Technical Report

NREL/TP-550-46345

August 2009

Field Testing Plug-in Hybrid 

Electric Vehicles with Charge 

Control Technology in the Xcel 

Energy Territory

T. Markel, K. Bennion and W. Kramer

National Renewable Energy Laboratory

J. Bryan and J. Giedd

Xcel Energy
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

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

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-550-46345

August 2009

Field Testing Plug-in Hybrid 

Electric Vehicles with Charge 

Control Technology in the Xcel 

Energy Territory

T. Markel, K. Bennion and W. Kramer

National Renewable Energy Laboratory

J. Bryan and J. Giedd

Xcel Energy

Prepared under Task No. WR641000
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iii 

Abstract 

In 2007, NREL and Xcel Energy collaborated on an analysis study to better understand the fuel 

displacement potential, the costs, and the emissions impacts of market introduction of plug-in 

hybrid electric vehicles (PHEVs) into the Xcel Energy Colorado Service Territory. The study 

indicated the potential for petroleum displacement and highlighted the value of vehicle charge 

management to limit costs and emissions impacts. As an extension and demonstration of the 

analysis results, a real-world fleet demonstration project was initiated. The purpose of the project 

was to 1) quantify the real-world fuel savings benefits of PHEVs, 2) gain an understanding of 

potential utility loading scenarios due to the introduction of PHEVs, and 3) develop and 

demonstrate the equipment and tools necessary for utility controlled charging. The project team 

successfully evaluated the performance of three vehicles placed in consumer service with charge 

control functions directed by Xcel Energy. Ten different charge control scenarios were explored, 

several of which correlated to the previous analysis study of 2007. Analysis of 16 weeks of field 

study data supports the conclusions drawn in the previous study. PHEV technology holds great 

promise for enabling petroleum reduction in transportation. Utility controlled charge 

management was demonstrated and potential load profiles were generated; some of which were 

synchronized with renewable resources and ancillary services suggesting that utility load 

management of PHEVs could provide both low CO2

Table of Contents

 transportation fuel pathways and grid 

operational flexibility for utilities. 

1 Introduction .......................................................................................................................................... 1

2 Approach............................................................................................................................................... 3

2.1 Driver selection and attributes ..................................................................................................... 3

2.2 Vehicle selection and attributes ................................................................................................... 3

2.3 Charging infrastructure................................................................................................................. 4

2.4 Data collection and operational control tools.............................................................................. 4

2.5 Operational scenarios and timeline.............................................................................................. 5

2.6 Differences between the field test and the analysis .................................................................... 6

3 Results................................................................................................................................................... 6

4 Future Work ........................................................................................................................................ 14

5 Conclusions......................................................................................................................................... 15

6 Acknowledgements............................................................................................................................. 16

7 References and Bibliography .............................................................................................................. 16
1 

1 Introduction

Electrification of transportation is critical for future sustainable mobility. The world’s 

dependence on petroleum is causing concern over future resource availability and the 

environmental impacts of continued fossil-fuel consumption. Plug-in hybrid electric vehicles 

(PHEVs), electric vehicles, and fuel cell vehicles provide opportunities for shifting transportation 

energy demand to the electricity network and its diverse set of resources.

PHEVs offer the opportunity to displace significant amounts of petroleum with electricity using 

moderately sized battery systems (relative to electric vehicles) and a reserve gasoline engine for 

sustained efficient operation. The costs of batteries for PHEVs remain high and alternative value 

streams are needed to open the door to market expansion. PHEVs can also present a challenging 

electrical load for the utility grid. Planning horizons for utilities require estimating load scenarios 

and opportunities for operational improvements many years into the future. 

Hybrid Electric Vehicles (HEVs) have the potential to reduce petroleum consumption between 

20-35%. PHEVs can reduce petroleum consumption between 35-70% where fuel consumption is 

more affected by the consumer charging and driving pattern. The CO2 emissions reduction for 

PHEVs is less than the petroleum consumption reduction because emissions from grid power 

sources must be considered. The level of CO2

In a previous collaborative study between NREL and Xcel Energy, travel survey simulation 

results were used to estimate several future PHEV load scenarios and their potential impact on 

the Xcel Energy Colorado service territory [7]. The vehicle simulations represented 227 unique 

driving profiles covering 24 hours of operation assuming all vehicles were PHEVs with 20 miles 

of urban electric driving capability. The simulation results lead to the generation of several 

recharge scenarios that were used to understand the costs and emissions impacts of meeting the 

associated new loads at several market penetration levels introduced into the Xcel Energy 

Colorado service territory. 

 impact is highly dependent on the electricity 

source, which varies regionally. The EPRI NRDC study [1] shows the variability for PHEVs 

based on the source of electricity is the same as for HEVs (~30%) when from coal, ~50% when 

from natural gas, and 66% if tied to renewables. Other studies present similar data [2-6]. 

