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

Efficiency & Renewable Energy, operated by the Alliance for Sustainable Energy, LLC.

Contract No. DE-AC36-08GO28308

Analysis of Off-Board Powered 

Thermal Preconditioning in 

Electric Drive Vehicles

Preprint 

Robb A. Barnitt, Aaron D. Brooker,

Laurie Ramroth, John Rugh, and

Kandler A. Smith

Presented at the 25th World Battery, Hybrid and Fuel Cell Electric 

Vehicle Symposium & Exhibition

Shenzhen, China

November 5 – 9, 2010

Conference Paper

NREL/CP-5400-49252

December 2010
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1

Analysis of Off-Board Powered

Thermal Preconditioning in Electric Drive Vehicles

Robb A. Barnitt, Aaron D. Brooker, Laurie Ramroth, John Rugh, and Kandler A. Smith

National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, U.S.A.

E-mail: robb.barnitt@nrel.gov

Abstract—Following a hot or cold thermal soak, vehicle climate control systems (air conditioning or heat) are required 

to quickly attain a cabin temperature comfortable to the vehicle occupants. In a plug-in hybrid electric or electric vehicle 

(PEV) equipped with electric climate control systems, the traction battery is the sole on-board power source. Depleting 

the battery for immediate climate control results in a reduced charge-depleting (CD) range and additional battery wear. 

PEV cabin and battery thermal preconditioning using off-board power supplied by the grid or a building can mitigate the 

CD range reduction and battery life impacts of climate control. To quantify the impact, the National Renewable Energy 

Laboratory (NREL) applied the Powertrain Systems Analysis Toolkit vehicle simulation program to develop and validate 

models of three relevant PEV platforms: a blended plug-in hybrid electric vehicle (PHEV) with a 15-mile (24-km) 

electric range (PHEV15), a series PHEV with a 40-mile (64-km) electric range (PHEV40s), and an electric vehicle with a 

100-mile (161-km) electric range (EV). Second, NREL surveyed literature and test data to develop representative air 

conditioning and heater load profiles. Next, NREL simulated PEV performance with and without thermal 

preconditioning over the UDDS and HWFET drive cycles, and for three different ambient temperature scenarios. Finally, 

battery wear was characterized using a physically justified semi-empirical lithium ion battery life model. This analysis 

shows that climate control loads can reduce CD range up to 35%. However, cabin thermal preconditioning can increase 

CD range up to 19% when compared to no thermal preconditioning. In addition, this analysis shows that while battery 

capacity loss over time is driven by ambient temperature rather than climate control loads, concurrent battery thermal 

preconditioning can reduce capacity loss up to 7% by reducing pack temperature in a high ambient temperature scenario.

Keywords— EV, PEV, PHEV, thermal, climate control

1. Introduction

Production and sales of plug-in hybrid electric and 

electric vehicles are forecasted to increase in the coming 

years. PEVs are viewed as a means to reduce liquid 

petroleum fuel consumption by using a greater fraction of 

electrical energy supplied by an on-board battery. The 

charge-depleting (CD) range of a PEV is limited by onboard battery capacity, which is used not only for driving

but also other loads. Notably, climate control loads 

(heating and cooling) can reduce the PEV’s CD range 

and/or cause the internal combustion engine to operate 

more frequently. Climate control loads increase PEV 

operating costs (liquid fuel and battery wear) and diminish 

the PEV’s intended usability (decreased CD range). PEVs 

represent a unique opportunity to thermally precondition a 

vehicle when it is plugged into an off-board power source. 

During hot or cold weather, the climate control load on the 

on-board power source is high at startup to cool down or 

warm up the vehicle from a thermal-soaked condition to a 

comfortable condition. If the cool down or warm up can be 

accomplished during battery charging, the higher transient 

climate control load on the power source could be 

eliminated. The reduction of the climate control load due 

to preconditioning has the potential to reduce fuel 

consumption and partially restore CD range. Additional 

advantages include improved battery life, improved 

occupant thermal comfort, and potentially improved safety 

due to enhanced driver vigilance.

2. Project Approach

 This section describes the approach to vehicle selection, 

model development and validation, fuel economy

calculation, climate control load profile development, 

battery life modeling, and climate control scenarios.

2.1 Vehicle Selection

PEVs that operate in CD mode at the beginning of a trip 

can potentially benefit from off-board powered thermal 

preconditioning. The most relevant PEV platforms that are 

scheduled for near-term market release are:

1. PHEV15—a blended PHEV with an approximately 

15-mile (23.4-km) all-electric range (AER) under 

certain usage conditions.

