Conference Paper 

NREL/CP-560-42958 

November 2008 

Fuel Cell Vehicle Learning 

Demonstration: Study of Factors 

Affecting Fuel Cell Degradation 

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

Presented at FuelCell 2008: Sixth International Fuel Cell Science, 

Engineering and Technology Conference 

Denver, Colorado 

June 16–18, 2008 
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FUEL CELL VEHICLE LEARNING DEMONSTRATION: STUDY OF FACTORS 

AFFECTING FUEL CELL DEGRADATION 

Jennifer Kurtz 

Senior Engineer 

National Renewable Energy Laboratory 

1617 Cole Blvd., Golden CO 80401, 

303-275-4061, FAX 303-275-2905, 

Jennifer_kurtz@nrel.gov

Sam Sprik 

National Renewable Energy Laboratory 

1617 Cole Blvd., Golden CO 80401 

303-275-3000, FAX 303-275-2905 

Keith Wipke 

National Renewable Energy Laboratory 

1617 Cole Blvd., Golden CO 80401 

303-275-3000, FAX 303-275-2905 

ABSTRACT 

The 5 year "Controlled Hydrogen Fleet and 

Infrastructure Demonstration and Validation Project" (or Fuel 

Cell Vehicle Learning Demonstration) was initiated by the U.S. 

Department of Energy (DOE) in 2004. The purpose of the 

project is to conduct an integrated field validation that 

simultaneously examines the performance of fuel cell vehicles 

and the supporting hydrogen infrastructure. Four industry teams 

are currently operating more than 92 vehicles and 14 refueling 

stations throughout the United States. More than 40 additional 

vehicles and several additional refueling stations will be added 

to the project through 2009. 

At the National Renewable Energy Laboratory 

(NREL), on-road driving and refueling data are analyzed to 

assess the technology status and progress, as well as to provide 

feedback to the hydrogen research and development 

community. A new/updated set of public results, in the form of 

composite data products (constructed to protect the intellectual 

property of the four teams), is released twice a year in the 

spring and fall. In addition to the public results, detailed 

analyses results are shared with each participating team. 

One of the analyses studies fuel cell degradation. The 

study includes following the fuel cell performance degradation 

trends, e.g. identifying fuel cell stacks that are decaying at a 

different rate than others of a similar design and in the same 

fleet, and explores connections between the real world data and 

fuel cell degradation. This study differs from other degradation 

studies in a lab setting or at the single cell level because this 

study uses full scale fuel cell stacks in vehicles with on-road 

driving and refueling. 

In the study, researchers investigate degradation 

factors by applying multivariate analyses for each individual 

team and for the combination of all four teams. Detailed results 

are reviewed with the individual teams in an effort to improve 

each analysis iteration and comprehension of the results. This 

paper will detail NREL's study of fuel cell degradation factors 

by describing the process, reviewing the latest public results, 

and reporting on any observed dominant factor groups affecting 

fuel cell degradation. 

FUEL CELL VEHICLE LEARNING DEMONSTRATION 

PROJECT 

Hydrogen fuel cell vehicles 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. 

 1 
Hydrogen can be derived from renewable sources, natural gas, 

coal, or nuclear energy. This versatility would enable 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 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. 

The 5 year "Controlled Hydrogen Fleet and Infrastructure 

Demonstration and Validation Project" (or Fuel Cell Vehicle 

Learning Demonstration) was initiated by the DOE in 2004. 

The purpose of the project is to conduct an integrated field 

validation that simultaneously examines the performance of fuel 

cell vehicles and the supporting hydrogen infrastructure. Four 

industry teams are currently operating more than 92 vehicles 

and 14 refueling stations throughout the United States. More 

than 40 additional vehicles and several additional refueling 

stations will be added to the project through 2009. Automotive 

OEMs are leading three of the four teams, and an energy 

provider leading the fourth. Figure 1 shows the teaming 

arrangement of the four teams along with their first-generation 

fuel cell vehicles. The major companies making up the four 

teams are as follows: 

• Chevron and Hyundai-Kia 

• Chrysler and BP 

• Ford Motor Company and BP 

• General Motors and Shell 

FIGURE 1: FIRST GENERATION VEHICLES WITH OEM & 

FUEL SUPPLIERS IDENTIFIED 

The objective of the Fuel Cell Vehicle Learning 

Demonstration Project is to conduct parallel learning 

demonstrations of hydrogen infrastructure and fuel cell vehicles 

to allow the government and industry to assess progress towards 

technology readiness. We are accomplishing this objective by 

validating the vehicle and infrastructure as a complete 

integrated system. The quantity and breadth of data collected 

and analyzed enables researchers to evaluate technology status 

against DOE program targets, as well as provide feedback to 

DOE-funded research and development as appropriate. The 

ability to feed results back into research and development 

efforts as an integrated part of DOE’s program makes this 

project unique compared to typical demonstration projects. 

