A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Fuel Cell Vehicle Learning 

Demonstration: Spring 2008 

Results 

Preprint 

K. Wipke, S. Sprik, and J. Kurtz 

National Renewable Energy Laboratory 

J. Garbak 

U.S. Department of Energy 

Presented at the NHA Annual Hydrogen Conference 2008 

Sacramento, California 

March 30–April 3, 2008 

Conference Paper 

NREL/CP-560-43055 

April 2008 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
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FUEL CELL VEHICLE LEARNING DEMONSTRATION: 

SPRING 2008 RESULTS1

K. Wipke2

, S. Sprik2, J. Kurtz2, J. Garbak3

Abstract 

The “Controlled Hydrogen Fleet and Infrastructure Demonstration and Validation 

Project,” also known as the Fuel Cell Vehicle and Infrastructure Learning 

Demonstration, is a five-year U.S. Department of Energy (DOE) project started in 

2004. The purpose of this project is to conduct an integrated field validation that 

simultaneously examines the performance of fuel cell vehicles and the supporting 

hydrogen infrastructure. The DOE’s National Renewable Energy Laboratory 

(NREL) has now analyzed data from almost three years of the five-year project. 

During this time, 92 vehicles have been deployed, 14 project refueling stations 

were placed in use, and no fundamental safety issues have been identified. We’ve 

analyzed data from over 200,000 individual vehicle trips covering 1,100,000 

miles traveled and over 40,000 kg hydrogen produced or dispensed. 

Public analytical results for this project are in the form of composite data 

products, which aggregate individual performance into a range that protects the 

intellectual property and the identity of each company, while still publishing 

overall status and progress. One of the key metrics from the project is fuel cell 

durability. We analyze all of the field data from the fuel cell vehicles, and make 

degradation projections based on a theoretical 10% drop in voltage at high 

current. With additional hours of operation accumulated on the stacks, the fourteam average projection is now 1,200 hours with some individual stacks 

accumulating more than 1,000 hours. In the next six months we will work to 

improve the accuracy of the voltage degradation projections by adding a nonlinear fit (or a two-step linear fit) to avoid potentially overestimating the projected 

time that could occur as the accumulated hours continues to grow. 

To understand what is causing the stacks to gradually degrade, NREL continues 

to characterize how each stack is used and performs multivariate analysis on this 

dataset to examine dominant variables affecting stack voltage degradation rate. 

Results to date indicate that extracting trends across all four teams is probably not 

possible due to technical differences among the teams’ hardware, but that 

 

1

 This work has been authored by an employee of the Midwest Research Institute under Contract 

No. DE-AC36-99GO10337 with the U.S. Department of Energy. The United States Government 

retains and the publisher, by accepting the article for publication, acknowledges that the United 

States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or 

reproduce the published form of this work, or allow others to do so, for United States Government 

purposes. 

2

 National Renewable Energy Laboratory, Hydrogen Technologies & Systems Center, Golden, CO 

3

 U.S. Department of Energy, Hydrogen Fuel Cells and Infrastructure Technologies Program, 

Washington, DC 

1 
individual results should be useful to the teams individually and for feeding back 

trends into the research and development program. 

We’ve analyzed fuel cell system efficiency at 1⁄4-power and compared it to the 

DOE target of 60%; system efficiency results from the four teams ranged from 

52.5% to 58.1%. Using data on communication vs. non-communication fills we 

found that communication fills demonstrated a higher rate of fill than noncommunication fills and the slowest of the non-communication fill rates (0.2 

kg/min) are being phased out. We also examined refueling and driving behavior, 

and found the Learning Demonstration fleet to be representative of national 

statistics with the exception of fewer late afternoon and weekend trips, an 

abundance of short trips, and a shorter average distance traveled per day. Finally, 

we’ve now published a total of 47 composite data products and made them 

directly accessible to the public from our Hydrogen Technology Validation Web 

site. 

Keywords: fuel cell vehicle, stack voltage degradation, hydrogen infrastructure, 

analysis, refueling. 

1. Introduction 

Hydrogen fuel cell vehicles (FCVs) are being developed and tested for their 

potential as commercially viable and highly efficient zero-tailpipe-emission 

vehicles. Using hydrogen fuel and high-efficiency fuel cell vehicles provides 

environmental and fuel feedstock diversity benefits to the United States. 

Hydrogen could be derived from a mixture of renewable sources, natural gas, 

biomass, coal, and nuclear energy, enabling the United States to reduce emissions 

and decrease its dependence on foreign oil. Numerous technical barriers remain 

before hydrogen fuel cell vehicles are commercially viable. Significant resources 

from private industry and government are being devoted to overcoming these 

barriers. 

The U.S. Department of Energy (DOE) is working with industry partners to 

develop these technologies through its Hydrogen, Fuel Cells & Infrastructure 

Technologies (HFCIT) Program. This multi-faceted program simultaneously 

addresses hydrogen production, storage, delivery, conversion (fuel cells), 

technology validation, deployment (education), market transformation, safety, and 

codes and standards. DOE has previously identified many key technical barriers, 

such as hydrogen storage and fuel cell durability. These barriers are being 

addressed through additional research. Other challenges may become apparent 

through integrated, real-world application of these technologies. Prior to this 

project, the number FCVs in service was small, and vehicle operation was focused 

primarily in California. The result was limited quantity and geographic diversity 

of the data collected. To address vehicle and refueling infrastructure issues 

simultaneously, DOE is conducting a large-scale “learning demonstration” 

involving automotive manufacturers and fuel providers. This learning 

2 
demonstration, titled the “Controlled Hydrogen Fleet and Infrastructure 

Demonstration and Validation Project,” is a cornerstone of the HFCIT Program’s 

technology validation effort, spanning from 2004 to 2009. 