2. PHEV40s—a series PHEV designed to provide up 

to 40 miles (64 km) of AER, then operate in 

charge-sustaining (CS) mode using a rangeextending gasoline engine.

3. EV—an EV designed to provide up to 100 miles of 

AER.

 All three PEVs use electric heating and cooling climate 

control systems.

2.2 Vehicle Model Development

Vehicle models were assembled using the Powertrain 

Systems Analysis Toolkit (PSAT). Relevant vehicle model 

specifications for each vehicle platform are presented in 

Table 1.
2 

Table 1: Vehicle Model Inputs

PHEV15 PHEV40s EV

Cd 0.25 0.28 0.29 

Frontal Area (m2

) 2.07 2.09 2.33 

Vehicle Mass (kg) 1,490 1,588 1,271 

Engine Power (kW) 73 53 NA 

Motor Power (kW) 60, 42 100 80 

Battery Capacity

(kWh) 5.2 16 24 

Battery Delta State of 

Charge (SOC) 66% 54% 84% 

Battery Maximum 

SOC 80% 80% 95% 

Battery Thermal 

Management Strategy 

Air 

cooling 

Liquid 

cooling 

No 

active 

cooling 

Battery Heat Transfer 

Coefficient (W/m2

K) 20 110 0 

Accessory Load (W) 300 300 300 

2.3 Vehicle Model Validation 

Once assembled, the models were validated based on 

fuel consumption in CS mode, CD range, and acceleration 

performance. Generally, the simulated results fell within 

10% of the published data (Table 2). 

Table 2: Vehicle Model Validation Results 

PHEV15 PHEV40s EV 

CD Range (km)

Published 23.4 64.4 160.9 

Simulated 24.9 66.3 168.8 

Error +6.6% +3.0% +4.9% 

Fuel Consumption CS Mode (L/100km)

Published 3.27 4.70 NA 

Simulated 3.42 4.39 NA 

Error +4.59% -6.60% 

0-60 mph Acceleration 

Published 10.9 8.5 NA 

Simulated 9.6 8.9 NA 

Error -11.9% +4.7% 

2.4 Fuel Economy and CD Range Calculations 

 A series of steps is used to estimate conventional and 

hybrid electric vehicle fuel economy. Before 2008, the 

U.S. Environmental Protection Agency (EPA) estimated 

vehicle fuel economy using two cycles, one representing 

city driving, and the other representing highway driving. 

Since these tests underestimated the amount of fuel use 

consumers would typically experience, each test result was 

multiplied by an adjustment factor. A weighted average 

was then used to combine the two adjusted test results. 

 In 2008, three more cycles were added to the test 

procedure to improve the fuel economy estimate. The fivecycle test procedure would take especially long to run for 

PHEVs. PHEVs have two fuel economies that characterize 

their performance on a drive cycle, the CD fuel economy 

and the charge sustaining (CS) fuel economy. Both 

estimates are needed to calculate a combined average 

based on how much driving is done in each mode. To 

calculate the two fuel economies, each drive cycle must be 

repeated until the vehicle depletes the battery and runs one 

complete CS mode cycle. Repeating five cycles multiple 

times is computationally intensive. 

 EPA derived a two-cycle approximation of the five-cycle 

test, as seen below in equations (1) and (2). This was used 

in this study to reduce computational time. For the 

PHEVs, the two-cycle approximation is used for CD mode 

and CS mode, as described in [1]. A weighted average of 

the two different mode fuel economies is then calculated 

based on statistics that show the distance typically driven 

in each mode. 

ܥ݅ݕݐ ܯܲܩ ൌ 1/ሺ.003259 ൅ ଵ.ଵ଼଴ହ

஼௜௧௬ ெ௉ீሻ (1) 

଺଺ଷସ.ଵ ൅ .001376ሺ1/ ൌ ܩܲܯ ݕܽݓ݄݅݃ܪ

ுிா் ெ௉ீሻ (2) 

 The CD range estimate used for the fuel economy 

calculation would not work for this study. It is based on 

discrete cycle increments, which would not capture the 

shorter cycle changes caused by preconditioning. Instead, 

this study used SOC values to estimate the CD range. 

Specifically, the CD distance was defined as the distance 

at which the SOC first reaches the average CS SOC plus 

1%. One percent SOC was added to the average CS SOC 

to improve the consistency of the method. Without the 

addition, the CD range did not consistently line up well 

with where the SOC leveled out. 