TABLE 1: KEY HYDROGEN LEARNING DEMONSTRATION 

TARGETS 

The targets listed in Table 1 address key barriers to 

successful market entry. 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 as a requirement for market acceptance. 

Fuel Cell Degradation Study 

The project has passed its mid-point and there are 

fuel cell stacks with more than 1,000 operating hours. 

NREL becomes more confident about projections of fuel 

cell degradation as more data and operating hours are 

added. Which factors affect fuel cell degradation is an 

important question to investigate because fuel cell 

degradation has been identified as a key technology hurdle 

along the path of fuel cell commercialization. For this 

reason, fuel cell durability is a key metric in the FCV 

Learning Demonstration project. As specified in Table 1, 

the DOE has established fuel cell performance targets for 

2006 and 2009, with the general expectation that 

approximately a 5,000 hour operation time will be 

necessary for commercial fuel cell vehicles. 

Performance Measure 2009* 2015** 

Fuel Cell Stack Durability 2000 hours 5000 hours 

Vehicle Range 250+ miles 300+ miles 

Hydrogen Cost at Station (untaxed) $3/gge $2-3/gge 

 * To verify progress toward 2015 targets 

 ** Subsequent projects to validate 2015 targets 

 2 
Objectives 

A number of degradation experiments are based on single cell 

testing in a laboratory environment and there is a gap in 

translating single cell degradation experiment findings to full 

scale, real world applications. Within the FCV Learning 

Demonstration project, we have access to a large data set of onroad fuel cell vehicle trips. (As used here, an FCV trip is a key 

on, key off scenario. The vehicle may not have gone anywhere, 

but there was at least a signal to start the fuel cell.) Studying 

these data may allow us to bridge the gap between single cell 

laboratory experiments and full scale applications. 

The foundation of this degradation study is built on 

trying to learn if there are any observable relationships between 

the FCV Learning Demonstration real world data and fuel cell 

degradation. The available data may not be sufficient to identify 

all of the possible degradation factors, but a vast amount of data 

has been collected in this project and these data can be 

processed to look at driving patterns and conditions, 

as well as fuel cell performance. It has already been 

observed [1] that fuel cells are not decaying at the 

same rate when compared among the project 

partners. In addition, fuel cells are not degrading at 

the same rate within fleets or within similar designs. 

This study is looking for general factors affecting 

degradation as well as attempting to answer why 

there are varying rates of fuel cell degradation. 

Because of the highly proprietary nature of 

fuel cell performance, many analysis details are 

protected. Public results are presented in a manner 

that will provide the findings of the degradation 

study while protecting intellectual property and 

identity of the project partners. There is a separate 

analysis for each team and those results are shared 

in detail with each team. Collaboration between 

NREL and the teams is critical for increasing the 

value of this degradation study. Many questions, 

experiences, and ideas are raised during individual 

conversations between NREL and the teams. NREL 

also hopes to supplement and/or complement existing team 

efforts to identify contributors to fuel cell degradation. Table 2 

has a list of objectives for the study of factors affecting fuel cell 

degradation. 

TABLE 2: FUEL CELL DEGRADATION STUDY OBJECTIVES 

Investigate relationship between on-road data and fuel 

cell degradation 

Address lack of full scale fuel cell degradation 

analyses/experiments 

Investigate reasons for differing fuel cell decay rates 

Collaboration with project partners 

Public reporting of any findings 

Data Collection 

NREL’s approach to accomplishing the project’s objectives is 

structured around a highly collaborative relationship with each 

of the four industry teams. We receive raw technical data from 

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 DOE 

Hydrogen 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. Figure 2 shows the flow of data and results, along 

with the quantity of data received per month since September 

2004. To date, NREL has received data from more than 

203,000 individual vehicle trips, amounting to more than 49 GB 

of raw, on-road data. 

On-Road Data Received -- Running Totals

49245

203379

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On-Road Data Received -- Running Totals

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FIGURE 2: PROCESS FLOW FOR HYDROGEN SECURE 

DATA CENTER ANALYSES AND RESULTS

To ensure that we are providing value to the hydrogen 

community, we publish composite data products (CDPs) twice a 

year at technical conferences. 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. NREL has created a 

web site to house all of the current composite data products at: 

http://www.nrel.gov/hydrogen/cdp_topic.html. We also provide 

our detailed analytical results privately back to each individual 

company in order to maximize the industry benefit from 

NREL’s analysis work and obtain feedback on our 

methodologies and results. 