The four operating scenarios considered in the simulation study were: 1) No utility control, 2) 

delayed charging, 3) valley fill charge, and 4) opportunity charging. The resulting load profiles 

expressed as kW/100 vehicles as a function of time of day are shown in Figure 1. The first three 

cases include a single recharge after driving is complete for the day and the fourth scenario 

allows recharging between trips throughout the day. These scenarios lead to petroleum 

consumption reductions relative to a comparable hybrid electric vehicle (HEV) of 25% for single 

daily charge and 43% for multiple daily charge scenarios. The impacts on the utility system were 

significantly different depending on the scenarios. The study assumed 500,000 vehicles or ~30% 

of the fleet were PHEVs and highlighted that the timing of charging has important impacts on the 

utility operational costs and emissions. The negative impacts of coincident loading to the utility 

peak were encountered during 17 hours under the opportunity charge scenario and 6 hours under 

the no utility control scenario, suggesting that only a small amount of utility involvement would 

be necessary to avoid capacity expansion while offering consumers great flexibility throughout 

the year. The emissions impact assessment highlighted the shift in the ratio of energy resources 
2 

serving PHEVs, which for Xcel is mainly coal and natural gas. Regardless of the source or 

charge scenario, the data suggest a net 15-20% reduction in CO2 emissions for PHEVs over 

HEVs. 

Figure 1: Generated Load Profiles for Four Charge Scenarios of a Fleet of PHEV20 Vehicles

The conclusions from the analysis study justified a real-world field test of the state of PHEV

technology in the Xcel Energy service territories. The field test of PHEV technology was 

intended to explore the in-use experience of PHEV fuel saving benefits. Potential impacts and 

benefits to utility operations were also of interest. Key questions of interest included: 

• Are in-use PHEV load profiles similar to the simulated ones? 

• Can utilities successfully communicate with and control vehicle charging? 

• Do various charge scenarios affect vehicle fuel displacement potential significantly? 

• Do various charge control scenarios significantly affect the capacity of a vehicle to support 

ancillary services?

• Will consumers plug in their vehicles consistently? 

Vehicle to grid or the ability to both charge and discharge the battery of the vehicle while tied to 

the utility grid was also an intended test scenario. However, technology development hurdles 

limited the study to consider charge control scenarios only. Vehicle to grid has been suggested to 

provide a valuable service to utilities that may offset the high incremental cost of PHEVs and aid 

in market expansion. Charge control capabilities as studied here can provide similar benefits with 

reduced capacity per vehicle.

Vehicle to grid also presents a new challenge in that the vehicle becomes a mobile distributed

resource. As such, the interconnection standards as defined in IEEE 1547 should apply [8]. 

Vehicles satisfying these interconnection standards would offer utilities mobile resources with

0

20

40

60

80

100

120

140

0 2 4 6 8 10 12 14 16 18 20 22 24

Power Demand (kW/100 vehicles)

Time of Day (hr)

Delayed Charge

Valley Fill

Opportunity 

No Utility Control
3 

safety and response expectations similar to those of existing distributed energy resources. 

Distributed energy resources, including both generation and energy storage, are critical elements 

of the evolving smart, green, clean, and efficient utility grid.

2 Approach

The goal of the current effort was to evaluate, under real-world conditions, the performance 

attributes of PHEVs with charge control technology in the Xcel Energy service territories. This 

was accomplished through collaboration between Xcel Energy, Hybrids Plus, V2Green, and 

NREL. The project required the selection of drivers, vehicles, and control hardware, data logging 

equipment, test scenarios, and analysis. The overarching intention was to expand our knowledge 

of PHEV benefits, consumer usage, and utility opportunities and challenges. 

2.1 Driver selection and attributes

Test drivers were selected from current Xcel Energy employees who had electrical service with 

Xcel Energy and who completed a survey describing both their driving profile and their interest 

in the project. Three employees were selected whose travel behavior was similar to national 

average daily distances and who traveled to and from work during normal “rush” hours. Two 

employees were located in Denver and one was located in Minneapolis/St Paul with each driver 

experiencing slightly different travel conditions during their commute to and from work. 

One requirement of the drivers was that both their work location and home location be located 

within the 3G data network. Electrical outlets were provided in both home and work locations 

and the drivers were encouraged to plug the PHEVs in every time they were at work or home for 

more than a few minutes and were not informed as to when the vehicle would be charging. The 

test drivers were not limited to driving the PHEVs to and from work, but rather were encouraged 

to drive the PHEVs as their everyday vehicle in order to replicate a real life situation. 