 A similar approach was used to estimate the range of the 

EV. This approach also used the two-cycle approximation 

of the five-cycle test procedure. Also, like the way each 

cycle was repeated multiple times for the PHEVs to 

estimate CD and CS mode fuel economies, the cycles were 

repeated twice for the EV to account for the higher heating 

or A/C load during the first cycle. Each depletion rate was 

then converted to a miles per gallon gasoline equivalent 

and adjusted using the two-cycle approximation equations. 

Unlike the PHEVs, a 30% fuel consumption adjustment 

ceiling was used to prevent the equations from 

extrapolating too far outside their intended domain. The 

two adjusted cycle consumption rates were then averaged 

based on the distance that would be driven in each mode, 

similar to how the CS and CD modes were averaged for 

PHEVs. Finally, the averaged adjusted city and highway 

results were average-weighted 55% and 45%, respectively, 

to come to a single fuel consumption rate. This rate was 

then multiplied by the usable capacity to estimate the total 

range. 

2.5 Climate Control Loads 

 A climate control load is divided into two parts: 

1. Transient—After a thermal soak, the transient climate 

control is characterized by a high initial load that 

decreases with time. An example is entering a hot 

vehicle after parking in the sun, driving, and having 

the air conditioning (A/C) on with maximum blower 

airflow to cool the interior. Vehicles have different 

transient times due to a variety of factors based on 
3

manufacturer design choices. We selected 10 minutes 

as a representative transient duration.

2. Steady State—During steady state, the impact of the 

thermal soak has been diminished. The climate 

control system maintains the thermal conditions in the 

passenger compartment. An example is driving down 

the interstate in the winter and having a moderate heat 

setting with the blower on low.

 Thermal preconditioning eliminates the transient climate 

control load on the battery. In this situation, the on-board 

power supply has only to provide the steady-state climate 

control load. We surveyed literature and test data to 

develop representative A/C and heater load profiles for our 

simulation vehicles.

2.5.1 Cooling

 For the A/C load, we constructed a load vs. time profile 

that was representative across our range of vehicles. Table 

3 shows the range of vehicle types, environments, and A/C 

systems from a variety of sources that we considered. 

Table 3: Cooling Load Data Sources

Source Vehicle Environment A/C

SAE 

ARCRP [2]

N/A, 

bench data hot mechanical

ANL [3] small EV moderate electrical

NREL [4] Prius hot electrical

Ford [5] Fusion hot electrical

ANL [3]

Mercedes 

S400 moderate electrical

Visteon [6]

midsized 

SUV hot mechanical

 The data from these sources were averaged to create a 

composite load profile. The 10-minute transient load was 

applied to the model as a linear decay from a peak power 

of 3.89 kW at the start of the drive to a 2.10-kW steadystate load. This equates to an average transient load of 2.99 

kW for the 10-minute period (Table 5). For the thermal 

preconditioning case, the steady-state load of 2.1 kW is 

applied at the start of driving. Additionally, an electric 

condenser fan is assumed to draw 150 W during the 10-

minute transient and 50 W during the steady-state period 

[7]. 

2.5.2 Heating

 For the electric heating load, it was not possible to define 

a single load profile for all vehicles because of the 

different control strategies to use electric power in PHEVs 

and the availability of waste heat in some vehicles. We 

reviewed the literature and defined composite electric 

heating loads for a PHEV15, PHEV40s, and EV. Table 4

shows the vehicle types and environments we considered 

from a variety of sources. 

 PHEV15—The electric heaters transition from 4 kW to 0 

kW in 10 minutes as waste heat becomes available in the 

no thermal preconditioning scenario. For the thermal 

preconditioning scenario, the electric heater is not used. As 

the vehicle begins to operate in CS mode and the engine 

operates intermittently, waste heat will be available for 

cabin heating.

 PHEV40s and EV—There is no waste engine heat, and 

all the heating power is supplied by electric heaters. There 

is a peak load of 6 kW initially that decreases to 2 kW at 

10 minutes (Table 5). For the thermal preconditioning 

scenario, the 2 kW load is applied at all time points during 

the simulation. 

Table 4: Heating Load Data Sources

Source Vehicle Environment

Behr [8] analysis cold

ANL [3] small EV moderate

GM [9] conventional cold

Ford [10] EV cold

GM [11] HEV cold

Valeo [12, 13, 

14] EV cold

Table 5: Climate Control Load Profiles

Mode Vehicle

Peak 

Load 

(kW)

Average 

Transient 

Load 

(kW)

Steadystate 

Load 

(kW)

A/C all 3.89 2.99 2.1

heat PHEV15 4 2 0

heat

PHEV40s 

and EV 6 4 2

 In the development of the SAE mobile A/C life cycle 

climate performance model, it was assumed that the 

blower was operated any time the vehicle was operated 

[15]. Our analysis was consistent with this, and a 150-W 

blower load was applied during all runs.