 3 
Approach 

Why did we apply a multivariate analysis method for the 

degradation study? First, the data set (as can be seen from 

Figure 2) is large, with many different types of data including 

length of trip, ambient temperature of trip, and fuel cell voltage 

and current. Data from this project are not controlled 

specifically for a degradation experiment; therefore we do not 

have the advantage of analyzing a data set that is truly 

independent. Parts of the data may be redundant, correlated 

with other data sections, and relationships or patterns may be 

hidden in the complexity and size of the data. A multivariate 

analysis will look for these relationships and highlight 

variations as well as similarities within the data set. Initially, a 

single factor study was established to look for isolated, 

dominant factors contributing to fuel cell degradation one 

variable at a time. Not surprisingly, no single factor was 

identified. The uncontrolled data set, interdependencies within 

the data, and a belief that in real world applications degradation 

would be affected by factor combinations, directed us to a 

multivariate analysis. 

There are many multivariate methods that we could 

apply to this data set. We started out by looking at Principle 

Component Analysis (PCA) and Factor Analysis (FA). PCA 

results for this data set would look for any linear combination of 

variables that identified differences among the samples. After a 

few iterations, it became obvious that while we may be 

highlighting relationships within the data, we were not targeting 

the relationship we were after – the relationship between the 

variables and fuel cell degradation. 

The next step was to attempt to fit a regression model 

to the data set. The regression model looks specifically for 

relationships between two data sets, in our case, fuel cell decay 

rate and the driving data. Partial Least Squares (or Projection to 

Latent Structures, PLS) is the specific regression method used. 

A PLS analysis allows us to focus on any relationships between 

fuel cell degradation and the driving data because a PLS model 

is built to explain the maximum amount of variance in the decay 

rate. (PLS method is based on the PCA method and many 

results can be interpreted in the same way.) A Latent Variable 

(LV) is a linear combination of variables that account for decay 

rate variance. The decay rate estimates are the primary 

observation (or Y, the dependent data set) and the driving data 

variables are the predictors (or X, the independent data set). 

Currently the PLS models are used as a tool to identify and 

describe possible correlations between the data set and decay 

rate. It is not used as a prediction tool (a common use of PLS 

models) for new samples, although this is a possible long term 

goal for this study. Figure 3 depicts the process we have used 

for the degradation study. 

FUEL CELL DEGRADATION ANALYSIS PROCESS 

Data Processing 

We spent some time identifying possible variables that 

could, or should, be included in the degradation study. The 

initial variable list was limited to data that were already being 

provided to the NREL, which varies by team. Input data for the 

degradation study were organized by fuel cell stack and many 

variables were broken down into sub-variables or bins. An 

example of a variable that was broken down into bins is length 

of trip. For one collection of fuel cell data, trip length may vary 

substantially. Using an average value may not be a true 

indication of the driving patterns. Instead of one average trip 

length, all trips were collected into multiple trip length bins; 0-

5, 5-10, 10-20, 20-30, and greater than 30-minute long trips. 

Fuel Cell 

Degradation Study 

Data 

Sample & 

Variable Sets 

Multivariate Analysis 

Single Factor 

Analysis 

Interpretations 

& Iterations 

Analysis Report - 

CDPs and Teams 

Approach 

FIGURE 3: NREL MULTIVARIATE DEGRADATION STUDY 

FLOW CHART

The data are collected in actual driving situations with 

real-world data acquisition issues. It was necessary to use some 

filters to clean up the data set to ensure the inclusion of only 

trips with fuel cell operation. There are also differences in the 

supplied data and fuel cell systems of each team. Therefore 

each team has a separate data set and regression model and a 

combined data set for all teams is used for the DOE fleet level 

regression analysis, for a total of five analyses that are updated 

regularly. 

A critical variable in the multivariate data set is the fuel 

cell decay rate. The method for estimating a fuel cell stack 

decay rate is as follows (graphically represented in Figure 4 and 

Figure 5). First, the fuel cell voltage, current, and operating 

 4 
hours are collected from the on-road trip data files. Every x 

points (x may vary based on the team and data) a non-linear fit 

is applied to the voltage (V) and current (I) points. A high 

current point has been selected for each team. The voltage is 

stored at this current point on each fit. 