2.2 Vehicle selection and attributes

Ford Escape HEVs were chosen for the study. Xcel Energy had experience with Ford Escapes in 

the corporate fleet. Both 2006 and 

2008 model year vehicles were used 

for the study. Figure 2 is an image of 

one of the test vehicles. The hybrid 

powertrain was substantially the same 

for these two model years. The base 

hybrid electric vehicle was converted 

to be a plug-in hybrid electric vehicle 

by Hybrids Plus. The original 1.8kWh 

NiMH battery pack of the hybrid 

powertrain was removed and replaced 

with a 12kWh Li-ion battery pack. The 

plug-in hybrid conversion included a 

controllable battery charger and a grid 

connection terminal. A data logging 

and communications package from 

Figure 2: Image of Xcel Energy Ford Escape PHEV V2Green (now GridPoint) was also 
4 

installed. Communications with the vehicle were established via the cellular network whenever 

the vehicle was driven or parked and charging. The Ford Escape hybrid powertrain coupled with 

the high energy battery pack allowed battery-only operation up to 29 MPH under light loads after 

an engine warm-up cycle while the battery had sufficient energy. Under higher speeds and loads, 

the vehicle would bias toward using the stored energy but also used gasoline energy to propel the 

vehicle. This operation regime is a low power blended plug-in hybrid implementation and 

provides approximately 50 miles of charge-depleting operation between battery recharge events. 

In charge-sustaining mode (beyond 50 miles) the vehicle operates at ~28mpg while in chargedepleting mode the fuel economy was expected to be ~55mpg. Charge and discharge capability 

at 6kW with a 220V AC connection was targeted, however, the vehicles for this project were 

limited to 1.3kW using a standard 110V 15A outlet. 

2.3 Charging infrastructure

Availability of charging infrastructure is critical for achieving the benefits of PHEVs. Vehicles 

are typically parked more than 90% of a day. This parked time is often at home or at work. In 

this study charging infrastructure was installed both at the driver’s home and at their place of 

work. Analysis of NHTS data by Tate, et al. suggests that 90-95% of the vehicle fleet is parked 

in one of these two locations. [6] Given that the vehicles were able to use 110V standard outlets, 

the participants also had the opportunity to use outlets beyond those of home and work. 

2.4 Data collection and operational control tools

The collection of data during this project was a necessary element to support analysis and results 

summary. We used remote access data logging hardware provided by V2Green for this study. 

The data logger included a power flow meter, a CAN bus interface, GPS unit, and a 

communications system. Data parameters collected are listed in Table 1. Data were uploaded to a 

server whenever communication was established.

Table 1: Data Collection Parameters

Data during key on, (1 sample per second) Data during key off, (1 sample per minute)

Time

Lifetime Fuel Consumption 

Fuel flow rate (g/s) 

Lifetime Odometer (km)

Vehicle speed (km/hr)

Battery DC current (amps)

Battery DC voltage (volts)

Ambient Temp (C)

Battery min/max temps (C)

Battery SOC (%)

Latitude (decimal deg)

Longitude (decimal deg)

Elevation (m)

Engine Temp (C)

Motor torque (Nm)

Motor speed (rpm)

Generator torque (Nm)

Generator speed (rpm)

Engine speed (rpm)

Battery Charge limit (kW or amps)

Battery Discharge limit (kW or amps)

Engine coolant Temp (C)

Brake pedal (%)

Accel. pedal (%)

Time 

AC current (amps)

AC voltage (volts)

Battery DC current (amps)

Battery DC voltage (volts)

Ambient Temp (C)

Battery min/max temps (C)

Battery SOC (%)

Battery Energy Available Discharge (kWh)

Battery Energy Available Charge (kWh)

Latitude (decimal deg)

Longitude (decimal deg)

Elevation (m)

Lifetime AC In (kWh)

Lifetime AC Out (kWh)

Plug Connection Engaged
5

2.5 Operational scenarios and timeline 

We explored four operational scenarios in the systems analysis study completed in 2007. These 

four scenarios were implemented as well as could be in the field environment. These four 

scenarios are: 

 Opportunity –any time vehicle is plugged in it is allowed to charge regardless of where it is 

located (no charge control, similar to opportunity scenario from 2007 study) 

 Single location –charging is allowed only at the designated home location (similar to no 

utility control) 

 Single location with delayed charge (similar to delayed charge control) 

o Only allowed to charge between 10pm and 8am 

 Single location with charging centered on utility load low point (similar to valley fill control) 

o 3:30am was assumed to be a typical lowest load point based on historical data 

o Charge start time calculated to charge 1⁄2 the total energy needed by 3:30am 

o Charging stopped by 8am. 

Four additional scenarios were also introduced that would provide insights into additional value 

of PHEVs with charge control to be synchronized with renewables and other grid attributes. 

