Market Segmentation Analysis of 

Medium and Heavy Duty Trucks 

with a Fuel Cell Emphasis 

Chad A. Hunter

National Renewable Energy Laboratory

June 14, 2018

DOE Hydrogen and Fuel Cells Program 

2018 Annual Merit Review and Peer Evaluation Meeting

This presentation does not contain any proprietary, confidential, or otherwise restricted information.

Project ID SA169

NREL/PR-5400-71478
NREL | 2

Overview: 

Fuel Cell M/HD Vehicle Market Segmentation

Timeline Barriers (4.5)

Start: September, 2017

End: September, 2018

35% complete

A. Future Market Behavior

• Assessing competitiveness of fuel cell M/HDVs

C. Inconsistent Data, Assumptions & Guidelines

• Consistent modeling methodology using established 

cost/price targets

D. Insufficient Suite of Models and Tools

• Update powertrain optimization models for M/HDVs and 

expand the national stock model 

Budget Partners

Total Project Funding: $150k

• FY18: $150k

Total DOE funds received to 

date: $150k

University of Vanderbilt

• Dr. Yuche Chen
NREL | 3

Relevance (1/2): 

FCTO Systems Analysis Framework

Fuel Cell M/HDV Market Segmentation 

Integrates System Analysis Framework: 

• Leveraging and expanding existing 

systems analysis models 

• Systems analysis approach using 

established cost and price targets

Analysis 

Framework • Cost estimation (TCO)

• Stock modeling

• Energy resource 

utilization

• H2 supply-chain 

optimization

Models & Tools

• FASTSim

• SERA

• VISION

• H2A production and 

delivery models

Studies & 

Analysis • Fuel cell M/HDV 

market analysis

• Framework 

implementation

Outputs & 

Deliverables

• Reports

• Public insights into 

market potential

• FCTO Program 

Targets

• GPRA Targets

• H2@Scale

• VTO/BETO/FCTO 

Market Segmentation

• TCO Frameworks 

• Fuel Cell 

Technologies Office

• Fuel cell vehicle 

analysis community

• Industry stakeholders

Acronyms

FASTSim: Future Automotive Systems Technology Simulator 

GPRA: Government Performance and Results Act

H2A: Hydrogen Analysis 

M/HDV: Medium/Heavy-Duty Vehicles

SERA: Scenario Evaluation and Regionalization Analysis

TCO: Total Cost of Ownership
NREL | 4

Relevance (2/2):

FCEV Market Segmentation Objectives

FY18 Objectives:

1. To provide industry, government, and non-government 

stakeholders a broad scoping assessment of medium/heavy 

duty fuel cell vehicle market opportunities across different 

classes, vocations, regions, and time

2. Assess technical barriers and opportunities for improvement in 

the medium/heavy duty fuel cell vehicle technology space to 

guide DOE investment in advanced technologies

The FCEV Market Segmentation project aims to identify the most 

promising markets for medium/heavy duty vehicles using a systems 

analysis approach with established technology and cost targets
NREL | 5

Approach (1/4): 

Analysis Method Integrates Multiple Models

Future Automotive Systems 

Technology Simulator (FASTSim)

• Powertrain cost optimization using 

vehicle attributes and vocations

Scenario Evaluation and 

Regionalization Analysis (SERA)

• National stock model based on 

VISION model, IHS/Polk data

• Stock, VMT, and fuel consumption 

disaggregated by region, vehicle, 

vocation, and time

• Total cost of ownership (TCO) 

analysis using regional-, vehicle-, 

vocation-, and time-specific detail

The combination of FASTSim and SERA will allow for geographically 

explicit stock modeling and fuel cell M/HDV market potentials

Cost Modeling

(FASTSim)

Adoption Rate 

Modeling 

(TCO, SERA)

Stock Modeling 

(SERA, VISION)

Vehicle Segment 

Market Potential

Orange = Data

Green = Model

Blue = Results
NREL | 6

Approach (2/4): 

