NREL is a national laboratory of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

FY14 Milestone:

Simulated Impacts of Life-Like Fast Charging on BEV Batteries

Prepared under NREL Task #VTP2.6407 

NREL FY14 Vehicle Technologies AOP 

WBS: 1.1.2.400

DOE Agreement: #22252

Office of Vehicle Technologies

U.S. Department of Energy

Jeremy Neubauer, Eric Wood, 

Evan Burton, Kandler Smith, and 

Ahmad Pesaran

Transportation and Hydrogen Systems Center

National Renewable Energy Laboratory

September 2014

NREL/MP-5400-63386
2

Motivation & Objective

• Motivation: Deployment of fast charging 

networks could significantly improve the 

utility of BEVs, but the impact of fast charging 

on batteries is not well understood under 

real-world conditions.

• Objective: Assess the impact of realistic fast 

charging scenarios on battery response, 

including thermal and degradation effects.
3

Outline

• Capabilities Added in FY14

o Cell-level evaluation of electrical, thermal, and life models

o Rerouting travel data to include stops at fast charge stations 

• Baseline Simulations

o Vehicle Specs, drive profile and infrastructure availability assumptions

o Compare simulated fast charger utilization with real-world data

• Fast Charge Impact Analysis

o Evaluate impact of fast charging on battery packs under three thermal 

management scenarios

o Consider cell heterogeneity in hot climate subject to fast charging

o A closer look at how fast charging impacts max battery temperatures

• Summary
Capabilities added in FY14

Cell-level evaluation of battery 

electrical, thermal, and life models
5

Multi-cell pack modeling

• Motivation: Availability of fast chargers can result in much longer sequences of 

back-to-back drive-charge-drive events. This is expected to create greater heat 

generation, thermal gradients, and peak temperatures within the pack. Thermal 

gradients can lead to heterogeneous degradation of cells within a pack, and 

thereby lower available capacity and battery lifetime.

• Objective: Add the ability to simulate individual cells within the BLAST-V software 

package, creating a more accurate depiction of peak battery temperatures and 

degradation under aggressive fast charge conditions than would otherwise be 

possible.

• Approach:

o Divide previously “lumped” battery mode into flexible electrical and thermal subsets

o Include ability to randomize initial conditions of SOC, capacity and resistance values

o Simulate temperature, voltage, and SOC for each individual cell

o Calculate degradation for each individual cell

• March 2014 Milestone: Complete addition of multi-cell code
6

BLAST-V

• Battery Lifetime Analysis and Simulation Tool for Vehicles

• Objective: Perform accurate techno-economic assessments of HEV, PHEV, 

and BEV technologies and operational strategies to optimize consumer cost- benefit ratios, petroleum use reductions, and emissions savings
7

Thermal and Electrical Model Separation

• Thermal model can be separately discretized from 

electrical model. This can be beneficial for accelerating 

simulation speed and/or simplifying model validation

• In this example, a 100-node electrical model and 3-node 

thermal model is employed

Three-node BEV 

battery thermal 

response during a 

parking event
8

Example Electrical Response

• Electrical model calculates voltage and SOC 

separately for each individual cell

• The example below shows a closely matched 

pack at beginning of life

Three-node BEV 

battery thermal and 

100 node BEV battery 

electrical response 

during a drive event
9

Example Degradation Response

• Different thermal and 

electrical conditions at 

each cell implies different 

degradation rates.

• Thus, cell level calculation 

of degradation has been 

included as well

Example ten-year 

capacity loss and 

resistance growth 

in a 100-cell BEV 

battery model
10

Example Fast Charge (50kW)

• Simulated pack 

electrical and thermal 

response to FC event at 

beginning of life

o Good charge acceptance behavior 

with taper not occurring until 

~90% SOC

o Significant heat generation with 

thermal model predicting 13°C 

increase over 16-minute charge

Single Cell Model
11

Example Fast Charge (50kW)

• Single and multi-cell models contrasted

o Multi-cell pack power capability limited by weakest cell

o Power tapering occurs slightly earlier in fast charge event

o Predicted max temperature is notably higher

Single Cell Model Multi Cell Model
12

Example Fast Charge

100-cell electrical model

3-node lumped thermal model
Capabilities added in FY14

Rerouting travel data to include stops 

at fast charge stations
14

Tour Planning in BLAST

BLAST estimates BEV utility by making a go/no-go decision before 

each tour (sequence of trips beginning and ending at home)