 Solar charge (charging centered around the peak solar generation) 

o Solar peak assumed to be at noon 

o Charging does not start before 8am and ends by 4pm 

o Each vehicle charging starts such that 1⁄2 of the energy needed is charged by noon 

 Regulation signal (MW and Automatic Generation Control – AGC signal) 

o Regulation power demand scaled to capabilities of vehicle test fleet such that less than 

5% of the events fall outside of the fleet capabilities 

o The number of vehicles charging and thus the aggregate power load follows the need for 

regulation MW 

o For AGC case, all vehicles charge when demand for AGC is low – directional control 

without magnitude control 

 Wind farm balancing (MW and AGC signal) 

o MW output of wind farm was scaled to the capabilities of the vehicle fleet such that less 

than 5% of the events fall outside of the fleet capabilities 

o Represents a vehicle fleet fueled entirely by wind output 

o AGC signal leads to directional control of vehicles without magnitude control 

o All vehicles charge if AGC demand is reduced 

 Real-time Price Point 

o Historical real-time price data were used to determine charge control signal 

o Allow vehicle charging anytime the real-time price is less than $70/MWh 

We collected 16 weeks of data using these scenarios. Several of the scenarios were repeated 

during the project to see changes in behavior and check consistency of results. 
6 

2.6 Differences between the field test and the analysis

There are significant differences between the analysis work completed in 2007 and the field trials 

conducted in 2008. The following should be noted: 

• Charging rate – the assumed recharge rate in the 2007 simulation study was 1.4kW while the 

observed vehicle performance was typically just under 1kW. The difference impacts the 

speed at which a vehicle can be recharged and prepared for the next trip. 

• Fleet size – the 2007 simulation study was based on 227 single day unique driving profiles 

and expanded to represent a 30% fleet penetration level. The field test resulted in three

vehicles that were consistently used for consumer operation. Each scenario was run for at 

least one week and some for several weeks. As a result at least 18 unique data sets for each 

operating scenario were collected. The small sample size of the field test limits our ability to 

extend conclusions to a population. 

• Infrastructure and consumer behavior – in the 2007 simulation study under opportunity 

charging, researchers assumed the consumers were perfect in always recharging the vehicle 

whenever parked and that charge infrastructure was ubiquitous while in the field study 

infrastructure was ensured at home and work only and consumer behavior impacted actually 

plugged-in time. This is likely to reduce the relative fuel savings potential observed in the 

field study relative to the 2007 simulation study. 

• Vehicle architecture – The 2007 simulation study assumed all vehicles were mid-size car 

platform PHEVs with 20 miles of urban (moderate power <~55 mph) electric-only operation 

while the vehicles used in the field trials were sport utility vehicles with ~50 miles of chargedepleting range. EV-only operation in the field test vehicles was mainly below ~15 mph. The 

field test vehicle energy capacity and vehicle performance would correlate to ~25 miles of 

urban EV range capability if the powertrain was capable of operating in that manner. The 

consumption and displacement magnitudes would likely be greater for the SUV. The 

extended range of the converted Escape PHEV results in the opportunity charging scenario 

having less impact as the consumer almost always has available energy in the battery given 

typical commuting distances of 30 miles per day. A larger battery also suggests that recharge 

durations may be extended relative to the vehicles in the 2007 simulation study. 

Regardless of differences between the studies, the field trials offered an opportunity to explore 

the state of technology, implementation options, consumer impacts, and future utility values. 

3 Results 

Six vehicles entered the field trial study. When testing began it was possible to collect data from 

four vehicles. Vehicle number 5 alone was used as a fleet vehicle. The analysis results focus on 

the characteristics of vehicles 2, 3, and 4 as they were used by employees and encountered fairly 

consistent usage profiles. The vehicles and their roles are summarized in Table 2. 

The testing matrix for this field trial study was extensive. One key goal was to provide some 

comparison to the previous analysis simulation study completed in 2007. To do so, four charge 

control scenarios that were similar to the previous work were implemented early in the study 

period. Additional scenarios of interest were added to provide further information on fuel 

consumption impacts and potential availability of vehicles to provide utility ancillary service 
7 

functions. Ten different operating scenarios were tested over a 16-week period as described in 

Table 3. 

Table 2: Xcel Energy PHEV Field Trials Vehicle Locations and Roles

Vehicle 

Number

Location Description 

1 Denver Development Vehicle

2 Minneapolis Consumer

3 Denver Consumer

4 Denver Consumer

5 Denver Xcel Fleet Vehicle

6 Denver Data logging issues

Table 3: Description of Study Charge Control Scenarios and Timelines

Study 

Week

Start 

Date

End 

Date

Description Comments

1 7/17 7/23 Opportunity Charge Neglected in results – assumed to be a 

practice period

2 7/24 7/30 Opportunity Charge Baseline 

3 7/31 8/6 Opportunity Charge Baseline 

4 8/7 8/13 Single Location Charge Charging only at Home. 

Similar to “No Utility Control.”

5 8/14 8/20 Single Location Delayed Charge Starts at 10pm and ends by 8am.

Similar to “Delayed Charge.”

6 8/21 8/23 Single Location Valley Fill (short 

week) 

Load centered at 3:30am.

Similar to “Valley Fill”

Short week due to demos.

7 8/28 9/3 Testing Postponed Demo at National Convention

8 9/4 9/10 Testing Postponed Demo at National Convention

9 9/11 9/17 Single Location Valley Fill Repeat of week 6

10 9/18 9/24 Charge Centered on Solar Peak Charging centered at noon with no 

charge before 8am or after 4pm.