Powertrain cost optimization using FASTSim

Vehicle/vocations assessed in FASTSim to 

determine cost, fuel economy, and weight

5. FASTSim

Cost Optimization

Multiple drive-cycles by vocation

Representative drive-cycle for vocation

Vehicle Cost

Fuel economy

Weight

Vehicles and vocations

4. Vehicle and Cost 

Target Data

FASTSim Cost Modeling Steps

1. Vehicles and vocations determined 

by market share data

2. Fleet DNA data used to obtain drivecycle data for each vehicle 

class/vocation combination

3. The Drive-Cycle Rapid Investigation, 

Visualization, and Evaluation (DRIVE) 

tool used to create representative 

drive-cycles

4. Vehicle attribute and GPRA cost 

targets (2018/2040) data input

5. Vehicle and vocation drive-cycle data 

used to optimize vehicle cost

Orange = Data

Green = Model

Blue = Results

3. DRIVE

2. Fleet DNA 

Drive-Cycle Data

1. Vehicle/Vocation 

Assessment
NREL | 7

Approach (3/4): 

SERA M/HDV Stock Modeling

2. SERA

Stock Model

1a. VISION Model

Stock, VMT, energy usage by

region, vehicle, vocation, and time

SERA Stock Modeling Steps

1. Determine data availability across various data sources

a) VISION Model

• Historic and future sales and market share by vehicle class and fuel type

• Annual vehicle-miles-travel (VMT), survival rate, and fuel economy

b) IHS/Polk

• Historic sales, market share data by vehicle and vocation 

c) VIUS

• Historic sales, market share data

• Annual VMT, fuel efficiency

2. Reconcile data sources, determine which to use

3. Incorporate data into SERA model, iterate until agreement 

between VISION and SERA

Develop SERA stock 

model based on 

various data sources 

to track vehicle 

population, VMT, and 

energy usage over 

time and region

1b. IHS/Polk

1c. VIUS
NREL | 8

Approach (4/4): 

Market Share Potential Based on Vehicle TCO

Vehicle cost

Fuel economy

Weight

Stock

VMT

Energy Usage

Total Cost of Ownership (TCO) Modeling

1. Integrate vehicle cost, fuel economy, 

and weight (FASTSim outputs) into 

SERA stock model

2. Incorporate additional ownership 

costs into SERA stock model

1. Fuel cost (AEO)

2. Operating & Maintenance cost

3. Opportunity Costs (Payload, Utilization)

4. Other Potential Value Streams

A. Independent electric motors (reduced jackknifing)

B. Lower center of gravity (reduced roll-over risk)

3. Identify vehicles/vocations with 

lowest TCO by region. Complete 

sensitivity analysis

Lowest cost vehicle 

technology by region

Market potential 

for FCEVs

SERA model will be used to 

calculated TCO for each vehicle 

class and vocation by region

3. Vehicle Segment 

Market Potential

2. Adoption Rate 

Modeling 

(TCO, SERA)

1b. Stock Modeling 

(SERA, VISION)

1a. Cost Modeling

(FASTSim)
NREL | 9

Accomplishments and Progress (1/4): 

FASTSim: Powertrain cost optimization

FASTSim Cost Modeling Step Progress

1. (Complete) Selected initial set of vehicles 

and vocations based on literature1and VIUS 

data

2. (Complete) Drive-cycle data for each vehicle 

class/vocation extracted from Fleet DNA

3. (Complete) The DRIVE tool has been used to 

create representative drive-cycles

Vehicle Class Vocation

Class 2b Small Van

Class 3 Enclosed Van

Class 3 School Bus

Class 3 Service, Utility Truck

Class 4 Walk-In / Multi-Stop, Step Van

Class 5 Utility, Tow Truck

Class 6 Construction, Dump Truck

Class 7 School Bus

Class 8 Construction, Dump Truck

Class 8 Line Haul

Class 8 Refuse, Garbage Pickup

Class 8 Tractor Trailer

1. Marcinkoski, J. V. (2016, June 19-22). Driving an Industry: Medium and Heavy Duty Fuel Cell Electric Truck Component Sizing. Montreal.

