FCV Learning Demonstration: 

Factors Affecting 

Fuel Cell Degradation

Jennifer Kurtz, Keith Wipke, Sam Sprik

ASME Fuel Cell 2008

Denver, CO

June 18, 2008

This presentation does not contain any proprietary or confidential information

NREL/PR-560-43554
2

Fuel Cell Vehicle Learning Demonstration 

Objectives

– Validate H2 FC Vehicles and Infrastructure in Parallel

– Identify Current Status and Evolution of the Technology

• Assess Progress Toward Technology Readiness 

• Provide Feedback to H2 Research and Development

Performance Measure 2009* 2015**

Fuel Cell Stack Durability 2000 hours 5000 hours

Vehicle Range 250+ miles 300+ miles

Hydrogen Cost at Station $3/gge $2-3/gge

Key Targets

1) FCV Learning Demonstration Overview

Photo: NREL

Solar Electrolysis Station, Sacramento, CA
3

Learning Demonstration Partners

1) FCV Learning Demonstration Overview

Gen 1 Gen 1

Gen 1 & 2

Gen 2

Gen 2 Gen 2

Gen 1
4

On-Road Data Received -- Running Totals

54720

230697

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

55000

60000

Sep-04Dec-04

Mar-05

Jun-05

Sep-05Dec-05

Mar-06

Jun-06

Sep-06Dec-06

Mar-07

Jun-07

Sep-07Dec-07

Mar-08

Jun-08

MB of Data

0

50000

100000

150000

200000

250000

300000

# Trips

MB of data

# trips

Vehicle 

Data

FCV Learning Demonstration Data Collection

NREL 

HSDC

Composite 

Data 

Products

Detailed 

Data 

Products

Vehicle 

Data

Vehicle 

Data

Vehicle 

Data

www.nrel.gov/hydrogen/cdp_topic.html

Note: data not specifically controlled for a FC degradation study.

>54 GB of on-road data

>230,000 vehicle trips

3 yrs of data analyzed

2 yrs of data to gather

Through April 2008

1) FCV Learning Demonstration Overview
5

FC Degradation Analysis

• Develop a fuel cell degradation study

• Utilize FCV Learning Demonstration 

real world data (driving and fueling) for 

study and identification of any 

relationships to fuel cell degradation

• Address lack of full scale, fuel cell 

degradation analyses/experiments

• Investigate reasons for differing fuel 

cell decay rates within a fleet

• Collaboration with project partners

• Reporting of any dominant factors 

affecting fuel cell degradation

2) FC Degradation Objectives

Objectives

Data 

Processing

Multivariate Analysis

Single Factor 

Analysis

Data Set

Reports

Interpretations & 

Iterations

Key metric in the FCV 

Learning Demonstration
6

Data Processing

• FC operation trip filters

• Sample (FC) filter

• Gen I available data (may vary 

between project partners)

• Scaled & mean-centered data

• Data through December 2007

• Observation: FC Decay Rate

– Voltage decay estimate 

– Low, average, or high decay rate 

classification

3) Analysis Overview

Objectives

Data 

Processing

Multivariate Analysis

Single Factor 

Analysis

Data Set

Reports

Interpretations & 

Iterations
7

Data Set

3) Analysis Overview

Variable Categories

FC Voltage & Current

Install Date

Starts/hr

Idle Time

Time Between Trips

Trip Length

Ambient Trip Temperature

Speed

Successful FC starts

Fill Data

Location

Data(1,1) Data(1,2) ... Data(1,75)

Data(2,1) Data(2,2) ... Data(2,75)

. . .

. . .

. . .

. . .

Data(31,1) Data(31,2) ... Data(31,75)

Stack1

Stack2

.

.

.

.

Stack31

Sample Decay 

Rate

DR1

DR2

.

.

.

.

DR31

Variables

Objectives

Data 

Processing

Multivariate Analysis

Single Factor 

Analysis

Data Set

Reports

Interpretations & 

Iterations

Select available variables that 

may have a relationship to 

known or expected 

degradation mechanisms
8

FC Stack Voltage Degradation Projection

Technique makes performance 

projection based on all available 

FC data & includes confidence 

intervals.

Decay rate = 

slope of fit line

Note: a 10% decay in operating voltage is 

a DOE benchmark, not an indication of 

fuel cell end-of-life.

3) Analysis Overview

0 500 1000 1500 2000 200

220

240

260

280

300

Operation Hours

Predicted Voltage at 300A

Voltage vs. Operation Hours at 300A: Vehicle19-Stack1 1339 hrs 1434 hrs 1550 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
9

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.

FC Stack Durability

(DOE Milestone)

Increasing real-world operation 

hours. Demonstrated FCV 

operating hours ~ 1200 hours

Average, projected operating 

hour to 10% voltage 

degradation ~1200 hours

3) Analysis Overview
10

FC Degradation Multivariate Analysis

• Why multivariate analysis?

– Dominant, single factors were not 

apparent in Single Factor analysis

– Large data set

– Data not collected in a controlled 

manor for a degradation study

– Interrelated &/or redundant data 

variables (reduction of factors)

– Likely a combination of factors

• Why Partial Least Squares (PLS)?