Note: No communication with vehicles 

for part of week, default to Opportunity

11 9/25 10/1 Charge Synch with Wind (AGC) Decrease in AGC of Wind causes all 

vehicles to charge

12 10/2 10/8 Regulation Signal (MW) Number of vehicles requested to 

charge was scaled with regulation 

demand magnitude.

13 10/9 10/15 Real-time Price Point Historical real-time price timeline 

controlled charge status.

14 10/16 10/22 Opportunity Charge Baseline – repeat

15 10/23 10/29 Regulation Signal Balance (AGC) Decrease in AGC of regulation causes all 

vehicles to charge

16 10/30 11/06 Charge Wind Balancing (MW) Number of vehicles requested to 

charge was scaled with Wind output 

magnitude.
8 

The fuel consumption benefits of PHEVs were estimated in the simulation study to be 25-43% 

less than those of a comparable hybrid vehicle. In the field trials the comparable HEV is rated at 

~28mpg or 8.4L/100km. Figure 3 highlights the petroleum consumption (top) and electric 

consumption (bottom) rates of the each of the study vehicles across the testing period on a 

weekly basis. The fuel consumption of the vehicles in the field trial ranged between 4 and 6 

L/100km resulting in a reduction of 29% to 52%. Data for weeks 6-8 are not included as these 

were demonstration weeks and data for vehicles 1, 5, and 6 are not included as they did not 

provide useful data consistently throughout the project as highlighted in Table 2. The electric 

consumption rates shown in the bottom section of Figure 3 were somewhat lower than expected 

and also highly variable on a week-to-week basis. The electricity consumption would be 

significantly affected by the total daily distance driven; driving farther than the charge depleting 

range (~50 miles) between charge events would dilute the electricity consumption data. The 

electric consumption rate could also be influenced by the number of daily cold engine starts. The 

PHEV implementation in the field trials starts the engine every time the vehicle is turned on to 

force engine and catalyst warm-up as an emissions reduction strategy. However, many short trips 

would then lead to a greater portion of driving on engine power rather than electric power. The 

electric consumption rate could also be affected by how often the drivers plugged in the vehicle 

to maintain the battery state of charge at a high level. In most weeks, there is a correlation 

between increased electrical consumption rate and decreased gasoline consumption rate resulting 

from the fuel displacement goals of the PHEV technology. 

Figure 3: Weekly Petroleum and Electricity Consumption Comparison 

Escape HEV

28mpg (8.4L/100km)
9 

Figure 4 shows a summary of the fuel displacement incurred during the project for the vehicles 

monitored. The two weeks in the middle, with no displacement, were demonstration weeks and 

ignored in the analysis. Overall, more than 90 gallons of fuel were displaced. Extrapolating this 

to annual savings relative to the baseline HEV results in savings of about 120 gallons per year 

per vehicle or a savings of ~$300 assuming a gasoline fuel price of $2.50 per gallon. All vehicles 

in this study were converted from HEV to PHEV without the support of the vehicle 

manufacturer. It is expected that when manufacturers create production-ready PHEV 

implementations that the savings could be much greater with modified engine controls and 

systems optimization.

Figure 4: Project Cumulative Fuel Savings

Besides fuel consumption benefits and electricity consumption characteristics, an important 

purpose of this study was to understand the consumer ability and willingness to plug-in the 

vehicle. The more often a vehicle is plugged in, the greater the opportunity for fuel displacement. 

Additionally, the more often it is plugged in, the more opportunity for the utility to use the 

energy storage as a controllable demand-side load or even as a generation source in the future. 

The total time that the vehicles were parked and connected to the utility grid was measured and 

is reported in Figure 5. The top chart is the percentage of hours connected and the bottom chart 

shows the total hours. The data suggest that the behavior of the consumer changed over the 

duration of the project. At the beginning of the study the percentage of time plugged in was as 

low as 20% and a trend of increasing time plugged in is observed. The time plugged-in stabilized 

around 60% near the middle of the study. It is not clear what happened during weeks 13 and 16 

that caused a variation in the amount of time plugged in. It should be noted that a real-time 

display of estimated miles per gallon was provided to the users throughout the field trial. 
10

Figure 5: Percent of Time Test Vehicles are in Key-off and Connected State

Figure 6: Operational State Comparison of Vehicles and Study Periods 

Figure 6 summarizes the operational state data by vehicle for three, four-week periods during the 

study. Four states are identified: 1) Key On – vehicle is being driven, 2) Key Off Connected – 

the vehicle is parked and plugged in, 3) Key Off Disconnected – the vehicle is parked but not 

plugged in, and 4) Unknown – the state could not be determined, maybe due to communications 

limitations. The Key On data support the expectation that driving constitutes a very small 

fraction of a vehicle’s life. The connected state constitutes about 40-60% of the time and the 

disconnected state was 25-40% of the time. The time the vehicle was in the disconnected state 

could be reduced with more prevelant infrastructure and driver education or utility incentives. 
11

Increasing the total connected time could justify the use of vehicles as a demand side 

controllable load or even a reserve capacity if the vehicles were to have vehicle-to-grid power 

flow potential. 