2.6 Battery Life

 Battery aging is caused by multiple phenomena related to 

both cycling and calendar age. Battery degradation is 

accelerated with the depth-of-discharge (DoD) of cycling, 

elevated temperature, and elevated voltage exposure, 

among other factors. Worst-case aging conditions drive the 

need to oversize batteries to meet warranty requirements. 

Systems and controls, such as thermal preconditioning,

may be able to lessen the impact of some of these 

conditions.

At the battery terminals, the observable effects of 

degradation are an increase in resistance and a reduction in 

capacity. These two effects can be correlated with power 

and energy loss that cause battery end-of-life in an 

application. Mechanisms for resistance growth include 

loss of electrical conduction paths in the electrodes, 

fracture and isolation of electrode sites, growth of film 

layers at the electrode surface, and degradation of 

electrolyte. Mechanisms for capacity loss include fracture, 

isolation, and chemical degradation of electrode material, 

as well as loss of cyclable lithium (Li) from the system as 

a byproduct of side reactions.
4

Under storage or calendar-aging conditions, the 

dominant fade mechanism is typically growth of a 

resistive film layer at the electrode surface. As the layer 

grows, cyclable Li is also consumed from the system, 

reducing capacity. In the present model, resistance growth 

and Li-capacity loss are assumed to be proportional to the 

square-root of time, t

1/2, typical of diffusion-limited filmgrowth processes. Under cycling-intense conditions, 

degradation is mainly caused by structural degradation of 

the electrode matrix and active sites. Cycling-driven 

degradation is assumed to be proportional to the number of 

cycles, N.

Cell resistance growth due to calendar- and cyclingdriven mechanisms are assumed to be additive,

R a a t a2N 1/ 2 = 0 + 1 + (3) (1)

 Cell capacity is assumed to be controlled by either loss of 

cyclable Li or loss of electrode sites,

min( , ) Q = QLi Qsites (4) (2)

where 1/ 2

0 1 Q b b t Li = + , and (5) (3) 

Qsites = c0 + c1N (6) (4)

 Models (3), (5), and (6) are readily fit to a resistance or 

capacity trajectory measured over time for one specific

storage or cycling condition. Using multiple storage and 

cycling datasets, functional dependence can be built for 

rate constants a1(T, V, ΔDoD), a2(T, V, ΔDoD), b1(T, V,

ΔDoD), c1(T, V, ΔDoD). The present battery life model 

was fit to laboratory aging datasets [16-19] for the Li-ion 

graphite/nickel-cobalt-aluminum (NCA) chemistry as 

described in [19]. The NCA chemistry has generally 

graceful aging characteristics, and is expected to achieve 8

or more years of life when sized appropriately for a 

vehicle application.

2.7 Climate Control and Temperature Scenarios

 Battery degradation is greatly affected by temperature, 

both while the vehicle is driving as well as while the 

vehicle is parked. Battery temperature when parked will be 

affected by recent driving history, outside ambient 

conditions, and heat dissipation path to outside ambient 

conditions where those ambient conditions have strong 

daily and annual variations. As an initial study, the present 

work neglects temperature variation due to variable 

ambient conditions. 

 Each climate control scenario incorporated an ambient 

temperature condition. For scenarios that include thermal 

preconditioning, the battery pack temperature was adjusted 

from ambient temperature. That is, for thermal 

preconditioning scenarios, the battery was warmed above a 

cold ambient temperature or was cooled below a hot 

ambient temperature over a 20-minute period prior to 

driving. These climate control, ambient, and battery pack 

temperature scenarios are presented in Table 6.

Table 6: Climate Control, Temperature Scenarios

Climate 

Control 

Scenario

Ambient 

Temp.

Thermal 

Preconditioning

Initial 

Battery 

Temp.

A/C on 

(hot) 35°C yes 26.7°C

no 35°C

Heat on 

(cold) -6.7°C yes 1.7°C

no -6.7°C

Neither 

A/C nor 

heat on

20°C NA 20°C

 Twenty-four–hour profiles for battery temperature were

created using battery heat generation rates taken from 

previously described vehicle simulations. As shown in 

Figure 1, the profiles assume a daily travel distance of

52.8 km/day (33 miles/day), divided into two driving trips, 

one at 8:00 a.m. and one at 5:00 p.m. Battery charging 

occurs at 10:00 p.m. at a 6.6-kW rate. For cases with 

thermal preconditioning, the two daily driving trips are 

preceded by a 20-minute ramp to the preconditioned 

temperature.