2. Project Objectives and Targets 

This project’s objective is to conduct parallel learning demonstrations of 

hydrogen infrastructure and FCVs to allow the government and industry to assess 

progress towards technology readiness. We are identifying the current status of 

the technology and tracking its evolution over the five-year project duration. In 

particular, we are tracking differences between the first- and second-generation 

FCVs. The DOE’s National Renewable Energy Laboratory (NREL) works to 

provide DOE and industry with maximum value from the data produced by this 

“learning demonstration.” We seek to understand the progress toward the 

technical targets and provide that information to the HFCIT research and 

development (R&D) activities. This information will allow the program to move 

more quickly toward cost-effective, reliable hydrogen FCVs and supporting 

refueling infrastructure. The ability to feed results back into to the research and 

development as an integrated part of DOE’s program makes this project unique 

compared to typical demonstration projects. 

Fuel cell stack durability is critical to customer acceptance of fuel cell vehicles, 

and will be discussed in this paper. Although 2,000-hour durability in 2009 is 

considered acceptable to validate progress, a 5,000-hour lifetime (equivalent to 

approximately 100,000 miles) is estimated to be a requirement for market 

acceptance. Vehicle range is also an important consumer expectation. Although 

many factors contributed to the failure of battery-electric vehicles to gain market 

acceptance despite California government mandates, limited vehicle driving range 

and long charging times were widely accepted as significant contributors. 

Finally, hydrogen production cost is a key metric because consumers are much 

less likely to purchase an alternative fuel vehicle if the fuel is significantly more 

expensive than gasoline. 

3. Auto Industry and Refueling Infrastructure Partners 

Automotive original equipment manufacturers (OEMs) are leading three of the 

four teams, and an energy provider is leading the fourth. The major companies 

making up the four teams are as follows: 

• Chevron and Hyundia-Kia 

• Chrysler and BP 

• Ford Motor Company and BP 

• General Motors and Shell

Figure 1 shows the teaming arrangement of the four teams along with their firstgeneration fuel cell vehicles. In addition to data from the four Learning 

Demonstration teams, data from another DOE project called the California 

Hydrogen Infrastructure Project (CHIP) is also analyzed and included in the 

3 
infrastructure results to further broaden the available data set. Figure 2 shows 

examples of the four types of hydrogen refueling stations analyzed at NREL. 

Figure 1: Photographs of the Four Teams' First-Generation Vehicles with Small 

Inset Photos Showing the Second-Generation Vehicles 

Delivered Liquid, 700 bar 

Irvine, CA

Mobile Refueler 

Sacramento, CA 

Delivered 

Compressed 

H2 

Delivered 

Liquid H2 

Electrolysis 

Natural Gas 

On-Site 

Reforming 

Water Electrolysis 

Rosemead, CA 

Steam Methane Reforming 

Oakland, CA

Figure 2: Four Examples of Hydrogen Production and Refueling Facilities 

4 
4. Data Analysis Approach and Tools 

NREL’s approach to accomplishing the Learning Demonstrations’ objectives is 

structured around a highly collaborative relationship with each of the four 

industry teams. We are receiving raw technical data on both the hydrogen 

vehicles and refueling infrastructure that allows us to perform unique and 

valuable analyses across all four teams. Our primary objectives are to feed the 

current technical challenges and opportunities back into the HFCIT R&D 

Program and assess the current status and progress toward targets. 

To protect the commercial value of these data for each company, we established 

the Hydrogen Secure Data Center (HSDC) to house the data and perform our 

analyses. To ensure value is fed back to the hydrogen community, we publish 

composite data products (CDPs) twice a year at technical conferences and in 

journals [1-2]. These data products report on the progress of the technology and 

the project, focusing on the most significant results. Additional CDPs are 

conceived as additional trends and results of interest are identified. We also 

provide detailed analytical results from each individual company’s data back to 

them to maximize the industry benefit from NREL’s analysis work and obtain 

feedback on our methodologies. These individual results are not made available to 

the public. 

 

To process such a large data set (second-by-second data from over 200,000 

vehicle trips), we have created a specialized analysis tool at NREL called the 

Fleet Analysis Tool (FAT). This tool enables us to convert the data into a 

common format, perform all of the predefined analyses, and then study the results 

graphically. The tool is unique in that it lets us quickly compare data from within 

a team (stack to stack) or between teams. It also is the mechanism by which we 

create our CDPs, which pull individual results from each team into aggregate 

results. 

5. Composite Data Products – Public Results 

The most recent set of CDPs is called the “Spring 2008” results, and includes a 

total of 47 results. Due to the large number of results now available from this 

project, not all of them can be discussed in conference papers or presentations. 