Figure 7: Plugged-in Duration by Time of Day Segment

Figure 7 slices the plugged in hours in a different way by looking at them in terms of four, sixhour time slots throughout a day. The top graph shows the results for a weekday and the bottom 

graph is a display of the weekend characteristics. During the weekdays, surprisingly, the time 

slot that seems to have the least number of plug-in hours was 6pm to midnight, suggesting that 

the vehicles may have been used more often, parked in areas without available outlets, or that the 

drivers chose to wait to plug in their vehicles. The weekend results were significantly different in 

that the mid day hours 6am to noon and noon to 6pm had very limited hours of connectivity. The 

main difference here is most likely a result of having work place charging available during the 

week and limited infrastructure availability in the places where the drivers traveled on the 

weekends. If there was a desire for utilities to use these resources as demand-side load 

management programs or for ancillary services there is likely a need to explore public 

infrastructure opportunities such that vehicle connectivity can be ensured during all time slots 

and locations. 

Regarding ancillary service potential, the capacity of the vehicle to provide charge or discharge 

power was assessed. Figure 8 provides one week of measured field data for three of the test 
12

weeks. The top chart is the aggregate discharge energy potential and the bottom chart is the 

aggregate charge energy potential. Each vehicle connected could provide approximately 10kWh 

of energy for charge or discharge depending on its current state of charge. A fully discharged 

battery in one vehicle when connected to the grid would represent 10kWh of charge potential 

and 0kWh of discharge potential. Vehicles not connected do not contribute. Charge potential 

represents an opportunity to increase grid load (i.e. to balance an increase in wind generation) 

and a discharge potential represents an opportunity to provide energy to the grid (i.e. peaking 

times or a decrease in wind generation). Given the charge control scenarios shown here and the 

fact that vehicle-to-grid was not enabled on these vehicles, they show a significant potential for 

discharge and limited potential for charge since the battery packs were often fully charged. 

Opportunity charge scenarios (not shown) showed even less opportunity for charge energy 

potential. The data suggest that it might be advantageous for a utility to consider V2G 

technologies that utilized alternative charge management strategies that provided fairly equal 

amounts of charge and discharge potential throughout the day (i.e. maintaining a mid-level SOC 

in the batteries) while still satisfying the driver’s need for electrical energy to displace petroleum.

Figure 8: Charge and Discharge Aggregate Capacities of the Test Fleet during Weeks 5, 9, and 10

Finally, aggregate load profiles for several of the test scenarios were developed to compare with 

the load profiles derived from the 2007 simulation study. The charge management scenario can 

dramatically impact what the bulk loading characteristics of PHEVs will be on the utility grid. 

Single Location Delayed Charge (blue)

Single Location Valley Fill (green)

Charge Centered on Solar Peak (red)
13

In Figure 9, the top chart can be directly compared to Figure 1 showing the four charge control 

strategies evaluated in the 2007 simulation study. In the top graph of Figure 9 the solid blue line 

represents Opportunity Charging and is comparable to the green line Opportunity Charge case in 

Figure 1. Likewise, the dashed green line in Figure 9 is Single Location Charge and corresponds 

to No Utility Control in Figure 1. The dotted red line in Figure 9 is Single Location Delayed and 

corresponds to Delayed Charge case in Figure 1. Lastly, the dash-dot light blue line in Figure 9 is 

Single Location Valley Fill and corresponds to the Valley Fill scenario in Figure 1. Both Figure 

1 and 9 present data as kW/100 vehicles and the magnitudes are somewhat different. The peaks

in Figure 9 are generally lower and broader. This is most likely due to the differences in the 

charge rate of <1kW in field test and 1.4kW in the study and the battery size ~10 kWh in field 

test and ~5-6kWh for the 2007 study. All four field test scenarios are surprisingly similar to 

those from the 2007 simulation study. 

Figure 9: Aggregate Load Profile Based on Real-World Field Test Charge Scenario Data

In addition to the four scenarios from the previous study, many other scenarios linking vehicle 

charging to renewables, prices, and ancillary service needs were tested. The bottom half of 

Figure 9 describes the aggregate load of several of these additional scenarios. In the bottom 

graph, the solid blue line is week 10 with charging centered on the solar peak at noon, the dashed 

green line is charging based on a wind farm-based AGC signal, the red dotted line is the result of 

charging synchronized with the magnitude of the system regulation needs, and the light blue 

dash-dot line is charging tied to the real-time price of electricity. Both of the regulation signal 
14

scenarios show significant variability on short time scales but also seem to provide a moderate 

level load throughout the time period. The centered on solar peak has a clear mid-day peak. The 

scenario synched with real-time price is also valuable as it forces most of the charging to occur 

during evening off peak hours and resulted in a fairly smooth moderate level load. 