Figure 1: Battery temperature and SOC profiles for 

PHEV40s, 35°C ambient temperature, with and without 

thermal preconditioning

3. Results

This section presents the results of vehicle performance 

and battery life analyses for the range of climate control 

system usage, ambient and battery temperature, and 

thermal preconditioning scenarios.

3.1 Vehicle Performance

Fuel consumption and CD range were simulated for 

each vehicle platform, with and without thermal 

preconditioning, for each climate control scenario. Results 

indicate the relatively large impact of climate control on 

CD range reduction, as well as the benefit of thermal 

preconditioning in avoiding climate control systeminduced battery discharge.

Figure 2 presents results for the PHEV15. This vehicle 

was modeled to use both engine and battery as needed in a 

blended fashion. Using heat increases fuel consumption by 

3.3% and decreases the CD range by 19.5%. Using A/C 

increases fuel consumption by 49.3% and decreases the 

CD range by 32.3%. Thermal preconditioning provides 

measureable benefits by reducing the initial climate 

6 am 10 am 3 pm 8 pm 1 am 6 am

Rest

20 minute preconditioning

8:00 am: 16.5 mile trip

Rest

20 minute preconditioning

5:00 pm: 16.5 mile trip

10:00 pm: Charge at 6.6 kW

Rest

6 am 10 am 3 pm 8 pm 1 am 6 am
5

control system load. Compared to no thermal 

preconditioning, thermal preconditioning with heat 

decreases fuel consumption by 1.4% and increases CD 

range by 19.2%. Compared to no thermal preconditioning, 

thermal preconditioning with A/C decreases fuel 

consumption by 0.6% and increases the CD range by 

5.2%. 

Figure 2: PHEV15 performance

 Figure 3 presents results for the PHEV40s. Using heat 

increases fuel consumption by 60.7% and decreases the 

CD range by 35.1%. Using A/C increases fuel 

consumption by 56.8% and decreases the CD range by 

34%. Compared to no thermal preconditioning, thermal 

preconditioning with heat decreases fuel consumption by 

2.7% and increases the CD range by 5.7%. Compared to 

no thermal preconditioning, thermal preconditioning with 

A/C decreases fuel consumption by 1.5% and increases the

CD range by 4.3%.

Figure 3: PHEV40s performance

 Figure 4 presents results for the EV. Using heat decreases 

the CD range by 34.7%. Using A/C decreases the CD 

range by 32.7%. Compared to no thermal preconditioning, 

thermal preconditioning with heat increases the CD range 

by 3.9%. Compared to no thermal preconditioning, 

thermal preconditioning with A/C increases the CD range 

by 1.7%.

Figure 4: EV performance

3.2 Battery Life Impacts

Battery 24-hour duty-cycle profiles were input into the 

life model described in Section 2.5 to simulate battery 

resistance growth and capacity fade over 10 years. Those 

results are presented here as a percent-per-year 

degradation rate. The primary factor causing different 

battery degradation rates between preconditioned and nonpreconditioned cases is the battery temperature exposure.

Non-thermally-preconditioned vehicles also experience 

slightly deeper battery discharges each day, although this 

is a minor factor in the present battery degradation

predictions.

Figure 5 shows percent resistance growth per year (blue 

bar), percent capacity loss per year (green bar), and battery 

average temperature (red symbol) for the PHEV15 for the 

various constant ambient temperatures, with and without 

preconditioning. For reference, end-of-life is commonly 

defined when battery remaining capacity has reached 70% 

to 80% of beginning-of-life capacity. A 2.5% capacity loss 

per year would result in 80% remaining capacity after 8 

years. For example, a 2.0% capacity loss per year in 

Figure 5 would result in 80% remaining after 10 years. 

Ambient temperature has the strongest influence on 

battery degradation rates. Compared to the 20°C baseline 

case, the 35°C ambient case with no preconditioning

increases capacity fade rates by 43%. The −6.7°C ambient 

case reduces fade by 52% relative to 20°C ambient.

Battery fade rates for actual geographic locations will be 

a composite of the constant ambient temperatures 

simulated here. In the United States, Phoenix, Arizona, is a 

typical worst-case high-temperature location, with annual 

and daily temperature variation expected to cause battery 

degradation similar to a 30°C constant temperature aging 

condition [20].

