule issued for 

model years 2021 through 2026 passed in early 2020, and it includes the One National Program Rule 

passed in late 2019. One participant asked us to clarify the source of EV technology costs, and we 

referred to the SAFE ruling, but we assured participants that EV technology costs will be updated to 

reflect values based on the review of the 2020 model input files underlying National Highway Traffic 

Safety Administration (NHTSA) final Environmental Impact Statement.

We were asked to specify which state programs and regulations are accounted for in the model. We 

responded that the NEMS TDM accounts for California’s Senate Bill 32 (SB-32), which aims to reduce 

passenger vehicle travel. SB-32 does not account for other state goals such as internal combustion 

engine (ICE) bans or zero emission vehicle (ZEV) mandates. ICE bans and ZEV mandates are not 

enforceable under current law and regulations applied for AEO2022. 

A participant asked if the AEO2022 Reference case contains a scenario where the U.S. government 

defaults and ends up in a recession. We explained that cases for this specific scenario are not included in 

the AEO2022 Reference case. However, we publish High and Low Macroeconomic side cases that can be 

used to analyze economic growth and energy consumption. Another participant asked a follow-up 

question on whether or not we have assumptions for the proposed Infrastructure Investment and Jobs 

Act, to which we responded we do not have current assumptions for proposed infrastructure bills 

because the bill is not reflective of current law and regulation. 

Electric vehicle topics

The majority of discussion questions focused on topics related to EVs. A participant asked if we plan on 

updating other EV cost modeling for vehicle components while implementing the new battery cost 

model. We responded that our non-battery vehicle component costs are based on NHTSA data and that 

we plan to update other EV operations parameters that influence total cost including battery energy 

density and maximum depth of discharge. 

A participant asked us to clarify the share of EVs in the LDV stock for current AEO projections and 

inquired how updated battery cost figures will perform after implementing updated battery figures. 

Currently, we are improving several components of the model that make predicting how the EV share of 

stocks will perform. However, AEO2021 projected that plug-in EVs (including fully electric and plug-in 

hybrid EVs) reach 8% of the total LDV stock in 2050. We expect the battery cost update will result in a 

higher share of plug-in EV stock when compared with AEO2021, but we cannot confirm at this time. 
WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES ONLY.

DO NOT QUOTE OR CITE BECAUSE RESULTS ARE SUBJECT TO CHANGE.

A participant asked if we could provide more information and data for battery costs and performance. 

We answered that the assumptions, methodology, and input parameter values will be available when 

the AEO2022 is published in early 2022. Currently, the NEMS TDM assumes battery cost reductions are 

related to cumulative production and are similar to Wright’s Law. Each doubling in cumulative 

production results in a corresponding percentage decrease in the battery cost, otherwise called the 

learning rate. A key update to the model is to increase this learning rate. We noted that the battery cost 

model provides an estimate of retail price equivalent battery costs, or the cost to the consumer 

purchasing the vehicle. A follow-up question asked us to clarify whether the battery price model is 

based on domestic or global battery production and requested information regarding the markup of

battery price. We informed the participant that learning rate is based on global battery production and 

pricing but model production values are domestic only and that batteries prices assume a 50% markup. 

Next, participants inquired about our data source for battery EV (BEV) fleet penetration. We informed 

them that we annually purchase Polk vehicle registration data to understand how many vehicles are in 

the fleet by region, powertrain, vintage, and type, which informs our BEV fleet penetration. A

participant asked whether BEVs will reach purchase price parity with gasoline LDVs. We explained that 

price parity does not seem likely unless a battery technology revolution further reduces prices. In 

addition, EVs would need to reach both purchase price parity as well as performance and functionality 

parity. Battery powered vehicles need to be as affordable as gasoline vehicles while also being equally 

convenient to refuel and equally equipped to drive long distances. 

Participants asked a few questions about the NEMS TDM. First, a participant asked about our 

assumptions regarding the number of EV charging stations. We answered that the NEMS TDM does not 

project counts or locations for EV charging infrastructure. We plan to talk with NEMS Electricity Market 

Module (EMM) staff about implementing new model components that account for the cost of EV 

infrastructure and availability. A participant followed up and asked if access to charging is a component 

of the LDV consumer choice model, which would mean EV deployment could be based on infrastructure 

constraints. We responded that whole access to charging is an important consideration for consumers 

who decide whether or not to purchase a vehicle with plug-in capabilities. Although important, it is not 

clear if the lack of infrastructure is restraining sales or if infrastructure is waiting on sales to provide a 

feasible business model. NEMS TDM assumes that infrastructure will increase proportionally with EV 

sales and neither promote nor limit EV adoption. However, the consumer choice model does include 

fuel availability in the utility equation used. 