– Linear regression model focused on 

FC decay rate

– Model designed to explain maximum 

variance in decay rate

3) Analysis Overview

Objectives

Data 

Processing

Multivariate Analysis

Single Factor 

Analysis

Data Set

Reports

Interpretations & 

Iterations
11

Objectives

Data 

Processing

Multivariate Analysis

Single Factor 

Analysis

Data Set

Reports

Interpretations & 

Iterations

Correlate Interface

3) Analysis Overview

Efficiently process large amounts 

of data & many analysis iterations

Interface to perform 

analysis and view results
12

What are the Correlations?

BiPlot Example

Note: the data depicted here helps illustrate the process for the Learning Demonstration (LD) analyses. Ultimately, the goal is to identify factors of 

decay rate and what the affect is (positive or negative). In order to do this, tendencies within the low, average, and high decay rate classification 

need to apparent. The actual data is more scattered than the example shown here, thus making it more difficult to identify patterns, especially in 

the LD fleet analysis.

-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

0.2

0.3

0.4

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

4) Results

Outlier

LV1

Possible Sample 

Groups

LV1 Factors

(orange circles)

Goal: find tendencies within 

the decay rate groups that 

translate to decay rate factors 

and the factors’ affects

Latent Variables:

Combination of input 

factors that describe 

decay rate variance 

LV2
13

What Factors are Important to the Model? 

Regression Vector Example

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

4) Results

High coefficient value indicates 

a factor’s importance in the 

overall model

Low coefficient does not 

necessarily imply a lack of value 

added from a factor

The factor’s coefficient sign (+ / -) 

indicates the directional 

relationship to decay rate
14

-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

How Good is the Model? 

Predicted vs. Measured Example

4) Results

Sample decay rate prediction & 

trends between decay rate classes

Multiple linear regression model:

ypred=x*a

x is sample data

a is regression vector

Example

Ultimately, model could be used to 

evaluate FC operation changes;

still in infant stage and not ready for 

that application.
15

Multivariate Analysis Results

4) Results

Objectives

Data 

Processing

Multivariate Analysis

Single Factor 

Analysis

Data Set

Reports

Interpretations & 

Iterations

• Public reporting through biannual composite data 

products and conferences

• Detailed reporting with project 

partners

• Collaboration with project 

partners is key
16

PLS Results - Learning Demonstration 

Degradation Factor Summary

4) Results

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

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.

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

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.
17

PLS Results – Identification of Factors 

Contributing to FC Degradation per Team

4) Results

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
18

Summary

• FCV on-road data (92 vehicles)

• Different look than a lab study of degradation

• Analysis Learning

– Decay rate classifications

– Analysis iterations & adjustments to input factors & included 

samples

– Additional data

• Complex factor interactions affecting FC degradation

• Team level analysis vs. DOE Fleet level analysis

– Team level analysis more valuable because of the variations 

between teams

– Team level analyses have high R2 values, but are not robust

– Identification of trends difficult because of scattered sample data

– Use DOE Fleet level analysis to compare difference between teams

• Collaboration with teams

5) Summary
19

Contact Information

Jennifer Kurtz

National Renewable Energy Laboratory

jennifer_kurtz@nrel.gov

303-275-4061

Keith Wipke (Primary project contact)

National Renewable Energy Laboratory

keith_wipke@nrel.gov

303-275-4451

All public Learning Demo papers and presentations are available online at

http://www.nrel.gov/hydrogen/proj_tech_validation.html
20

Single Factor GUI

Go Back

Backup
21

NREL Web Page Provides Direct Access 

to All Composite Data Products

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

Dyno Range (2) Window-Sticker Range (3) On-Road Range (4)(5) 0

50

100

150

200

250

300

Vehicle Range (miles)

Vehicle Range1

2015 Target

2009 Target

Created: Feb-27-07 4:49 PM

(1) Range is based on fuel economy and usable hydrogen on-board the vehicle. One data point for each make/model.

(2) Fuel economy from unadjusted combined City/Hwy per DRAFT SAE J2572.

(3) Fuel economy from EPA Adjusted combined City/Hwy (0.78 x Hwy, 0.9 x City).

(4) Excludes trips < 1 mile. One data point for on-road fleet average of each make/model.

(5) Fuel economy calculated from on-road fuel cell stack current or mass flow readings.

Backup
22

Equation Example

x=sample data, a vector that is 1 by factor #:

e.g. [50 300 .5 ....... .7 .2 1]

a = regression vector, a vector that is factor # by 1:

e.g. [.4 .1 -.3 ...... .1 -.1 .1]’

The model equation is: 

ypred=x*a+b,

where a is the regression vector, x is a sample’s data vector, 

ypred is the predicted decay rate, and b is the intercept (b=0 for 

this model). 

Because of the data processing (mean-centering and scaling) 

in the model, the x & ypred value is processed and ypred is 

reverted back into decay rate units for the prediction. 

Backup
23

Simulated Data Set Snapshot

Backup

Scaled & mean-centered Simulated Data