Therefore, we discuss all of the results in detail in semi-annual NREL technical 

publications called Progress Reports. This paper will cover some highlights from 

the recent results. An expanded version of these results can be found in the Spring 

2008 Progress Report, which is available on our Web site 

(www.nrel.gov/hydrogen/proj_learning_demo.html). 

5.1 Fuel Cell Operation and Impact on Efficiency 

Results published from this project in 2006 showed that the range of fuel cell net 

system efficiency at 1⁄4 power from the four Learning Demonstration teams ranged 

from 52.5% to 58.1%, which is very close to DOE’s target of 60%. Recent 

analysis has focused on examining the regions of most frequent operation and 

their overall impact on energy usage. Since a fuel cell system’s peak efficiency is 

5 
normally at low powers (typically 10% to 25%), we evaluated the fuel cell system 

operation from a number of different perspectives to better understand whether 

the unique performance characteristics of the fuel cell system were being 

maximized. As reported in the last progress report, a significant amount of time is 

being spent at low fuel cell system power. In fact, the teams’ average amount of 

time spent at <5% of peak power was over 50%. However, for overall vehicle 

fuel efficiency, the amount of energy spent at various power levels and the 

efficiency at those power levels is the critical metric. We found that much of the 

fuel cell energy (about 40%) is expended at fuel cell power levels between 20% 

and 50% of peak power (Figure 3). This matches up very well with the peak fuel 

cell system efficiency points (at ~25% power) previously discussed. Only about 

20% of the energy is expended at powers <15% of peak power, indicating that 

low power efficiency is not as important as the percentage of time spent there 

would imply. 

10

Figure 3: Fuel Cell System Energy within Power Levels 

5.2 The Impact of Short Trips 

Recently there has been much public attention on the potential for plug-in hybridelectric vehicles (PHEVs) to improve the United States’ oil-dependency situation 

without waiting for fuel cell vehicles to be commercialized. The Learning 

Demonstration vehicle data were evaluated to see how these early fuel cell 

vehicles were being used (mostly in fleet operation) and what impact these real 

duty cycles would have on plug-in vehicles and potentially future plug-in versions 

of these fuel cell vehicles. We first looked at the amount of energy consumed by 

all Learning Demonstration vehicle trips (Figure 4) and found that almost 40% of 

the trips required less than 0.5 kWh of energy to be produced by the fuel cell 

system. This indicates that a battery would not have to be very large to handle 

several plug-in FCV trips for the Learning Demonstration vehicles, provided that 

0

2

4

6

8

% Energy by Power levels: DOE Fleet

% Energy

0-5%

5-10%

10-15%

15-20%

20-25%

25-30%

30-35%

35-40%

40-45%

45-50%

50-55%

55-60%

60-65%

65-70%

70-75%

75-80%

80-85%

85-90%

90-95%

95-100%

>100%

% Fuel Cell Power (Gross) of Max

80

60

40

Cumulative %

20

0

100

Created: Feb-27-08 12:04 PM

6 
the battery could also provide the peak power required and survive the larger 

swings in state-of-charge. However, this is not the entire story, and if the 

assumption is that PHEVs will primarily be recharged slowly during offpeak/night times, then these data need to be analyzed with both the daily miles 

traveled and the amount of time between trips in mind. 

40

Figure 4: Histogram of Fuel Cell System Output Energy 

We also performed these additional analyses and found that an effective 20-mile 

electric range would electrify about 1⁄2 of the Learning Demonstration fleet’s daily 

miles traveled. However, this would satisfy only about 1⁄4 of the national daily 

average miles traveled. We also found a large number of Learning Demonstration 

vehicle “hot-starts,” with about 60% of trips occurring within one hour of the 

previous trip. While this could be beneficial for fuel efficiency, it could also 

indicate that not all of the short trips could necessarily be electrified because there 

may not be sufficient time to recharge the battery from the grid in between trips, 

even if day-time opportunity charging is used. The bottom-line is that a thorough 

analysis of actual vehicle target-market duty cycles must occur for the benefits of 

PHEVs to be understood, preferably through using actual PHEV fleets and 

recharging behavior. Such an evaluation is envisioned through DOE’s current 

solicitation for a PHEV Learning Demonstration (see DOE’s Web site for details: 

http://www.netl.doe.gov/business/solicitations/#00360). 

5.3 Fuel Cell Voltage Degradation and Influencing Factors 

One of this project’s key metrics is fuel cell system durability. Fuel cell stacks 

will need roughly a 5,000 hour life to enter the market for light-duty vehicles. For 

this demonstration project, targets were set by DOE at 1,000 hours in 2006 and 

2,000 hours in 2009. Results were first published from this project by NREL in 

the fall of 2006. These results were relatively preliminary because most stacks at 

0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 >5 0

5

10

15

20

25

30

35

Trip Energy: DOE Fleet

% Trips

Energy Consumed [kWh]

Created: Feb-27-08 12:04 PM

# of Trips: 150221

Great opportunity for synergy 

between fuel cell drivetrain and 

plug-in HEV battery sizing to 

“electrify” these short trips

7 
that time only had a few hundred hours of operation or less accumulated on-road. 

Since DOE’s target for 2006 was 1,000 hours, NREL developed a methodology 

for projecting the gradual degradation of the voltage based on the data received to 

date to allow a comparison. This involved creating periodic fuel cell polarization 

curve fits from the on-road stack voltage and current data, and calculating the 

voltage under high current [4]. This enabled us to track the gradual degradation 

of the stacks with time and do a linear fit through each team’s data for all of their 

stacks. We then compared these results to the first-generation target of 1,000 

hours for 2006. 