Other topics

Specific to the impact of the COVID-19 pandemic, a participant asked how the pandemic has affected 

freight demand and to what extent medium- and heavy-duty truck (MHDV) electrification are included 

within the model. First, we explained that industrial output is the driver for on-road freight demand in 

the NEMS TDM. We did not apply adjustments to freight model results since the results respond 

appropriately to industrial output projections from the macroeconomic module. Second, the freight 

truck vehicle choice component of NEMS TDM includes electric trucks as an option, and we are currently 

working on updates to the model as well as tracking the latest market developments for electric freight 

trucks. 
WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES ONLY.

DO NOT QUOTE OR CITE BECAUSE RESULTS ARE SUBJECT TO CHANGE.

A participant asked for the difference in battery pack prices when comparing light-duty vehicle pack 

prices to freight truck battery pack prices. We noted that data on freight truck battery prices can 

sometimes be unreliable, and our current research of freight truck battery pack prices has alluded to a 

price 1.5–2 times higher when compared with LDV battery packs. 

Regarding heavy-duty trucks, a participant asked if we made changes to heavy-duty truck technology or 

efficiency and how much oil price matters. We answered that we made no additional changes to heavyduty truck technology or efficiency for AEO2022. Also, oil price is important in the fuel choice 

component, but whether oil price is the major driver or another driver in the freight truck projections

remains unclear. A follow-on question by a participant asked if we have specifics on which HDV industry 

outputs have changed. We answered that agriculture, mining, and construction all decreased in the 

projection when compared with AEO2021. However, for industry outputs not mentioned, we 

recommend contacting the Macroeconomic team for further insight. 

A participant commented that work-from-home does not appear to be relevant in the long-term 

projections and asked if we could comment on the trend within the TDM model. We explained that we 

do not explicitly account for the telework trend. Instead, we apply COVID-19 assumptions to reduce 

vehicle miles travelled (VMT) by a small percentage. This small percentage reduction applied to VMT in 

the long term accounts for some of the potential long-term downward pressure on VMT. 

Lastly, a participant inquired about side cases included alongside the AEO2022 Reference case. We

replied that we are planning on including side cases, although the specific side cases have not been 

determined yet. 
WORKING GROUP PRESENTATION FOR DISCUSSION PURPOSES ONLY.

DO NOT QUOTE OR CITE BECAUSE RESULTS ARE SUBJECT TO CHANGE.

Attendees

Guests (Webex/phone)

Samaneh Babaee OnLocation, Inc

Megan Beardsley U.S. Environmental Protection Agency

Daniel Bizer-Cox U.S. Environmental Protection Agency

Akshay Delity U.S. Environmental Protection Agency

Katya Garcia-Israel U.S. Department of Transportation

David Gohlke Argonne National Laboratory

Kevin Green U.S. Department of Transportation

Michael Hartrick Alliance For Automotive Innovation

Robert Hershey Professional

Michael Hilliard Oak Ridge National Laboratory

Aaron Hula U.S. Environmental Protection Agency

Bryan Just American Petroleum Institute

Ben King Rhodium Group

Hannah Kolus Rhodium Group

Elizabeth McNamee U.S. Department of Energy

John Meyer Leidos

Neil Miller U.S. Environmental Protection Agency

Tiffany Mo U.S. Environmental Protection Agency

Evvan Morton U.S. Department of Energy

Steve Plotkin Argonne National Laboratory

Gregory Powell U.S. Department of Transportation

Christopher Ramig U.S. Environmental Protection Agency

Kevin Shen Union of Concerned Scientists

Wyatt Thompson University of Missouri

Clayton Vernon Sunoco

Alex Wang U.S. Environmental Protection Agency

Jarrett Whistance University of Missouri

Frances Wood OnLocation, Inc

Arthur Yip National Renewable Energy Laboratory

EIA attendees (Webex/phone) 

Erin Boedecker Mala Kline Mark Schipper

Caroline Campbell John Maples Nicholas Skarzynski

Jim Diefenderfer Elizabeth May Courtney Sourmehi

Michael Dwyer Kelly Perl Michael Stanley

Travis Freidman James Preciado Thomas White

Mindi Farber-DeAnda Mark Schipper