FIGURE 4: EXAMPLE OF VI CURVE FIT 

0 500 1000 1500 2000 200

220

240

260

280

300

Voltage vs. Operation Hours at 300A: Vehicle19-Stack1

Operation Hours

Predicted Voltage at 300A

1339 hrs1434 hrs1550 hrs

10% drop (248V)

Nominal (276V)

Created: Apr-17-08 12:04 PM

warm-up time=10 min

pwr rate filt=1000 kW/s

amp rate filt=1000 A/s

pts per fit=2500

1 data pt every 1 seconds

FIGURE 5: EXAMPLE OF DECAY RATE ESTIMATE 

 5 
The voltage points are plotted against the fuel cell 

operating hours and a linear fit is applied. The slope of the fit is 

the decay rate estimate. The nominal voltage is the y-intercept 

of the line and the projection to 10% drop is the operating hour 

point where the line reaches 90% of the estimated nominal 

voltage. 

Our goal for this multivariate degradation analysis is 

not to predict how long the fuel cell will be able to meet the 

vehicle demands. The 10% mark is a DOE target or benchmark 

to evaluate the status and progress of the fuel cells in the 

project. The method for estimating the fuel cell decay rate is the 

same for all the fuel cell stacks and may not be sufficient for 

predicting the time until end of life, but it does provide a fair 

comparison of how quickly the performance of each fuel cell is 

degrading. The decay rate estimate also allows us to compare 

the rate of one fuel cell to another fuel cell and identify high 

decay rate fuel cells. 

Figure 6 combines all the fuel cell operating hours and 

degradation estimates into one figure that protects the teams’ 

identities and details. The maximum accumulated hours on a 

fuel cell stack, without stack repair, has surpassed the 2006 

target of 1,000 hours and the range of maximum operating 

hours is approximately 300-1200 hours. The range of average 

accumulated operating hours is approximately 200-650 hours. 

The range is set by the span between teams, where each team 

has one point. The data presented in Figure 6 are from first 

generation fuel cell vehicles. Second generation systems will be 

compared against the 2009 targets. The range of projections to a 

10% drop in voltage crosses the 2006 target of 1,000 hours, 

with the average projection exceeding the 2006 target. 

0

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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 6: CDP #1 - ACCUMULATED HOURS AND PROJECTION TO 10% VOLTAGE DROP 

 6 
Sample & Variable Set 

Criteria for including the samples and variables started 

out as a wide net, looking for any and all possible data. Some 

filters were required to focus in on samples and variables that 

would accurately represent the fuel cell data set. Samples were 

included based on available data, operating hours, and 

confidence in the decay rate estimate. Variables were included 

based on available data and possible relevance to fuel cell 

degradation. As the study evolved, variables that did not appear 

to be contributing to the model were removed (like region and 

filling station). Variables were also added, removed, or 

modified based on communications with the teams. Processing 

improvements were made and samples and variables were 

added or removed with each iteration in an effort to improve the 

interpretation of the results. Table 3 has a list of possible 

variable categories and Figure 7 is a data set example with ‘n’ 

number of stacks and ‘i’ number of included variables. The 

variable categories attempt to include variables that are thought 

to contribute to fuel cell degradation; including variables such 

as the number of starts/hour and extreme voltage and current 

operating time. 

TABLE 3: EXAMPLE OF VARIABLE CATEGORIES 

Fuel Cell Voltage and Current 

Install Date 

Starts per hour 

Idle Time 

Time between Trips 

Trip Length 

Ambient Trip Temperature 

Speed 

Fill Data 

Location 

The settings, samples, and variables may vary for each 

team, analysis iteration, and addition of new data. A 

classification was applied to samples, in part because of team 

variations. The classification is based on the decay rate within a 

fleet’s samples and is broken into three classes, high, average, 

and low. The classification of samples based on decay rate 

allows for a simple, visual check for groups of either high or 

low decay rate samples. 

FIGURE 7: EXAMPLE OF MULTIVARIATE DATA SET WITH 

‘n’ SAMPLES AND ‘i’ VARIABLES 

Multivariate Analysis 

As mentioned earlier, the multivariate method applied 

here is PLS. The observation (Y data set) is the samples’ decay 

rate and the predictors (X data set) are the sample driving data 

submitted to NREL from on-road fuel cell vehicle trips. The 

data are scaled and mean-centered in a pre-processing step 

because of the wide range of variable values possible in the data 

set, which includes data through December 2007. 

 In the HSDC, we have created a graphical user 

interface (GUI) called “Correlate” (see 

Figure 8) that is part of a suite of interfaces designed to process 

large amounts of data efficiently and to provide a common 

location to view results and perform additional analyses. 