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2006 Target

2009 Target

Actual Operating Hours Accumulated To-Date Projected Hours to 10% Degradation

Max Hrs Accumulated (1)(2) Avg Hrs Accumulated (1)(3) Projection to 10% Degradation (4)(5)

Time (Hours)

DOE Learning Demonstration Fuel Cell Stack Durability:

Based on Data Through 2007 Q4

Max Projection

Avg Projection

Created: Feb-26-08 11:46 AM

(1) Range bars created using one data point for each OEM.

(2) Range (highest and lowest) of the maximum operating hours accumulated to-date of any OEM's individual stack in "real-world" operation.

(3) Range (highest and lowest) of the average operating hours accumulated to-date of all stacks in each OEM's fleet.

(4) Projection using on-road data -- degradation calculated at high stack current. This criterion is used for assessing progress against DOE targets,

 may differ from OEM's end-of-life criterion, and does not address "catastrophic" failure modes, such as membrane failure.

(5) Using one nominal projection per OEM: "Max Projection" = highest nominal projection, "Avg Projection" = average nominal projection.

 The shaded green bar represents an engineering judgment of the uncertainty due to data and methodology limitations. Projections will change

 as additional data are accumulated.

Figure 5: Hours Accumulated and Projected Hours to 10% Stack Voltage 

Degradation 

In the past year and a half, many more hours have been accumulated on the fuel 

cell stacks, and the range of fleet averages is ~200 to 700 hours, with the range of 

fleet maximums spanning ~300 to 1,200 hours (Figure 5). This is the first time, to 

our knowledge, that light-duty passenger fuel cell cars have publicly accumulated 

more than 1,000 hours in real-world operation without repair, which is a 

significant project accomplishment. Therefore, the amount of extrapolation we 

have to make using the slope of the linear voltage degradation method (10% 

voltage drop target divided by the mV/hour slope), continues to decrease. 

However, with the additional data we have received, we are also finding that the 

accuracy of our projection of the 10% voltage degradation time might be 

improved by using a non-linear fit to account for the more rapid degradation that 

occurs within the first few hundred hours. It appears as though the current linear 

fit may be overestimating the time to a 10% voltage drop for the stacks that have a 

significant number of accumulated hours because the effect of the first data points 

on the linear fit becomes smaller (less weight) as more data is added. This causes 

the beginning of life voltage from the fit (from which the 10% drop is calculated) 

8 
to be lower than it was when we had just a few hundred hours of data. Therefore, 

NREL will experiment with alternate degradation fits to make the projected time 

to 10% degradation as accurate as possible. 

The projected time to 10% fuel cell stack voltage degradation from the four teams 

using the linear technique had an average of more than 1,200 hours with a high 

projection of more than 1,900 hours from one team, surpassing the 1,000-hour 

DOE target. Note that the 10% criterion, which is used for assessing progress 

toward DOE targets, may differ from the OEM’s end-of-life criterion and does 

not address “catastrophic” failures such as membrane failure. The secondgeneration stacks introduced in this project in 2008 will be compared to the 2,000-

hour target for 2009. 

We have continued the multivariate analysis that was initiated in 2007 to 

determine the dominant factors that are affecting the rates of degradation. We 

started out by creating a database of all of the Learning Demonstration stacks and 

various performance attributes. Each individual stack was examined for the hours 

of data accumulated to date and the confidence in the fit of the degradation slope. 

We then manually removed about one-third of the stacks from the degradation 

factors analysis to try to have as clean a data set as possible for the analysis. The 

database now includes the following key factors for each stack: 

• Average degradation rate (key dependent variable) 

• Ambient temperature 

• Time at various voltages 

• Time at various currents 

• Number of cold and hot starts (based on time between trips) 

• Idle time 

• Trip length 

• Average number of stack starts/hour. 

After trying many techniques, we focused on partial least squares regression 

(PLS) analysis because it was the most direct way of measuring how much of the 

variance in voltage degradation could be explained by specific groups of factors. 

We first performed the PLS on the stack data from all four teams to see if there 

were any overall trends that covered all of the technology involved. The trends 

across all four teams were not strong, which we soon discovered was because the 

trends among the companies were often different. 

Next we looked at each team’s data individually and performed the same PLS 

analysis (Figure 6). The connection between voltage degradation rate and the 

variables improved, and we were able to pull out groupings of factors that 

appeared to cause either higher or lower than average decay rates within each 

team. Note that the teams’ PLS models have a high percentage of explained 

decay rate variance, but at this point the models are not very robust and results are 

scattered. We found that while there were some common factors among several 

team’s results, there were also normally contradictory trends from one or more of 

9 
the teams (an example of this conflicting trend is for high voltage time and low 

current time for team four vs. team two). This analysis effort is continuing in 

close collaboration with each of our industry partners. NREL will work closely 

with each team to carefully examine the inputs and outputs from this analysis and 

identify trends that can be fed back into the companies’ research as well as into 

DOE’s R&D program. 