For the PHEV15 in Figure 5, thermal pre-heating 

at -6.7°C ambient has a slight negative impact on battery 

capacity loss, increasing fade rate by 4.5%. At such low 

temperatures, however, the small fade rates are relatively 

inconsequential. Hot ambient conditions will derive the 

most benefit from thermal pre-cooling. At 35°C ambient 

temperature, pre-cooling decreases the capacity-fade rate 

by 2.1% for the PHEV15 with air-cooled battery. This 

reduction in the hot-climate fade rate can be used in either 

of two ways: (1) if battery size is fixed, a preconditioned 
6

battery will last longer than a non-preconditioned battery, 

or (2) if battery size is not fixed, a preconditioned battery 

can be sized slightly smaller (with lower cost) and still 

achieve the same life as a non-preconditioned battery.

Figure 5: PHEV15 battery degradation rates (left axis) and 

average temperature (right axis)

Figure 6 and Figure 7 show battery degradation rates for 

the PHEV40s and EV platforms, respectively. Trends are 

similar to the PHEV15. At 35°C ambient, thermal 

preconditioning reduces capacity-loss rate by 4.1% and 

7.1% for the respective PHEV40s and EV platforms. 

Reductions in resistance-growth rate are 7.0% and 13.8% 

for the respective platforms.

Figure 6: PHEV40s battery degradation rates (left axis) and 

average temperature (right axis)

Figure 7: EV battery degradation rates (left axis) and 

average temperature (right axis)

In summary, pre-cooling of electric-drive vehicle 

batteries is predicted to reduce capacity fade by 2.1% to 

7.1% and resistance growth by 3.0% to 13.8% in hot 

(35°C) ambient conditions. In a hot geographic location 

such as Phoenix, Arizona, (where degradation due to 

fluctuating ambient temperature is similar to constant

30°C aging), the realized reduction in battery degradation 

will be slightly less. The three vehicle platforms each 

derive slightly different benefits from pre-cooling, partly 

due to the assumed battery thermal management strategies 

(Table 1) and partly due to the size of each vehicle’s 

battery. Battery temperature rise results from multiple 

factors, namely battery thermal mass, heat generation rate 

while driving, and rate of active cooling. Energy storage 

systems that benefit most from pre-cooling will be those 

with small battery thermal mass, those with high heat 

generation rates, and those with limited or no active 

cooling while driving. Each of these systems is likely to 

experience a large temperature rise while driving and will 

benefit from starting a driving trip with a pre-cooled 

battery.

4. Conclusions

 This analysis shows that climate control system loads can 

significantly increase fuel consumption (up to 60.7%) and 

decrease CD range (up to 35.1%) in PEVs. Off-board 

powered thermal preconditioning of a vehicle cabin is one 

way to reduce the negative impact of climate control 

system loads. When compared to no thermal 

preconditioning, thermal preconditioning can provide a 

moderate reduction in fuel consumption (up to 2.7%). 

However, thermal preconditioning can partially restore CD 

range (up to 19.2%). The restoration of several kilometers 

of CD range may resonate with consumers for whom 

“range anxiety” is an issue and potential barrier to 

widespread adoption of PEVs.

 Pre-cooling of electric-drive vehicle batteries is predicted 

to reduce capacity fade by 2% to 7% and resistance growth 

by 3% to 14% in hot (35°C) ambient conditions. Vehicles 

that benefit most from battery pre-cooling will be those 

with small battery thermal mass or high heat generation 

rates (i.e., PHEVs with a short electric range) and those 

with limited battery active cooling systems.

 Off-board powered thermal preconditioning has benefits 

to the consumer via CD range extension and less 

expensive energy costs (electricity versus liquid fuel 

and/or battery capacity), as well as vehicle manufacturers 

via extended battery life and avoided warranty claims.

5. References

[1] Gonder, J., Brooker, A., Carlson, R., and Smart, J. 

Deriving In-Use PHEV Fuel Economy Predictions from 

Standardized Test Cycle Results, presented at the 5th IEEE 

Vehicle Power and Propulsion Conference, Dearborn, 

Michigan, 2009 (NREL/CP-540-46251).

[2] Society of Automotive Engineers, Cooperative Research 

Program: Alternative Refrigerant Project Phase I, Draft 

Final Report, July 14, 2003.

[3] Lohse-Busch, H., Small EV Testing and Analysis, 

Presentation to DOE. Argonne National Laboratory

November 17, 2009. email re: Test data, APRF.