FIGURE 8: NREL FAT CORRELATE GUI 

 

The Correlate GUI can be broken into three sections; 

inputs (top left in Figure 8), outputs, and additional interfaces 

(lower left in Figure 8). In the inputs section of the GUI, the 

user can include and adjust the creation setting of the data set 

and PLS model. Data settings include: company, vehicle type, 

minimum operating hour sample filter, data set iteration, range 

for decay rate classification, and the option to view and modify 

the included samples and variables. PLS model settings include: 

data set selection, whether the data is input as percentages or as 

actual values, model iteration, and the number of latent 

variables (LVs) used to construct the model. In the outputs 

section, the user can easily view figures for the data and the 

model plots, as well as the model statistics. The outputs section 

also has an area where notes can be collected and a report on 

the analysis can be created. 

Decay 

Rate 

DR1 

DR2 

. 

. 

. 

. 

DRn 

 

Data(1,i) 

Data(2,i) 

. 

. 

. 

. 

Data(n,i) 

 Variables 

Data(1,1) 

Data(2,1) 

. 

. 

. 

. 

Data(n,1) 

Sample 

. . . 

. . . 

. . . 

. . . 

. . . 

. . . 

. . . 

Data(1,2) 

Data(2,2) 

. 

. 

. 

. 

Data(n,2) 

FC1 

FC2 

. 

. 

. 

. 

FCn 

 7 
Because of the proprietary nature of issues related to 

fuel cell degradation and the fuel cell system, the actual analysis 

will not be described in detail, except for the publically 

available CDPs on this subject. Instead, a simulated example 

will be described and discussed in an effort to highlight the 

interpretation and iteration step. 

Interpretation and Iterations 

The intention of the regression model applied to the fuel 

cell vehicle data is to provide insight into any correlations 

between fuel cell decay rate and the driving data. A regression 

model may also be used as a tool for predicting the outcome for 

additional samples, but that is not the purpose of this study. 

The PLS model is assembled in a way that places the focus 

on expressing hidden links relating the data and fuel cell 

decay rate. We will discuss a few of the model outputs that 

are necessary for interpreting the model. 

 8 

There are a number of model outputs that will help 

highlight any correlations. In general, creating the model is 

more straightforward than interpreting the model results. A 

latent variable (LV) is created in the model as a collection 

of the input variables, collected together to explain 

variance in the decay rate; this is shown as a percent of 

explained variance. The amount of explained variance is 

useful for evaluating the contribution of a LV and thus the 

contributions of the input variables in that LV. The 

contribution of an input variable in a LV is measured by the 

variable loading. If a variable is important in the makeup of 

a LV, it will have a high variable loading (positive or 

negative). Loadings will also draw attention to interactions 

(similar and opposite) of variables. With a non-designed 

data set (the input variables are not necessarily independent 

of one another) like the one in this study, dependent 

relationships within the input variables may also be found 

by viewing a loadings plot. A loadings plot can be viewed 

as a single LV and against other LVs. 

Just as the input variables are partly described by the LV 

loadings, the samples are partly described by the LV scores. A 

high (positive or negative) sample score on a LV implies that 

LV explains that sample. Sample relationships can be identified 

with scores. Samples with similar scores will have commonality 

within their input variables. Groups of samples with high decay 

rates are particularly interesting in this study because if those 

high decay rate samples are isolated from the remaining 

samples, the reason for a high decay rate may be found in 

differences of the sample driving data. Scores can also be 

plotted with a single LV and against other LVs. As the 

magnitude of a score or loading increases, the amount of 

variance captured by a LV increases. 

When trying to make the connection between sample 

groups and the relationship to the input variables, one must look 

at both the sample scores plot and variable loadings plot. One 

graphical representation of the combined details is a biplot, 

which overlays the sample scores on the variable loadings, 

Figure 9. If a sample has a higher than average value in a 

variable when that variable has a high positive loading on a LV, 

then the sample has a high, positive score on that same LV. If a 

group of high decay rate stacks exists, we look for the variables 

in the same area, or directly opposite. These variables may be 

the link between the data and a partial explanation of decay rate 

variance. Figure 9 through Figure 11 are examples created from 

a simulated data set in an attempt to help depict the 

interpretation methods. Figure 9 highlights some possible 

sample groups and the corresponding variables, and identifies 

an outlier. 