Team 1

High Voltage Time

High Current Time

Hot Ambient Temp

Short Trips

Starts/hour

Team 2

Team 3

Team 4

Hot Starts

Starts/hour

High Voltage Time

Low Current Time

Idle Time

Cold Starts

Warm Ambient Temp

Long Trips

High Voltage Time

Low Current Time

Hot Starts

Idle Time

Short Trips

Starts/hour

1) On-going fuel cell degradation study using Partial Least Squares (PLS) 

regression model for each team.

2) Teams’ PLS models have a high percentage of explained decay rate variance, 

but the models are not robust and results are scattered. 

H*

L**

High Voltage Time

Low Current Time

Idle Time

Low Voltage Time

Cold Starts

Hot Ambient Temp

Short Trips

Starts/hour

H*

H*

H*

H*

L**

L**

H*: Factor group associated with high decay rate fuel cell stacks

L**: Factor group associated with low decay rate fuel cell stacks

Created: Feb-27-08 12:17 PM

Team 1

High Voltage Time

High Current Time

Hot Ambient Temp

Short Trips

Starts/hour

Team 2

Team 3

Team 4

Hot Starts

Starts/hour

High Voltage Time

Low Current Time

Idle Time

Cold Starts

Warm Ambient Temp

Long Trips

High Voltage Time

Low Current Time

Hot Starts

Idle Time

Short Trips

Starts/hour

1) On-going fuel cell degradation study using Partial Least Squares (PLS) 

regression model for each team.

2) Teams’ PLS models have a high percentage of explained decay rate variance, 

but the models are not robust and results are scattered. 

H*

L**

High Voltage Time

Low Current Time

Idle Time

Low Voltage Time

Cold Starts

Hot Ambient Temp

Short Trips

Starts/hour

H*

H*

H*

H*

L**

L**

H*: Factor group associated with high decay rate fuel cell stacks

L**: Factor group associated with low decay rate fuel cell stacks

Created: Feb-27-08 12:17 PM

Figure 6: Primary Factors Affecting Learning Demonstration Team Fuel Cell 

Degradation 

5.4 Status of On-Board Hydrogen Storage Technology 

Additional hydrogen storage data have recently been reported to NREL using a 

more detailed hydrogen storage system breakdown spreadsheet. This spreadsheet 

included the breakdown of the mass and volume due to the hydrogen itself, the 

pressure vessel, and the balance-of-plant. The hydrogen storage balance-of-plant 

category includes: 

• Controls and measurement (hydrogen storage-specific electronics) 

• Fuel delivery to powerplant (plumbing) 

• Hazard mitigation components (hydrogen sensors, pressure release 

devices, venting) 

• Fueling equipment (filters, nozzle receptacle, piping, communications, 

grounding) 

• Mounting brackets, auxiliary equipment (thermal management, etc.). 

While early published results for this project only included first-generation 

vehicle data (and included a mix of 350 bar, 700 bar, and liquid hydrogen storage 

systems, the spring 2008 detailed data were supplied for both first- and secondgeneration hydrogen storage systems. We compared the difference in the ranges 

of mass stored in the teams’ 350 bar and 700 bar systems as shown in Figure 7, 

10 
and found the best 700 bar Learning Demonstration weight percentage improves 

from just under 4% to just under 4.5% (the DOE 2007 target for advanced 

materials-based storage technologies is 4.5%). It is important to recognize that 

the second-generation (700 bar) systems also typically have the benefit of 

economies of scale because they will have a larger total mass of hydrogen stored 

to meet the 250-mile range target. 

9

Figure 7: Weight Percent of Hydrogen Stored in 350 bar and 700 bar Systems 

We also compared the storage technologies for volumetric capacity (how much 

hydrogen can be stored per volume), shown in Figure 8. This is where the 

advantage of going to a higher pressure really emerges, showing the potential for 

a significant increase in the mass of hydrogen stored per liter (the best of the 

range increasing from almost 20 g/L up to about 25 g/L), making the packaging of 

the system on a vehicle more attractive. 

Finally, the percentage breakdown by each of these categories was averaged 

across the four teams so that the differences between 350 bar and 700 bar could 

be examined for mass and volumetric characteristics (Figure 9). The pie-chart 

comparison shows that while the average hydrogen weight percentages are similar 

for 350 and 700 bar, and the pressure vessel and balance-of-plant at 700 bar take 

up a larger percentage of the system volume, the 700 bar systems are attractive 

because they enable a more compact package and extended range. 

350bar 700bar 0

1

2

3

4

5

6

7

8

Weight Percent Hydrogen (%)

Weight Percent Hydrogen

2015 DOE MYPP Target1

2010 DOE MYPP Target1

2007 DOE MYPP Target1

Created: Feb-15-08 7:32 AM

1

Targets are set for advanced materials-based hydrogen storage technologies.

11 
0.1

Figure 8: Comparison of Mass of Hydrogen Stored per Liter of System 

Figure 9: Average Breakout of Mass and Volume for Hydrogen, Pressure Vessel, 

and Balance-of-Plant 

 

Average Breakout of H2 Storage System Volume

73%

24%

3%

Average Breakout of H2 Storage System Mass

73%

23%

3.26%

 350 bar

35% 55%

10%

H2 Volume (%)

Pressure Vessel Volume (%)

Balance of Plant Volume (%)

26%

3.45%

71%

H2 Mass (%)

Pressure Vessel Mass (%)

Balance of Plant Mass (%)

 700 bar

Created: Feb-15-08 6:53 AM

350bar 700bar 0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

Mass H2 per Liter (kg/L)

Mass of Hydrogen Per Liter

2015 DOE MYPP Target1

2010 DOE MYPP Target1

2007 DOE MYPP Target1

Created: Feb-15-08 7:32 AM

1

Targets are set for advanced materials-based hydrogen storage technologies.