-6.7°C 

Ambient

-6.7°C 

Ambient 

(preconditioned)

20°C Ambient 35°C Ambient

35°C Ambient

(preconditioned)

Resistance Growth 2.0 2.1 4.1 6.6 6.4

Capacity Loss 1.2 1.3 2.5 3.6 3.5

Average Temperature -4.2 -3.4 21.6 35.9 35.2

-10

-5

0

5

10

15

20

25

30

35

40

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Battery Pack Temperature (oC)

Resistance Growth or Capacity Loss

(% per year)

-6.7°C 

Ambient

-6.7°C 

Ambient 

(preconditioned)

20°C Ambient 35°C Ambient

35°C Ambient

(preconditioned)

Resistance Growth 0.8 1.1 2.2 4.2 3.9

Capacity Loss 1.0 1.1 2.1 3.0 2.9

Average Temperature -3.2 -1.3 21.7 35.7 34.0

-10

-5

0

5

10

15

20

25

30

35

40

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Battery Pack Temperature (oC)

Resistance Growth or Capacity Loss

% per year

-6.7°C 

Ambient

-6.7°C 

Ambient 

(preconditioned)

20°C Ambient 35°C Ambient

35°C Ambient

(preconditioned)

Resistance Growth 1.3 1.3 2.1 5.4 4.7

Capacity Loss 1.4 1.5 2.5 3.9 3.6

Average Temperature -1.5 -0.2 21.7 37.4 35.0

-5

0

5

10

15

20

25

30

35

40

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Battery Pack Temperature (oC)

Resistance Growth or Capacity Loss

% per year
7

[4] Rugh, J. (2009). Impact of Sungate EP on PHEV 

Performance: Results of a Simulated Solar Reflective 

Glass PHEV Dynamometer Test. 32 pp.; NREL Report No. 

TP-540-45908. Golden, CO: National Renewable Energy 

Laboratory.

[5] Maranville, C., Ford Motor Company, personal email.

Ford Fusion cooling load. June 4, 2010.

[6] Meyer, J., Visteon Corporation, A/C Cooling Load data, 

March 1, 2007.

[7] Papasavva, S., Hill, W.R., and Brown, R.O., “GREENMAC-LCCP®: A Tool for Assessing Life Cycle 

Greenhouse Emissions of Alternative Refrigerants,”

Proceedings of the 2008 SAE Congress, Detroit, MI. Paper 

# 2008-01-0829. Society of Automotive Engineers.

[8] Feuerecker, G., Schafer, B, Strauss, and T. Behr.,

“Auxiliary Heating Systems of Conventional and Heat 

Pump Type: Technology, Performance, and Efficiency,”

Proceedings of the 7th Vehicle Thermal Management 

Systems Conference, May 10-12, 2005, Toronto, Canada, 

Paper # 2005-01-2055, Society of Automotive Engineers. 

2005-01-2055 SAE.

[9] Hill, W. Retired from GM, personal communication, June 

15, 2010.

[10] Maranville, C. Ford Motor Company, personal 

communication, June 4, 2010.

[11] Zeng, X., Major, G., Hirao, T., and Imaiida, T., “An 

Automotive Hybrid Heating System for Parallel Hybrid 

Passenger Cars,” Proceedings of the 2000 SAE Congress, 

Detroit, MI., March, 2000, Paper # 2000-01-1276.

[12] Roy, D., Petitjean, P., Clodic, D., and El Khoudy, K., 

“Influence of Thermal Preconditioning technologies on 

A/C System Power and Passenger Thermal Comfort,” , 

Proceedings of the 6th Vehicle Thermal Management 

Systems Conference, May 18-21, 2001, Brighton, UK, 

Paper # C599/004/2003.

[13] Dauvergne, J., “Thermal Comfort of Electric Vehicles,” 

Proceedings of the 1994 SAE Congress, Detroit, MI. 

March 1994, Paper #. 940295. Society of Automotive 

Engineers. Society of Automotive Engineers.

[14] Pomme, V., “Reversible Heat Pump System for an 

Electrical Vehicle,” Proceedings of the 1997 SAE 

Congress, Detroit, MI. May 1997, Paper #..971772, 

Society of Automotive Engineers.

[15] SAE J 2766 SURFACE VEHICLE STANDARD “Life 

Cycle Analysis to Estimate the CO2-Equivalent Emissions 

from MAC Operation,” issued February 16, 2009.