-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 -0.3

-0.2

-0.1

0

0.1

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0.5

LV 1

LV 2

Fake Data - Biplot 

LV 2 

Ave DR

High DR

Low DR

variable loading

x-axis zero

y-axis zero

LV1: ~ 72% explained DR variance

LV2: ~ 15% explained DR variance

Sample Scores

Factor Loadings Stack21

Stack25

Stack28

Stack31

Stack5

Stack17

Stack2 Stack15

0-30 min b/t Trips

0-5 min Trips

0-20oC Trips

BoLV

Starts/hr

Install Date

20-30 min Trips

A

B

Outlier

LV1

Possible 

Sample Groups

LV1 Factors

(orange circles)

LV2

FIGURE 9: SIMULATED DATA – BIPLOT EXAMPLE 

The PLS model is a linear regression fit of the data and 

there is a regression coefficient for each variable. The 

regression coefficient is another output that helps the user 

understand how the variable is contributing to the overall 

model, as well as the directional relationship between the 

variables and the decay rate. These regression coefficients are 

not used without evaluation at the LV level because there may 

be variables with a low regression coefficient that could still tell 

a story about a relationship between the variables and decay 

rate for a LV. Because we are using PLS as a tool to identify and 

describe the relationships, variables with low regression 

coefficients will not be removed simply based on the regression 

coefficient. Figure 10 is an example of a regression vector, ypred

= x*a, where ‘x’ is the sample data and ‘a’ is the regression 

vector. 
5 10 15 20 25 -0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Variable

Reg Vector for Y 1

 starts/hr

 installDate

 BoL V

 %Time at Idle

 %Time at 0-5% Power

 %Time at 5-20% Power

 %Time at 20-40% Power

 %Time at >40% Power

 11%Trips,0-5 mins long

 %Trips,5-10 mins long

 %Trips,10-20 mins long

 %Trips,20-30 mins long

 %Trips,>30 mins long

 %Trips,deltaT 0-30mins

 %Trips,deltaT 30-120mins

 %Trips,deltaT 120-240mins

 %Trips,deltaT >240mins

 %Trips,0-1 mile

 %Trips,1-5 miles

 %Trips,5-20 miles

 %Trips,>20 miles

 %Trips,0-20C

 %Trips,20-40C %Trips,>40C

 Design

Variables/Loadings Plot for XdataFake

FIGURE 10: SIMULATED DATA – REGRESSION VECTOR EXAMPLE 

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 -1.5

-1

-0.5

0

0.5

1

1.5

2

Y Measured 1

Y Predicted 1

 Stack1

 Stack14

 Stack19

 Stack4 Stack16

 Stack6 Stack20

 Stack8

 Stack9

 Stack10

 Stack7 Stack12

 Stack13 Stack5

 Stack15

 Stack2

 Stack17

 Stack18 Stack11

 Stack3

 Stack21

 Stack22

 Stack23

 Stack24

 Stack25

 Stack26

 Stack27

 Stack28 Stack29

 Stack30

 Stack31

Samples/Scores Plot of XdataFake

R^2 = 0.822

2 Latent Variables

RMSEC = 0.42863

RMSECV = 0.50512

Y Predicted 1

Ave DR

High DR

Low DR

1:1

x-axis zero

y-axis zero

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 -1.5

-1

-0.5

0

0.5

1

1.5

2

Y Measured 1

Y Predicted 1

 Stack1

 Stack14

 Stack19

 Stack4 Stack16

 Stack6 Stack20

 Stack8

 Stack9

 Stack10

 Stack7 Stack12

 Stack13 Stack5

 Stack15

 Stack2

 Stack17

 Stack18 Stack11

 Stack3

 Stack21

 Stack22

 Stack23

 Stack24

 Stack25

 Stack26

 Stack27

 Stack28 Stack29

 Stack30

 Stack31

Samples/Scores Plot of XdataFake

R^2 = 0.822

2 Latent Variables

RMSEC = 0.42863

RMSECV = 0.50512

Y Predicted 1

Ave DR

High DR

Low DR

1:1

x-axis zero

y-axis zero

FIGURE 11: SIMULATED DATA – PREDICTED VERSUS MEASURED EXAMPLE 

 9 
When interpreting regression model results, there are a 

couple of points to watch out for. The model may be over-fitting 

the data and giving a false sense of model accuracy, or the 

model may not be able to separate the noise from the relevant 

correlations, if there are any. This may be especially possible in 

a study where the data is not accumulated in a controlled, 

design of experiments, manner. The model statistics and sample 

outliers can be a signal of potential problems with the model. 

A couple ways to find outliers can be done by looking 

at the sample scores and/or residuals. Outliers can be an 

important step in interpreting a model because an outlier may 

truly be different than the rest of the samples and contain key 

data patterns that will help unlock the meaning of other sample 

groups. An outlier could just as easily be a sample with data 

errors or a sample that should not be compared with the rest of 

the samples. An outlier may be disproportionately influencing 

the model and may have to be removed to have a more accurate 

interpretation of the model. Figure 9 shows an outlier in a 

scores plot and Figure 11 shows the predicted versus measured 

example plot. You can see that an outlier in one plot is not 

necessarily an outlier in another plot. 