12 
5.5 Vehicle Refueling Performance 

Hydrogen vehicle refueling needs to be as similar as possible to conventional 

vehicle refueling to allow an easier commercial market introduction. Over 8,700 

refueling events have been analyzed to date, and the refueling amount, time, and 

rate have been quantified. The average time to refuel was 3.43 minutes with 87% 

of the refueling events taking less than five minutes. The average amount per fill 

was 2.25 kg, reflecting both the limited storage capacity of these vehicles (~4 kg 

max) and peoples’ comfort with letting the fuel gauge get close to empty (to be 

discussed later). DOE’s target refueling rate is 1 kg/minute, and these Learning 

Demonstration results indicate an average of 0.79 kg/min, with 24% of the 

refueling events exceeding 1 kg/minute (Figure 10). Therefore, we can conclude 

that high-pressure gases are approaching adequate refueling times and rates for 

consumers; however, the challenge is still in packaging enough high-pressure 

hydrogen onboard to provide adequate range, or finding alternate advanced 

hydrogen storage materials that can replace the need for high-pressure tanks. 

Figure 10: Refueling Rates 

The refueling histogram just discussed included all types of refueling events 

(communication and non-communication). Communication fills allow the 

refueling station to “talk” to the vehicle to know what temperature and pressure 

the tank is at to avoid overheating it. There has been much interest from industry 

and from the codes and standards community about the potential for 

communication fills to occur at a higher rate and with a more complete fill. 

Figure 11 shows two curves: the red curve is a spline fit to the histogram for noncommunication fills while the blue curve represents the communication fills. The 

center part of the graph shows a similar rate of fill for the communication and 

non-communication fills; however the communication fills are capable of having 

a higher fill rate (up to around 1.7 kg/min) as shown in the blue shaded region on 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0

100

200

300

400

500

600

Avg Fuel Rate (kg/min)

Number of Fueling Events

Histogram of Fueling Rates

All Light Duty Through 2007Q4

2006 Tech Val Milestone

2010 MYPP Adv Storage Materials Target

5 minute fill of

5 kg at 350 bar

3 minute fill of

5 kg at 350 bar

Average = 0.79

 % >1 = 24

Created: Feb-15-08 1:44 PM

Average rate: 0.79 kg/min 

24% of refueling events exceeded 1 kg/min

13 
the right. There is also a group of vehicle/station combinations still doing noncommunication fills at the slower rate of ~0.2 kg/min on the left portion of the 

graph, shaded in red. This rate of fill was established many years ago in 

California to provide a conservative and safe approach for refueling vehicles 

before much real-world experience had been gained. 

Figure 11: Fueling Rates – Communication and Non-Communication Fills 

Figure 12: Comparison of Fueling Rates by Year 

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0

50

100

150

200

250

300

350

400

Avg Fuel Rate (kg/min)

Number of Fueling Events

Histogram of Fueling Rates

Comm vs Non-Comm Fills - All Light Duty Through 2007Q4

Comm

Non-Comm

2006 Tech Val Milestone

2010 MYPP Adv Storage Materials Target

5 minute fill of

5 kg at 350 bar

3 minute fill of

5 kg at 350 bar

Fill Type Avg (kg/min) %>1 

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

Comm 0.94 36%

Non-Comm 0.66 20%

Created: Feb-27-08 11:26 AM

350

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0

50

100

150

200

250

300

Avg Fuel Rate (kg/min)

Number of Fueling Events

Histogram of Fueling Rates

All Light Duty by Year Through 2007Q4

2005

2006

2007

2006 Tech Val Milestone

2010 MYPP Adv Storage Materials Target

5 minute fill of

5 kg at 350 bar

3 minute fill of

5 kg at 350 bar

Year Avg (kg/min) %>1 

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

2005 0.66 17%

2006 0.72 20%

2007 0.86 28%

Created: Feb-27-08 11:39 AM

14 
When the data is analyzed by year (Figure 12), we find that this slower refueling 

rate was heavily used in 2006 but was almost completely phased out in 2007. 

With these differences in distribution in mind, the average fill rate for all 

communication fills is 0.94 kg/min vs. 0.66 kg/min for non-communication fills, 

with 36% and 20%, respectively, exceeding DOE’s 1 kg/min target. 

5.6 Vehicle Driving Behavior 

Some questions have arisen about whether the Learning Demonstration vehicles 

are being used like conventional vehicles, or whether their usage is being too 

“controlled” to match typical driving behavior. To investigate this we looked at 

the time of day people initiated their trips and which day of the week the trips 

were occurring on. Figure 13 shows a clock-face radial histogram, with the green 

data representing the time of day when people initiated their trips. Overlaid on 

top of that we have placed pink diamonds to show the national statistics based on 

the 2001 National Household Travel Survey (NHTS) Data [5]. What we find is 

that the Learning Demonstration vehicles are driven at similar times of day to the 

national statistics, with the exception of the late afternoon between 4 and 6 p.m. 

when the average person (nationally) is likely either picking up kids from school, 

driving home from work, or running errands. Since the first-generation Learning 

Demonstration vehicles are primarily used for professional or fleet activities, it is 

not surprising that there would be a difference. The percentage of trips taken 

between 6 a.m. and 6 p.m. is similar to the national statistics (88.7% vs. 81.5%). 