A reduced-order electrical model of the battery is used to estimate 

SOC at the end of each trip in the proposed tour based on initial 

state of the vehicle, anticipated road and cabin loads, and 

available charging infrastructure

If estimated SOC is determined to dip below a pre-defined driver 

tolerance at any point during the tour, the driver elects not to take 

their BEV on said tour

If estimated SOC stays above tolerance, driver elects to take BEV 

and high-resolution simulation of battery electrical, thermal, and 

life behavior is conducted
15

Tour Planning in BLAST

Group trip data into tours, where 

each tour starts and ends at home

(Assume L2 charging at home)

For each tour, the driver must 

decide whether or not to take BEV 

based on estimated range

Assume drivers require battery 

stay above 15% SOC (or about 15 

miles) to select travel via BEV

Depart / Arrive Miles Minutes

8:31am / 9:07am 21.2 36.3

4:33pm / 4:48pm 9.9 15.6

5:39pm / 6:10pm 13.7 30.9

Example Tour 1
16

Tour Planning in BLAST

BLAST estimates SOC 

through tour using reduced 

order battery model

If minimum estimated SOC 

is above driver’s range 

tolerance, BLAST proceeds 

with simulating the tour, 

otherwise tour is evaluated 

as single parked event

Many tours stay above 

range tolerance without 

work/public charging

Depart / Arrive Miles Minutes Estimated SOC

8:31am / 9:07am 21.2 36.3 100% → 81%

4:33pm / 4:48pm 9.9 15.6 81% → 73%

5:39pm / 6:10pm 13.7 30.9 73% → 61%

charge

discharge

rest

Example Tour 1
17

Tour Planning in BLAST

Consider a different example 

tour from the BLAST travel data

If minimum estimated SOC 

drops below range tolerance, 

BLAST has the option of 

rerouting select trips to include 

stops at fast charge stations

Depart / Arrive Miles Minutes Estimated SOC

8:14am / 8:40am 20.0 26.3 100% → 79%

12:34pm / 1:11pm 35.0 37.0 79% → 42%

3:55pm / 4:36pm 37.3 41.2 42% → 3%

5:49pm / 6:07pm 13.6 19.0 3% → 0%

charge

discharge

rest

Example Tour 2

All fast charge stations assumed 

50kW in this analysis
18

Rerouting for Stops at FC Stations

BLAST considers two data sources 

when rerouting tours

1. Alternate path of travel 

combinations using O/D pairs 

from original travel data and 

Google Maps Directions API

2. User-defined EVSE networks

Using said input data, BLAST 

reschedules the original tour while 

attempting to:

• Keep minimum estimated SOC 

above driver tolerance

• Minimize number of stops and 

time spent at FC stations

*Constraint is applied that all trip 

start times be preserved from 

original travel data

Google Maps

Directions API

BLAST 

Rerouting 

Algorithm

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
19

Rerouting for Stops at FC Stations

*Map does not reflect actual travel data;

trip coordinates have been synthesized for illustrative purposes.

Rerouted path of travel from 

Google API (Example Tour 2)

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
20

Rerouting for Stops at FC Stations

Trip 1

Trip 3 Trip 4

Trip 2

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
21

Rerouting for Stops at FC Stations

Need to identify candidate 

FC stations for current tour 

given predefined EVSE 

network for entire area

Fast Charge Stations 

available in simulation

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
22

Rerouting for Stops at FC Stations

Create one mile buffer 

around path of travel;

retain stations inside buffer 

for further consideration

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
23

Rerouting for Stops at FC Stations

Approximately here 

(northbound on trip 3)