[16] Broussely, M., “Aging of Li-ion batteries and life 

prediction, an update,” 3rd Int. Symposium on Large 

Lithium-Ion Battery Technology and Application, Long 

Beach, California, May 2007.

[17] Hall, J., Lin, T., Brown, G., Biensan, P., and Bonhomme,

F., “Decay processes and life predictions for lithium ion 

satellite cells,” 4th Int. Energy Conversion Engineering 

Conf., San Diego, California, June 2006.

[18] Smart, M., Chin, K., Whitcanack, L., and Ratnakumar, B.,

“Storage characteristics of Li-ion batteries,” NASA Battery 

Workshop, Huntsville, Alabama, Nov. 2006.

[19] Broussely, M., Chap. 13 in: Advances in Lithium-Ion

Batteries, van Schalkwijk, W., and Scrosati, B., editors.

New York: Kluwer Academic / Plenum Publishers, 2002.

[20] Smith, K., Markel, T., and Pesaran, A., “PHEV Battery 

Trade-off Study and Standby Thermal Control,” 26th 

International Battery Seminar & Exhibit, Fort Lauderdale, 

FL, March, 2009.

6. Acknowledgments

The authors wish to thank Lee Slezak and David 

Anderson, U.S. Department of Energy Vehicle 

Technologies Program, for their financial support in 

conducting this project.

7. Authors

Robb A. Barnitt 

National Renewable Energy Laboratory 

Golden, Colorado USA

Email: robb.barnitt@nrel.gov

Mr. Barnitt leads Vehicle Systems 

Analysis staff and research within the 

Center for Transportation Technologies 

and Systems at NREL. He holds a B.S. 

from the University of Massachusetts at 

Amherst and a M.S. in engineering from Stanford University.

Aaron Brooker 

National Renewable Energy Laboratory 

Golden, Colorado USA

Email: aaron.brooker@nrel.gov

Mr. Brooker is a Senior Engineer at the 

National Renewable Energy Laboratory. 

He holds a B.S. in mechanical engineering 

from Michigan Technological University 

(1998) and a M.S. in mechanical 

engineering from the University of Colorado (2000).

Laurie Ramroth 

National Renewable Energy Laboratory 

Golden, Colorado USA

Email: laurie.ramroth@nrel.gov

Ms. Ramroth is an Engineer at the National 

Renewable Energy Laboratory. She holds a 

B.S. in mechanical engineering from 

Wayne State University in Detroit, 

Michigan, where she focused on alternative 

energy technologies.

John Rugh

National Renewable Energy Laboratory 

Golden, Colorado USA

Email: john.rugh@nrel.gov

Mr. Rugh is a Senior Engineer at NREL, 

where he leads CoolCab and Vehicle 

Integrated Thermal Management tasks. He 

holds a B.S. in mechanical engineering 

from Colorado State University and an

M.S. in mechanical engineering from Purdue University.

Kandler A. Smith

National Renewable Energy Laboratory 

Golden, Colorado USA

Email: kandler.smith@nrel.gov

Dr. Smith is a senior engineer researching 

energy storage systems for NREL’s 

Center for Transportation Technologies. 

He holds a Ph.D. degree in mechanical 

engineering from Pennsylvania State 

University. He is an active member of the IEEE, the 

Electrochemical Society, and the Society for Automotive 

Engineers. His research includes electric-drive vehicles, battery 

electrochemical/thermal characterization and modeling, and 

battery-life predictive modeling.
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Analysis of Off-Board Powered Thermal Preconditioning in Electric 

Drive Vehicles: Preprint

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14. ABSTRACT (Maximum 200 Words)

Following a hot or cold thermal soak, vehicle climate control systems (air conditioning or heat) are required to quickly 

attain a cabin temperature comfortable to the vehicle occupants. In a plug-in hybrid electric or electric vehicle (PEV) 

equipped with electric climate control systems, the traction battery is the sole on-board power source. Depleting the 

battery for immediate climate control results in reduced charge-depleting (CD) range and additional battery wear. 

PEV cabin and battery thermal preconditioning using off-board power supplied by the grid or a building can mitigate 

the impacts of climate control. This analysis shows that climate control loads can reduce CD range up to 35%. 

However, cabin thermal preconditioning can increase CD range up to 19% when compared to no thermal 

preconditioning. In addition, this analysis shows that while battery capacity loss over time is driven by ambient 

temperature rather than climate control loads, concurrent battery thermal preconditioning can reduce capacity loss up 

to 7% by reducing pack temperature in a high ambient temperature scenario.

15. SUBJECT TERMS

EV; PEV; PHEV; thermal; climate control

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