The nighttime driving behavior trend is also similar to the national statistics, 

although there are overall more evening trips driven nationally than within the 

Learning Demonstration. 

Driving Start Time - Day: DOE Fleet

9%

3

12

9

6

DOE Fleet

NHTS

Created: Feb-27-08 10:51 AM

Total Driving3 % of driving trips b/t 6 AM & 6 PM: 88.7% Events = 139968

1. Driving trips between 6 AM & 6 PM

2. The outer arc is set at 12 % total Driving.

3. Some events not recorded/detected due to data noise or incompleteness.

AM PM

% of NHTS trips b/t 6 AM & 6 PM: 81.5%

2001 NHTS Data Includes Car, Truck, Van, & SUV day trips

ASCII.csv Source: http://nhts.ornl.gov/download.shtml#2001

Figure 13: Driving Start Times between 6AM and 6PM Compared with National 

Statistics 

15 
We also examined the days of the week that people drove the Learning 

Demonstration FCVs and compared this with the national statistics. Nationally 

the trips are relatively uniform on weekdays, with a slight dip on the weekends, 

but the Learning Demonstration vehicles are rarely driven on the weekends. 

Additionally, Learning Demonstration vehicles have significantly more trips 

Tuesday through Thursday as compared to Monday and Friday, which does not 

reflect typical national behavior. While the day of the week doesn’t matter to the 

car’s performance, it might indicate that some of the weekend types of trips (for 

example: long trips to mountains or lots of short trips to the hardware store) are 

not being captured in the first generation vehicle duty cycles. 

We analyzed the length of trips and compared these results to national statistics, 

as shown in Figure 14. With more than 40% of the Learning Demonstration trips 

being less than one mile long, it is clear that the amount of time spent at low fuel 

cell power (discussed earlier) is due in part to a large number of short trips for 

which the vehicle is not likely accelerated to higher speeds and powertrain 

demands. This differs from the national driving statistics, which show that only 

about 10% of the trips are less than one mile long. If a large number of 

starts/hour is one of the major degradation factors, as has been reported at the 

laboratory scale, then this large number of short driving trips could be 

prematurely shortening the life of the Learning Demonstration fuel cells. Further 

investigation is necessary before that linkage can be made based on our analysis 

of the real-world data. 

0 5 10 15 20 25 0

5

10

15

20

25

30

35

40

45

Frequency (%)

Trip Length (miles)

Trip Length: DOE Fleet

DOE Fleet

NHTS

Created: Feb-27-08 11:56 AM

2001 NHTS Data Includes Car, Truck, Van, & SUV day trips

ASCII.csv Source: http://nhts.ornl.gov/download.shtml#2001

Figure 14: Trip Length Histogram Comparison with National Statistics 

16 
5.7 Vehicle Refueling Behavior 

As previously mentioned, with limited hydrogen refueling infrastructure and 

limited on-board hydrogen storage, some drivers do not like to let the tank get 

close to empty to minimize the risk of running out of fuel. To investigate this 

further, NREL analyzed the data submitted in a new and unique way, which was 

to look at what the fuel level in the tank was just prior to each refueling event. In 

some cases these data came from on-board data based on the pressure in the tank, 

and in other cases they came from refueling logs where each fill was assumed to 

be to the “full” level, allowing a subtraction of the amount fueled to determine the 

initial tank level. 

Figure 15 shows the results from this analysis, where a histogram has been placed 

radially on an image of a fuel gauge to make interpreting the graph as intuitive as 

possible. The level at which people most commonly refuel the Learning 

Demonstration vehicles is at just over 1⁄4 full; this covers 15% of the refuelings. 

While some drivers are letting the tank get even lower than that, few let it get 

close to being empty. Additionally, we placed a green needle on the chart which 

indicates the median tank level at fill (1⁄2 above, 1⁄2 below), which is a little above 

3⁄8 of a tank (40% of full). In the future, we would like to compare these data 

results to data from conventional liquid-fueled vehicles, if they exist publicly, to 

see if people are refueling their fuel cell vehicles differently than their 

conventional vehicles. 

Tank Levels: DOE Fleet

15%

E F

Created: Feb-27-08 10:51 AM

Total refuelings1 = 13085

1. Some refueling events not recorded/detected due to data noise or incompleteness.

Median Tank Level = 39% at Fill

2. The outer arc is set at 20% total refuelings.

3. If tank level at fill was not available, a complete fill up was assumed.

Figure 15: Hydrogen Tank Level at Refueling 

We also looked at the time of day people refueled in order to understand the usage 

patterns at the hydrogen refueling stations and better allow new or potential 

station operators to understand the potential demand by time of day. For 

traditional liquid fuels, with big tanker truck deliveries periodically, the time of 

17 
day people refuel does not normally matter. Instead, the station operator must 

simply ensure that the next tanker comes before he runs out of fuel. For today’s 

hydrogen fuel stations, with very limited storage capacity and some sites 

producing hydrogen throughout the day, it is important to know the time of day 

that people refuel in order to match the supply (on-site production) with the 

demand. 