Utilize SOC estimation 

through tour to identify first 

instance of SOC dropping 

below driver tolerance

Walk backwards in time to 

identify closest FC station

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
24

Rerouting for Stops at FC Stations

Using identified station, break 

up trip to include dwell at 

station long enough to reach 

~95% estimated SOC

Re-estimate SOC through tour 

considering dwell at FC station; 

locate additional stations later in 

tour if necessary

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
25

Tour Planning in BLAST

Depart / Arrive Miles Minutes Estimated SOC

8:14am / 8:40am 20.0 26.3 100% → 79%

12:34pm / 1:11pm 35.0 37.0 79% → 42%

3:55pm / 4:03pm 7.8 8.3 42% → 34%

4:20pm / 4:53pm 30.0 32.9 95% → 62%

5:49pm / 6:07pm 13.6 19.0 62% → 49%

Depart / Arrive Miles Minutes Estimated SOC

8:14am / 8:40am 20.0 26.3 100% → 79%

12:34pm / 1:11pm 35.0 37.0 79% → 42%

3:55pm / 4:36pm 37.3 41.2 42% → 3%

5:49pm / 6:07pm 13.6 19.0 3% → 0%

charge

discharge

rest

charge

discharge

rest

Original Tour Rerouted Tour w/ stop at FC station

17 minute FC
26

Tour Planning in BLAST

Depart / Arrive Miles Minutes Estimated SOC

8:14am / 8:40am 20.0 26.3 100% → 79%

12:34pm / 1:11pm 35.0 37.0 79% → 42%

3:55pm / 4:03pm 7.8 8.3 42% → 34%

4:20pm / 4:53pm 30.0 32.9 95% → 62%

5:49pm / 6:07pm 13.6 19.0 62% → 49%

All trips in rerouted tour start on time 

(based on original data)

Depart / Arrive Miles Minutes Estimated SOC

8:14am / 8:40am 20.0 26.3 100% → 79%

12:34pm / 1:11pm 35.0 37.0 79% → 42%

3:55pm / 4:36pm 37.3 41.2 42% → 3%

5:49pm / 6:07pm 13.6 19.0 3% → 0%

Original Tour

All trips in rerouted tour start on time 

(per original data)

Rerouted Tour w/ stop at FC station

17 minute FC
27

Tour Planning in BLAST

Depart / Arrive Miles Minutes Estimated SOC

8:14am / 8:40am 20.0 26.3 100% → 79%

12:34pm / 1:11pm 35.0 37.0 79% → 42%

3:55pm / 4:03pm 7.8 8.3 42% → 34%

4:20pm / 4:53pm 30.0 32.9 95% → 62%

5:49pm / 6:07pm 13.6 19.0 62% → 49%

Depart / Arrive Miles Minutes Estimated SOC

8:14am / 8:40am 20.0 26.3 100% → 79%

12:34pm / 1:11pm 35.0 37.0 79% → 42%

3:55pm / 4:36pm 37.3 41.2 42% → 3%

5:49pm / 6:07pm 13.6 19.0 3% → 0%

Original Tour

Statistics recorded on incremental 

driving time and distance resulting 

from rerouting

BLAST records statistics on 

incremental driving time and distance 

resulting from rerouting and FC stops

Rerouted Tour w/ stop at FC station

17 minute FC
28

Tour Planning in BLAST

Depart / Arrive Miles Minutes Estimated SOC

7:13am / 9:55am 140.1 162.0 100% → 0%

1:23pm / 3:29pm 135.2 125.8 0% → 0%

4:40pm / 5:05pm 13.7 25.6 0% → 0%

Example 3 - Original Tour

Algorithm can also enable very long tours that 

require several stops at fast charge stations

Example of a tour between 

Tacoma, WA and Portland, OR
29

Tour Planning in BLAST

Depart / Arrive Miles Minutes Estimated SOC

7:13am / 7:52am 39.7 39.1 100% → 53%

8:04am / 8:59am 55.9 54.7 95% → 30%

9:17am / 9:49am 33.4 31.8 94% → 55%

10:00am / 10:17am 18.7 17.3 93% → 71%

1:23pm / 1:37pm 14.3 13.4 71% → 54%

1:48pm / 2:40pm 53.9 52.3 93% → 30%

2:58pm / 4:02pm 66.0 63.3 94% → 18%

4:23pm / 4:24pm 1.5 0.6 94% → 92%

4:40pm / 4:59pm 13.7 19.0 92% → 78%

charge

discharge

rest

Rerouted Tour w/ stops at FC stations

While such tours are deemed 

feasible during tour planning, 

BLAST will additionally evaluate 

the thermal and life impacts of 

such an aggressive cycling profile

18 minute FC

11 minute FC

12 minute FC

11 minute FC

18 minute FC

21 minute FC
30

Tour Planning in BLAST

FC Stop 1

6

5

2

3,4

*Map does not reflect actual travel data;

trip coordinates have been synthesized for illustrative purposes.