Model statistics are another way to check how well the 

model is describing the data. The explained variance, 

correlation coefficient, cross validation, and root mean square 

errors will all help determine how good the model is. A high 

correlation coefficient (R2

), typically greater than 0.7, is a signal 

that the model is doing a good job of fitting the data. The use of 

a cross validation method (in this study the cross validation 

method is “leave one out”), will help measure the model’s 

ability to predict the dependent variable. With a cross validation 

method included, there will be two error numbers for the model; 

Root Mean Square Error of Calibration (RMSEC) and Root 

Mean Square Error of Cross Validation (RMSECV). Ideally, the 

RMSECV would be within two times the value of the RMSEC. 

If the RMSECV is much greater than the RMSEC, the model is 

not robust and may be highly dependent on a few of the 

samples. These statistics must be used with one another to 

provide an overall picture of the model’s validity. 

While interpreting the analysis results, there are many 

questions that are looked at by running the analysis again but 

with variations on the inputs. A number of iterations are 

typically needed to improve the interpretation of the model and 

develop conclusions. 

Analysis Reporting 

Public Results We make every effort to create public 

degradation results that are detailed and significant. There are a 

number of hurdles with the public version of the degradation 

results. One is that all public results must protect team identity 

and intellectual property. A second hurdle is the analysis 

complexity and our ability to form a clear and concrete 

conclusion. The majority of value added from this analysis is 

within the individual team conversations covering the details of 

the analysis variation and interpretations. There are two current 

CDPs relating to the multivariate degradation analysis, CDP#48 

and CDP#49. 

DOE Fleet

High Current Time

Hot Starts

Starts/hour

Low Voltage Time

High Voltage Time

Cold Starts

Short Trips

0 Speed Trips

Hot Ambient Temp

H*

H*

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

regression model for combined Learning Demonstration Fleet.

2) DOE Fleet model has a low percentage of explained decay rate variance. 

Created: Feb-21-08 9:32 AM

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

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

DOE Fleet

High Current Time

Hot Starts

Starts/hour Due to differences among teams, the 

DOE Fleet Analysis results are spread 

out and concrete conclusions are difficult 

to draw.

Individual team analyses (CDP#49) 

focused on patterns within a fleet.

Low Voltage Time

High Voltage Time

Cold Starts

Short Trips

0 Speed Trips

Hot Ambient Temp

H*

H*

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

regression model for combined Learning Demonstration Fleet.

2) DOE Fleet model has a low percentage of explained decay rate variance. 

Created: Feb-21-08 9:32 AM

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

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

Due to differences among teams, the 

DOE Fleet Analysis results are spread 

out and concrete conclusions are difficult 

to draw.

Individual team analyses (CDP#49) 

focused on patterns within a fleet.

FIGURE 12: CDP#48 – VARIABLE GROUPS ASSOCIATED 

WITH FUEL CELL DEGRADATION FOR COMBINED DOE 

LEARNING DEMONSTRATION FLEET 

Figure 12 depicts a summary of the spring 2008 DOE fleet-level 

degradation analysis, which is just one piece of the degradation 

study. We are looking for groups of samples and groups of 

variables, specifically within the extremes of the samples’ decay 

rate range. There are two boxes of input variables specified in 

Figure 12. Each box sets apart a variable group that is attached 

to either high or low decay rate sample groups. In general, if a 

box is associated with a high decay rate sample group, those 

samples have higher than average time (or # of trips) for the 

variables listed in the box. The box of variables is a reminder 

that the potential relationships between the input variables and 

fuel cell decay rates are based on a collection of variables, not 

the impact of an isolated variable. 

The number of boxes labeled in CDP#48 and CDP#49 

depend on the analyses results. If there were groups of variables 

that could be considered the opposites of those variable groups 

already categorized, they were not specifically labeled in 

CDP#48 and CDP#49. For example, variable groups associated 

with low decay rate samples are only labeled if the opposite 

group for high decay rate samples is not already specified. 

The DOE fleet analysis explains a lower overall decay 

rate variance than the individual team analyses, primarily 

because of the differences among the teams. The thought 

process of trying to connect the analysis results to a strong 

correlation with fuel cell degradation is more straightforward in 

the team analyses, than in the LD fleet analysis. However, the 

team analysis results still remain scattered and it is difficult to 

determine tangible patterns and correlations with the data and 

fuel cell degradation. Figure 13 is a summary of the individual 

team analyses. There is no one dominant variable group among 

all four teams, which emphasizes the earlier statement that the 

combined DOE fleet is more difficult to interpret because of 

differences between the teams. 