Ex. Class 8 Line Haul Representative Drive-Cycle from DRIVE

Vehicles and vocations determined 

and drive-cycle data obtained
NREL | 10

Accomplishments and Progress (2/4): 

Powertrain cost optimization using FASTSim

FASTSim Cost Modeling Step Progress

4. (In Progress) FASTSim is being updated to optimize M/HDV with cost targets (GRPA, 

FCTO) and vehicle attribute data

5. (In Progress) Vehicle and vocation cost optimization and validation is on-going

• Preliminary results for conventional, HEV, 

BEV, and FCEV powertrains (PHEV ongoing)

• Different auxiliary power requirements may 

be used to evaluate climate considerations

FASTSim is being updated and 

validated to optimize M/HDV 

vehicles

Target 2020 2040

Battery

Battery Mass [kg/kWh] 4.2 2.5

Battery Price HEV ($/kW) 20.0 13.0

HEV Battery Cost [$/kWh] 194.4 80.0

PHEV Battery Cost [$/kWh] 194.4 80.0

PEV Battery Cost [$/kWh] 194.4 80.0

Fuel Cell

Hydrogen storage (kWh/kg) 1.5 2.2

Fuel cell specific power (kW/kg) 0.65 0.65

Fuel cell cost ($/kW) 40 30

Hydrogen tank cost ($/kWh) 10 8

Hydrogen fuel price ($/kg) 4 4

Vehicle Drag 

Coefficient 

Frontal 

Area (m2)

Glider Mass 

(kg)

Center of 

Gravity 

Height (m)

Class 3 Enclosed Van 0.714 6 3694 0.305

Class 4 Parcel Delivery 0.698 6 3694 0.305

Class 8 Regional Truck 0.796 9.5 13625 0.53

Class 8 Line Haul 0.6 8.5 13625 0.53

Transfer Truck 1 5.61 13625 0.53

Drayage Truck 0.8 6 13625 0.53
NREL | 11

Accomplishments and Progress (3/4): 

FASTSim Results: Class 8 Line Haul Case Study

FASTSim results provide 

upfront vehicle costs, 

fuel economy, and mass 

for TCO analysis

FASTSim Class 8 Line Haul Upfront Cost (no O&M / Opportunity Costs)

FASTSim Class 8 Line Haul Key Results

• Preliminary FASTSim results indicate 

upfront Class 8 Line Haul costs 

higher than conventional but lower 

than BEV

• FCEVs have higher fuel economy 

but are heavier than conventional 

vehicles (to be validated) 

• Fuel, O&M, Opportunity costs, and 

other potential value streams are 

not accounted for in FASTSim but 

will be included in the TCO analysis

Class 8 Line Haul 2020 Technology 2050 Technology

BEV FCEV HEV CONV BEV FCEV HEV CONV

MSRP ($k) 471 257 149 144 251 236 146 144

Fuel economy (mi/gge) 15.1 9.8 6.8 6.5 15.9 10 6.8 6.5

Mass (thousand kg) 32.9 30.1 28.6 28.4 30.2 29.3 28.5 28.4
NREL | 12

Accomplishments and Progress (4/4): 

SERA M/HDV Stock Model: Data Comparison

Polk data used to 

disaggregate VISION 

population, VISION 

fuel economy and 

VMT will be used

SERA Stock Modeling Steps

1. (Complete) Determine and compare data availability across various data sources

2. (In progress) Incorporate vehicle data into SERA model, match VISION model

• Polk data shows larger 

MDV/HDV stock populations

• Fuel economy data from 

VISION matches VIUS C7-8

• VISION and VIUS VMT are 

consistent for both classes
NREL | 13

Accomplishments and Progress

Responses to Reviewers’ Comments

• N/A as this is a new, FY18 project for FCTO
NREL | 14

Collaboration and Coordination

• Vanderbilt University - Modeling

– Dr. Yuche Chen supporting vehicle stock model development
NREL | 15

Remaining Challenges and Barriers

FASTSim Model

• Continue to validate model outputs for vehicle cost, fuel economy, 

and weight

• Evaluate climate effects on auxiliary power load

SERA Stock Model

• Need to evaluate tradeoffs between having increased stock model 

detail (region, vocation, and vehicle specific VMTs, survival rates, and 

fuel economies) and exactly matching the VISION model

• Spatial distribution of VMT across regions could be challenging give 

time and resource limits. Could be a potential future enhancement

SERA Total Cost of Ownership Analysis

• Limited data on fuel cell and battery truck upfront costs, weight, 

O&M costs, opportunity costs, and other potential value streams
NREL | 16

Proposed Future Work Future Work

FASTSim Cost Modeling

• Continue updating and validating FASTSim M/HDV 

outputs (cost, fuel economy, weight) 