Path of travel

Utilized FC

Available FC

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
Baseline Simulations

Vehicle Specs, Driving Profiles, & 

Infrastructure Availability Assumptions
32

Vehicle Design & Efficiency

• Employing NREL’s DRIVE cycle for 

real-world efficiency prediction 

– See Neubauer & Wood, Accounting 

for the Variation of Driver 

Aggression in the Simulation of 

Conventional and Advanced 

Vehicles, SAE 2013 World Congress

– Note: does not account for grade.

• Assuming a year 2020 mid-size 

sedan for the vehicle platform

• Apply FASTSim to design specific 

drivetrains

– NOTE: We apply a 300 W aux load 

for EPA based sizing, but remove 

the aux load for DRIVE efficiency 

calculation. Aux loads are then 

added as appropriate within the 

BOM simulations.

Vehicle Model:

9 sec 0-60 mph

75 mile EPA range

22.1 kWh battery

106 kW motor

1576 kg curb weight

220 Wh/mi on DRIVE cycle w/o aux

NREL’s 

DRIVE cycle
33

Real-World Driving Data: Trip Distribution Set A

• 317 vehicle-specific, year-long data 

records were pulled from the Puget 

Sound Regional Councils Traffic Choices 

Study

o Records selected based on availability of 

365 consecutive days of data without 

significant error

• Not all drivers are likely to purchase 

BEVs; total mileage and percentachievable mileage may be good 

indicators of likely BEV drivers

• We chose to analyze drive patterns that 

meet the following criteria:

o 80% of their year-one driving is 

achievable with the BEV without 

infrastructure support

o More than 8,000 miles are achieved with 

the BEV in year one

• With these criteria, 91 of 317 (29%) 

drivers were selected as stand-alone 

BEV drivers (shown in blue).
34

Real-World Driving Data: Trip Distribution Set B

• The previous 80% utility factor 

counts out some high mileage 

drivers that could benefit from 

infrastructure

• We therefore created and added a 

second set of drive patterns that met 

the following criteria:

o Not included in Set A

o More than 8,000 miles per year are 

driven with a CV

• With these criteria, an additional 89 

of 317 (28%) drivers were selected 

(shown in red).
35

Baseline EVSE Scenario

• For analysis of fast charging 

impact on batteries, it was 

necessary to select a 

baseline public 

infrastructure scenario

• The Pacific Northwest has 

fairly good geographic 

coverage of existing FC 

stations already in the 

ground

o 34 existing FC stations in 

Washington State

Existing DCFC Stations (source: NREL 

Alternative Fuels Data Center, Jan 2014)

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
36

Baseline EVSE Scenario

• Incremental utility afforded by 

existing FC stations was compared 

to a number of artificial rollouts

• Found existing infrastructure 

scenario to be sufficient at 

improving BEV utility with a 

relatively small number of stations

Existing DCFC infrastructure used as 

default for remainder of analysis

Google Maps credit: © 2014 Google, Map Data © 2014 Tele Atlas
Baseline Simulations