Avoiding incremental loads coincident with the demand peak will be critical in controlling 

energy costs and the need for capacity expansion as the PHEV market expands. Likewise, charge 

management scenarios were also shown to affect the marginal emissions characteristics of the 

energy needed for PHEVs in the 2007 study. In the 2007 study, CO2 emissions reductions were 

estimated to be 15-20% relative to an HEV, independent of the charge management scenario. 

EPRI and NRDC published a study [1] also suggesting that for vehicles fueled solely with 

natural gas resources, (which is the majority of the fuel on the margin in Xcel Colorado territory) 

reductions of 22% would be likely. They also showed that the lowest CO2 cases were those 

linked with renewables showing a 48% reduction compared to a hybrid vehicle. The field study 

included two scenarios in which charging was directly synchronized with renewable generation. 

In such scenarios one would expect CO2

The analysis of the work conducted thus far highlights several key benefits of performing this 

field study. We can expect that consumer behavior and adaption throughout a study period will 

be significant so the duration of the study and information sharing throughout the study should 

be considered. It is also of great value to observe similar load characteristics in the field as those 

assessed in simulation studies prior to the experiment. The results suggest that electrical 

connectivity and electrical infrastructure requires further consideration depending on the value 

proposition perspective. We expect that consumers will want to purchase the smallest battery that 

meets their needs leading to lower purchase costs while the utility may prefer larger batteries that 

provide greater capacity for ancillary services. In order to provide the consumer and utilities the 

best value proposition, battery size and electrical infrastructure will need to be considered. An 

infrastructure cost benefit analysis would be beneficial. Capacity, power rating, and 

infrastructure availability will be important in defining the ability and benefit to utilities of 

shaping the PHEV load curve in the context of several operational perspectives that include cost, 

emissions, and distribution system congestion. 

 emissions reductions similar to those shown in the 

EPRI/NRDC study.

4 Future Work

The work conducted collaboratively between the partners in this study greatly expanded the 

knowledge base of all involved and will impact the research community. However, it also 

identified several new areas for future exploration. The following is a short elaboration of several 

of the most important next steps. 

Market expansion of PHEVs is most likely going to occur in heterogeneous and pocketed 

fashion. It would be valuable to assess how the distribution system may be impacted by a role 

out of PHEVs of various capabilities in select communities. Specifically, Xcel Energy’s Smart 

Grid City project in Boulder, Colorado would provide an ideal venue for measuring the 

distribution system impacts of PHEVs with either charge management or vehicle-to-grid 

capabilities. The distribution system state (e.g., congestion, thermal conditions) could be 

incorporated into the charge/discharge management strategy. Such a research demonstration 
15

should also address the challenges of control group scale. Working towards statistical group sizes 

of 30 or more and technology demonstration fleets of 100 or more vehicles would likely provide 

greater confidence in results and distribution of characteristics.

No discharge strategies could be evaluated during the test period due to limitations of the 

installed equipment. There is value in demonstrating the ability to support grid functions with 

mobile energy storage resources. Bi-directional power electronics equipment should be 

developed, tested, and evaluated in the field. Ideally, these units would have variable rate control 

to provide ability to tune the network with full range charge and discharge control of vehicle 

storage resources. The current field study was only capable of on/off charge control. In a related 

study, NREL is in the process of completing work on evaluating the impacts of plug in vehicles 

may have on distribution grid. [9] 

For integration with smart grid endeavors it seems that a more thorough exploration of 

communications options is needed. Cellular/GPRS technology for data flow was used in the 

current testing. Other lower cost options such as power line carrier and low power wireless 

communication should also be evaluated as they may present more scalable options.

Finally, a more detailed study of the costs and potential value to both consumers and the utility 

of expanded charge infrastructure would be beneficial. The field trial showed that approximately 

30% of the time the vehicles were not electrically connected to a charging plug. We expect that 

utilities should consider ways to provide a vehicle charging infrastructure that could allow a 

consistent charge/discharge management resource for future grid refined control. Further study is 

needed to evaluate the tradeoffs of vehicles that require high power and low power electrical 

interconnection. 

5 Conclusions

Xcel Energy’s leadership in technology innovation provided the opportunity to gain a better 

understanding of the real-world implications of a collaborative study on the benefits, the cost, 

and the emissions impacts of PHEVs on the Xcel Colorado Territory. A field study was 

conducted with a group of six vehicles of which three provided valuable data. Data were 

collected over a 16-week period in which 10 charge management scenarios were conducted. The 

primary goals of the study were to 1) quantify the real-world fuel savings benefits of PHEVs, 2) 

gain an understanding of potential utility loading scenarios due to the introduction of PHEVs, 

and 3) develop and demonstrate the equipment and tools necessary for utility-controlled 

charging. 