• Complete modeling for all vehicles/vocations

SERA Stock Modeling 

• Integrate VISION and Polk/IHS data into SERA model

• Verify alignment between VISION and SERA

SERA TCO Modeling

• Review and compile available data on O&M, 

opportunity costs, and other value streams

• Complete spatial and temporal TCO modeling

• Complete sensitivity analysis on cost assumptions

Potential Future Scope

• Evaluate other vehicle segments (rail, ships, planes)

• Integrate with H2@Scale through temporal and 

spatial supply, demand, and storage requirements

• Evaluate cost-volume feedback loop between 

production volume ramp up and cost curves

Project Plan

FASTSim Cost Modeling

• Define vehicles/vocations

• Obtain relevant data

• Complete modeling

SERA Stock Modeling

• Evaluate data sets

• Develop stock model

SERA TCO Modeling

• Integrate FASTSim outputs 

input into SERA

• Acquire indirect cost data

• Complete TCO modeling

• Sensitivity analysis

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NREL | 17

Technology Transfer Activities

• FASTSim is currently available (LDV) and the updated 

version (with M/HDV capabilities) will be made available 

after project completion

– https://www.nrel.gov/transportation/fastsim.html

• Licensing of SERA model is being considered

• Analysis visualizations may be added to NREL’s 

Hydrogen Demand and Resource Analysis (HyDRA) tool

– https://maps.nrel.gov/hydra/
NREL | 18

Summary

Relevance

• Expansion of systems analysis models that assess cost and market barriers to fuel cell 

vehicle adoption

• Provide stakeholders a broad assessment of medium/heavy duty fuel cell vehicle market 

opportunities and guide future DOE investment

Approach

• FASTSim for vehicle optimization to obtain vehicle cost, fuel economy, and weight

• SERA for stock modeling using VISION, Polk/IHS data

• SERA TCO modeling direct costs, opportunity costs, and other value streams

Accomplishments and Progress

• Vehicle segmentation and drive-cycle data obtained for FASTSim analysis

• Initial results from FASTSim acquired, undergoing verification/validation

• VISION, Polk/IHS, and VIUS data evaluated and being integrated into SERA 

Collaboration

• Vanderbilt University

Current and Potential Future Work

• Complete validation of updated M/HDV FASTSim model and results

• Complete SERA stock model and alignment with VISION

• Complete TCO modeling by integrating FASTSim results and cost data into SERA

• Potential: Evaluate other vehicle segments and integrate results into H2@Scale analysis
NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency 

and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency 

and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency

and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Thank You
Reviewer-Only Slides
NREL | 21

Critical Assumptions and Issues

1. Data Availability

a. Limited data to verify electric truck upfront costs and weight

b. Limited data to verify indirect (O&M) and opportunity costs

c. Regional cost differences depend on data availability

d. Assume infrastructure costs are rolled into fuel costs

2. Stock Model Fidelity

a. Detailed travel data is needed for each vehicle/vocation if VMT 

is to be split between specific regions of interest

b. Temporal travel detail (hourly) is needed for proper assessment 

of demand charges – outside of current scope
NREL | 22

Publications and Presentations

N/A at this time
NREL | 23

Data Management Plan

• This project will maintain compliance with data management requirements 

of the Department of Energy and abide by the Office of Energy Efficiency 

and Renewable Energy data sharing and preservation requirements.

• To the greatest extent and with the fewest constraints possible, this project 

will make digital research data available to, and useful for, the broader 

scientific community, industry, and the public. 

• Technical reports, journal article accepted manuscripts, software, and 

scientific research datasets will be submitted to OSTI through the DOE 

Energy Link System. Data from this project deemed appropriate for public 

access will be made available through the NREL Data Catalog 

• Data in this public release will be in a machine-readable digital format (e.g., 

comma-delimited).

• This project will not generate or use Personally Identifiable Information 

(PII). Any data containing national security implications, business 

confidentiality, or intellectual property will not be released in accordance 

with all laws and DOE regulations, orders, and policies.