Compare Simulated Fast Charger 

Utilization to Real-World Data
38

FC Utilization & Validation

• Average driver utilized FC 10 

times in first year of life

o Extreme case driver utilized 

FC at an average rate of 8 

times a month

• FC utilization correlates well 

with incremental VMT

• Some drivers complete 100% 

of travel w/o need for FC
39

FC Utilization & Validation

• BLAST runs reveal average FC 

connection times of 10-22 minutes

o Dependent on arrival SOC

• EV Project data indicated average 

FC connection times of 14-24 

minutes

EV Project Data

BLAST Result
40

FC Utilization & Validation

• BLAST predicts the majority 

of tours enabled by fast 

charging to require 1-2 FC 

events

• One outlying vehicle utilized 

fast charging 12 times on its 

average FC-enabled tour
41

FC Utilization & Validation

• BLAST aggregates charge energy by location

• Group all FC locations together and average driver 

receives 7.6% of energy from fast charging

o Max: 41.5%

o Min 0.0%

• EV Project reports fast charges accounting for 1-21% 

of all charge events for Nissan Leaf’s under study 

that frequently used fast chargers

o Where a cost for fast charging was present, 8% of 

charging energy came from fast charging for Nissan 

Leaf’s under study

EV Project Data

BLAST Result
Fast charge impact analysis

Evaluate impact of fast charging on 

battery packs under three thermal 

management scenarios
43

Simulation Sweep

• Perform 10 years of battery simulations for 180 

driving profiles given...

o EVSE:

– L2 home charging

– L2 home charging + present day FC station availability

o Climate:

– Seattle (coincident with travel data)

– Phoenix (worst case thermal management)

o Battery Thermal Management System:

– Passive cooling

– High-power liquid cooling (active only when driving)

– High-power liquid cooling (active when driving & charging)
44

Results: Average Battery Temp

• In Seattle, neither average FC utilization rates nor increased levels of pack cooling 

capability significantly impacted average battery temperature 

o Exception: Drivers with very high FC utilization saw slightly elevated impact

• However, subject to a Phoenix climate, we begin to see the impacts of aggressive 

battery cooling systems on average battery temperatures

Bar = average across 180 driving profiles; Whiskers = max/min value across 180 driving profiles
45

Results: Battery Life

• Correlating well with average battery temperature trends, battery 

degradation is largely unaffected by fast charging and battery cooling 

systems in the Seattle climate, but is strongly affected by battery cooling 

systems in the Phoenix climate

o 10-year capacity fade reduces from ~28% to ~21% in the presence of active 

cooling during driving and charging

Bar = average across 180 driving profiles; Whiskers = max/min value across 180 driving profiles
46

Real-World Comparison

Years

Odometer 

Avg, mi

Avg FC 

Events

Avg Capacity 

Loss w/o FC

Avg Capacity 

Loss w/ FC

INL Field Test 1.5 50,000 526 24.8% 27.4%

BLAST Simulation 10 97,157 155 27.0% 27.6%

• While experimental and simulated conditions differ in 

terms of duration, mileage, and fast charge utilization, 

the common outcome is that fast charging has a 

relatively minor impact on battery life in both cases

• Compare INL field study with BLAST simulations from 

Phoenix with fast charging and passive battery cooling
47

Results: Battery Max Temp

• Impact of FC was most 

observable in maximum 

pack temperatures from 

passively cooled packs

o Back-to-back sequencing of 

drive-FC-drive produces 

significant heat generation 

resulting in dangerous 

thermal conditions

• Simulated packs with 

aggressive cooling systems 

were able to mitigate heat 

generation on FC tours and 

maintain safe thermal 

conditions

Bar = average across 180 driving profiles

Whiskers = max/min value across 180 driving profiles
Fast charge impact analysis

Consider cell heterogeneity in a hot 

climate subject to fast charging
49

Model Parameters

Vehicle Model:

9 sec 0-60 mph

75 mile EPA range

22.1 kWh battery

106 kW motor

1576 kg curb weight

220 Wh/mi on DRIVE cycle w/o aux

Battery Model:

100S1P configuration

±2% BOL capacity variation

±5% BOL internal resistance variation

±10% life coefficient variation

3 node thermal model

Active cooling BTMS (drive+charge)