We found that 86% of the fills occurred between 6 a.m. and 6 p.m., with 14% 

occurring at night. The distribution is relatively uniform with steady usage 

between 8 a.m. and 4 p.m., and a mild peak at lunchtime (9%). The conclusion 

from this analysis is that with a uniform distribution of when people refuel during 

the day, a station that has on-site production can either be sized to meet that 

demand during the day and then essentially shut off at night, or it can be sized 

(smaller) for the average over a 24-hour period, have a larger on-site hydrogen 

storage buffer, and run continuously. Finally, we looked at what day of the week 

people were refueling and found that the Learning Demonstration vehicles are 

primarily refueled Monday through Friday, with very few vehicles refueled on the 

weekend. This is consistent with the days of the week that people are driving the 

vehicles most and when the hydrogen stations that have attendants are open. 

6. Conclusions 

NREL has now analyzed data from almost three years of the five-year project 

with 92 vehicles having been deployed, 14 project refueling stations in use, and 

no fundamental safety issues identified. We’ve analyzed data from over 200,000 

individual vehicle trips covering 1,100,000 miles traveled and over 40,000 kg 

hydrogen produced or dispensed. With additional hours of operation accumulated 

on the fuel cell stacks the four-team average projection to 10% voltage 

degradation is 1,200 hours. During 2008, we will determine if we can improve 

the accuracy of our projections by adding a non-linear fit (or a two-step linear fit) 

to avoid overestimating the projected time as the accumulated hours continues to 

grow. 

To answer the question of what is causing the stacks to gradually degrade, NREL 

continues to characterize how each stack is used and perform multivariate 

analysis on this dataset to examine dominant variables affecting stack voltage 

degradation rates. Results to date indicate we are probably not going to be able to 

extract strong trends across all four teams due to differences among the teams, but 

that individual results may be useful to the teams individually and for feeding 

trends back into the R&D program. We’ve analyzed fuel cell system efficiency at 

1⁄4-power and compared it to the DOE target of 60%; system efficiency results 

from the four teams ranged between 52.5% and 58.1%. 

Using data on communication vs. non-communication fills, we found that 

communication fills demonstrated a higher rate of fill than non-communication 

fills while the slowest of the non-communication fill rates are being phased out. 

We also examined refueling and driving behavior, and found the Learning 

18 
Demonstration fleet to be representative of national statistics with the exception 

of fewer late afternoon and weekend trips, an abundance of short trips, and a 

shorter average distance traveled per day. Finally, we’ve published a total of 47 

CDPs to date and made them directly accessible to the public through our 

Hydrogen Technology Validation Web site 

(http://www.nrel.gov/hydrogen/proj_learning_demo.html). 

 

In the future, we will further explore the correlations of real-world factors 

influencing fuel cell degradation and strive to separate their interwoven 

dependencies. We will semi-annually (spring/fall) compare technical progress to 

program objectives and targets and provide results to the public by participating in 

technical conferences and writing reports. For the second-generation vehicles 

introduced in 2008, we will begin evaluating improvements in fuel cell durability, 

range, fuel economy, and safety, and publish results when there are sufficient 

second-generation vehicles to mask the companies’ identities. As an important 

part of the project, we will identify opportunities to feed project findings back into 

HFCIT Program R&D activities to maintain the project as a “learning 

demonstration.” 

7. References 

1. K. Wipke, S. Sprik, J. Kurtz, H. Thomas, “FCV Learning Demonstration: Project Midpoint 

Status and Fall 2007 Results,” Presented at EVS-23 Conference, Anaheim, CA, Dec. 2007. 

Also available on the World Wide Web: http://www.nrel.gov/hydrogen/pdfs/42197.pdf

2. K. Wipke, C. Welch, H. Thomas, S. Sprik, S. Gronich, J. Garbak, “Controlled Hydrogen Fleet 

and Infrastructure Demonstration and Validation Project—Initial Fuel Cell Efficiency and 

Durability Results,” World Electric Vehicle Association Journal, Vol. 1, 2007. Also available 

on the World Wide Web: http://www.nrel.gov/hydrogen/pdfs/40921.pdf

3. K. Wipke, S. Sprik, J. Kurtz, H. Thomas, “Learning Demonstration Interim Progress Report – 

September 2007,” NREL/TP-560-42264 (October 2007). Also available on the World Wide 

Web: http://www.nrel.gov/hydrogen/pdfs/42264.pdf

4. C. Welch, K. Wipke, S. Gronich, J. Garbak, “Hydrogen Fleet and Infrastructure Demonstration 

and Validation Project: Data Analysis Overview,” National Hydrogen Association Conference, 

Washington, DC (March 2005). Also available on the World Wide Web: 

http://www.nrel.gov/hydrogen/pdfs/37845.pdf

5. Oak Ridge National Laboratory, 2001 NHTS Data, http://nhts.ornl.gov/download.shtml#2001, 

ASCII.csv file. 

19 
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Fuel Cell Vehicle Learning Demonstration: Spring 2008 Results 

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