The analysis summarized in this report highlights several key insights about PHEVs and charge 

management. The real world performance of the cars and their energy consumption 

characteristics even in the small fleet of this field test was sufficient to support the conclusions 

drawn by the analysis of the simulation study completed in 2007. Charging loads presented 

similar shapes as those identified earlier. Several new load shapes were also developed in the 

field test. In particular, load shapes tied to renewable resources, real-time price, and ancillary 

service needs were innovative. Fuel savings in the field correlated well with those previously 

estimated and were in the range of 25-50% reduction relative to the comparable hybrid vehicle. 

The field trial data for these three vehicles suggest little variation in fuel savings due to utility 
16

charge management scenarios, suggesting that such features could be acceptable to consumers. 

No discharge scenarios were evaluated in this specific field trial study. 

Given the size of the battery and the commuting behaviors of the participants, 110 VAC charging 

at home and work provided substantial benefit. For this study group, connectivity to the grid was 

observed 40-60% of the time. It was noted that connectivity during weekend mid-day hours was 

low and could likely be addressed with infrastructure expansion.

The hardware, software, and analyses conducted under this collaborative project constitutes 

ground-breaking work that is helping the industry accelerate its adoption of fuel saving 

electrified transportation technologies through integration with utility grid operations.

6 Acknowledgements 

The authors would like to thank the Xcel Energy management team for their patience and 

willingness to pursue world-changing technologies. The NREL management team is also 

acknowledged for their support of industry partnerships and collaboration necessary to accelerate 

the scale and pace of energy technology adoption. 

7 References and Bibliography

1. “Environmental Assessment of Plug-In Hybrid Electric Vehicles. Volume 1: Nationwide 

Greenhouse Gas Emissions.” EPRI, Palo Alto, CA: 2007. 1015325. 

2. R. Sioshansi and P. Denholm. “Emissions Impacts and Benefits of Plug-In Hybrid 

Electric Vehicles and Vehicle-to-Grid Services.” Environ. Sci. Technol., 2009, 43 (4), pp 

1199–1204, January 22, 2009. 

3. A. Elgowainy, A. Burnham, M. Wang, J. Molburg, and A. Rousseau. “Well-to-Wheels 

Energy Use and Greenhouse Gas Emissions Analysis of Plug-in Hybrid Electric 

Vehicles.” Argonne National Laboratory.ANL/ESD/09-2. February, 2009. 

4. S. W. Hadley, A. Tsvetkova. “Potential Impacts of Plug-in Hybrid Electric Vehicles on 

Regional Power Generation.” Oak Ridge National Laboratories, ORNL/TM-2007/150, 

2008. 

5. M. Kintner-Meyer, K. Schneider, R. Pratt. “Impacts Assessment of Plug-In Hybrid 

Vehicles on Electric Utilities and Regional U.S. Power Grids. Part 1: Technical 

Analysis.” PNNL-SA-53700. 2007. 

6. E. Tate and P.J. Savagian, “The CO2

7. K. Parks, P. Denholm, and T. Markel “Costs and Emissions Associated with Plug-In 

Hybrid Electric Vehicle Charging in the Xcel Energy Colorado Service Territory.” 

NREL/TP-640-41410 May 2007. 

Benefits of Electrification: E-REVs, PHEVs and 

Charging Scenarios.” SAE World Congress. April 2009. SAE Document 2009-01-1311. 
17

8. B. Kramer, S. Chakraborty and B. Kroposki, “A review of plug-in vehicles and vehicleto-grid capability”, IEEE Industrial Electronics Society Annual Conference, IECON 

2008, November 2008. 

9. P. B. Evans, S. Kuloor, B. Kroposki, “Impacts of Plug-in Vehicles and Distributed 

Storage on Electric Power Delivery Networks.” To be published IEEE Vehicle Power 

and Propulsion Conference. Sept. 2009. Detroit, MI. 

10. J. Gonder, T. Markel, M. Thornton, A. Simpson, “Using Global Positioning System 

Travel Data to Assess Real-World Energy Use of Plug-In Hybrid Electric Vehicles.” 

Transportation Research Record: Journal of the Transportation Research Board. Vol. 

2017, 2007; pp. 26-32; NREL Report No. JA-540-41444. doi:10.3141/2017-04 

11. P. Denholm, W. Short, “Evaluation of Utility System Impacts and Benefits of Optimally 

Dispatched Plug-In Hybrid Electric Vehicles (Revised).” 30 pp.; NREL Report No. TP620-40293. 

12. T. Markel, A. Simpson, “Cost-Benefit Analysis of Plug-In Hybrid Electric Vehicle 

Technology.” (WEVA-2006-053). World Electric Vehicle Association Journal. 8 pp.; 

NREL Report No. JA-540-40969. 
F1147-E(10/2008)

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Field Testing Plug-in Hybrid Electric Vehicles with Charge Control 

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Results of a joint study by Xcel Energy and NREL to understand the fuel displacement potential, costs, and 

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