 10 
CONCLUSIONS 

 hours. 

FIGURE 13: CDP#49 - VARIABLE GROUPS ASSOCIATED 

WITH FUEL CELL DEGRADATION FOR EACH TEAM 

 

Even though there are no clear, common variable 

groups among the four teams, a handful of variables, like starts 

per hour and high voltage and current are listed with all teams. 

Each team’s fuel cell system may be configured differently and 

will therefore react differently to the range of vehicle demands. 

The interaction between vehicle demand and how the fuel cell 

system responds relative to fuel cell degradation, could be one 

possible summary statement. For example, how a fuel cell 

system responds to vehicle demand may make it more 

susceptible to hard driving cycles. Impacts of driving patterns 

(and the fuel cell system’s sensitivity to different drive 

tendencies) may partly answer the question of why fuel cell 

stacks in a fleet and of a common design are decaying at 

different rates. The individual team analyses have a higher 

correlation coefficient than the DOE fleet analysis and seem to 

fit the data better, but all the analyses have a high RMSECV 

value and are not yet robust models. 

Detailed Results Detailed descriptions and data on 

each team analysis are sent as a supplement packaged with 

CDP#49 to the teams. As can be seen in Figure 13, individual 

team analyses are varied and not all team analyses have clear 

distinction among the samples, variable groups, and driving 

data. We discuss the interpretations and possible analysis 

iterations with each team. The process of running through many 

analysis iterations is helped along by the creation of the 

correlate GUI which provides more flexibility to change, 

review, and comment on the analysis iterations. The GUI allows 

this degradation study to evolve over time. 

The Fuel Cell Vehicle 

Learning Demonstration project 

has completed over two years of 

operation, data accumulation, and 

reporting of detailed and 

composite data products. In spring 

2008 there were 47 CDPs were 

released [4]. All CDPs, previous 

versions, and papers / 

presentations can be viewed at 

http://www.nrel.gov/hydrogen/proj

_learning_demo.html. The amount 

of actual operating hours 

accumulated on the fuel cell 

stacks continues to increase, as 

does NREL’s confidence in the 

fuel cell degradation projections 

and trends as well as NREL’s 

ability to apply this degradation to 

the second generation fuel cell 

vehicles, for which we have just 

begun receiving data. The maximum fuel cell operating hours 

have surpassed the first actual operating hours target of 1,000 

hours and the average projection of fuel cell operating hours to 

10% voltage degradation has surpassed the first degradation 

target of 1,000

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

We have processed data from over 203,000 trips to 

create a condensed data set designed to look for correlations 

between on-road driving data and variations in fuel cell 

degradation by applying a multivariate regression model, PLS. 

The degradation study is still in the early stages and continues 

to evolve with the addition of new data, understanding of 

multivariate analysis tools, and most importantly, close 

collaboration with the teams. Results from the PLS models 

continue to be complex and difficult to interpret, especially with 

the combined, DOE fleet analysis. Scatter in the model results 

continue to hinder the formation of clear and dominant 

correlations and conclusions, but we continue to look for ways 

to improve the analysis and have plans to look into variations 

such as transients, hybridization impacts, and changes to input 

samples and variables. NREL is striving to construct a 

meaningful multivariate degradation study that provides value 

to the participating teams and looks for significant correlations 

to fuel cell degradation that can be presented to the hydrogen 

and fuel cell research and development community. 

REFERENCES 

[1] Kurtz, J., Wipke, K., Sprik, S., “FCV Learning 

Demonstration: Factors Affecting Fuel Cell Degradation,” Fuel 

Cell Durability and Performance Conference, Miami, Florida, 

November 2007. (Online at 

http://www.nrel.gov/hydrogen/pdfs/42413.pdf) 

 11 
 12 

[2] Wipke, K., Sprik, S., Kurtz, J., Thomas, H., Garbak, J., 

“FCV Learning Demonstration: Project Midpoint Status and 

Fall 2007 Results Presentation,” EVS 23, Anaheim, California, 

September 2007. (Online at 

http://www.nrel.gov/hydrogen/pdfs/42417.pdf) 

[3] Wipke, K., Sprik, S., Kurtz, J., Thomas, H., Garbak, J., 

“Learning Demonstration Progress Report – September 2007,” 

September 2007. (Online at 

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

[4] Current CDPs - Spring 2008 (online at 

http://www.nrel.gov/hydrogen/cdp_topic.html and at 

http://www.nrel.gov) 
F1147-E(10/2008) 

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Fuel Cell Vehicle Learning Demonstration: Study of Factors 

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Conference paper prepared for the FuelCell2008 conference describing the results of the DOE Controlled Hydrogen 

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