• Simulate 10 years life in 

Phoenix subject to as- necessary fast charging

• Consider identical vehicle model from previous section 

with the pack disaggregated into individual cell models 

with distinct characteristics
50

Pack Thermal Gradients

• BLAST simulations resulted in maximum cell-to-cell 

temperature variation of 11°C on average

• Modeled behavior compares favorably with small field 

dataset collected by BMW on the Mini E platform

BLAST Model

BMW Mini E 

Field Data
51

Value of Multi-cell Thermal Model

• Multi-cell modeling predicts peak temperatures 

1.5°C to 4°C warmer than single-cell model
52

Capacity Loss Results

• Average cells lost 19% to 24% of BOL capacity

o Best case vehicle/cell combination lost 17% BOL capacity

o Worst case vehicle/cell combination lost 27% BOL capacity

• Difference in capacity across cells in a pack is typically ~5%

• Capturing minimum cell capacity is important, as this is what will 

limit range and vehicle utility without advanced balancing

Distribution across cells in pack
53

Resistance Growth Results

• Average cells saw 17% to 48% resistance growth

o Best case vehicle/cell combination saw 15% resistance growth

o Worst case vehicle/cell combination saw 54% resistance growth

• Difference in resistance across cells in a pack is typically ~6%

• Capturing maximum cell resistance is important, as this is what will limit 

power (in combination with thermal gradient) without advanced 

balancing

Distribution across cells in pack
Fast charge impact analysis

A closer look at how fast charging 

impacts max battery temperatures
55

Example Vehicle: Full Year Passive Battery Cooling

Pack temperatures climb to dangerous levels 

on long tours with multiple back-to-back 

fast charge events

Thermal history of battery w/ passive cooling and fast charging

Pack temperatures climb to dangerous levels 

on long tours with multiple back-to-back 

fast charge events

On multiple occasions, pack temperatures 

climb to dangerously high levels, and not 

just in the summer.
56

Example Vehicle: Half Day

Zoom in on these temperature peaks and we find 

sequences of multiple back-to-back drive-then-fast-chargethen-drive-again events (4 fast charge events in <12 hours)

Passive Battery Cooling
57

Example Vehicle: Half Day

Large amount of battery internal heat 

generation during fast charge events

Passive Battery Cooling
58

Example Vehicle: Half Day

Heat generation during driving also plays a big role.

These are “fast charge enabled” miles

Passive Battery Cooling
59

Example Vehicle: Half Day

Consider the same profile w/ active 

battery cooling and fast charging

All of the miles are achieved while 

maintaining battery temps <30°C

Active Battery Cooling
Summary
61

Summary

• FY14 New Capabilities

o Demonstrated ability to capture cell level electrical/thermal/life 

effects in comprehensive ownership analysis

o Developed architecture for evaluating impact of public charging 

on vehicle and infrastructure stakeholders while accounting for 

spatial station availability and temporal vehicle demand
62

Summary: Correlation with Real World Data

• Our new-for-FY14 routing logic produces fast charge usage 

statistics – particularly time at stations and energy from 

stations – that agree well with EV Project data

• Our degradation data agrees well with INL’s fast charge 

degradation tests, in that the difference in degradation 

between cases with and without fast charging is small

• BLAST-V simulations predict much higher maximum 

battery temperatures than INL’s BEV fast charging 

experiments, but these experiments have multi-hour rests 

in between fast charging and subsequent driving events
63

Summary: Analysis Results

• Aggressive active battery cooling is important for 

constraining maximum battery temperatures, particularly 

for long fast-charge-enabled tours in hot climates

o Such conditions could pose a safety risk without aggressive 

active cooling, though it is more likely that the BMS will 

intervene to reduce available charging and driving power

• Active battery cooling can also positively impact battery 

lifetime in hot climates when utilized while parked at a 

charger in addition to while driving

• Use of multi-cell thermal and electrical models provide 

additional insight into battery thermal and degradation 

responses
64

Moving Forward

• We’ve completed simulation of multiple cases (multiple 

drivers, climates, vehicle range, etc.) that can be used to 

assess the additional utility of fast charging

• Analysis thereof will be conducted in the remaining 2014 

calendar year to provide additional information on the 

overall benefits of fast charging and our expectations for 

its importance in electrifying the vehicle fleet

• These results and those described herein will be compiled 

into papers and presentation for the 2015 SAE World 

Congress
65

Acknowledgments 

• This activity is funded by the DOE 

Vehicle Technologies Office, Energy 

Storage Technology

• We appreciate the support provided by 

our DOE program managers

o David Howell

o Brian Cunningham

• Special thanks to NREL’s Vehicle 

Systems Analysis team for supporting 

this effort with FASTSim

• Technical questions regarding this work 

should be directed to Jeremy Neubauer 

at 303-275-3084 or

jeremy.neubauer@nrel.gov.