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Journal of Power Sources 259 (2014) 117e124

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Journal of Power Sources

journal homepage: www.elsevier .com/locate/ jpowsour

Estimating the HVAC energy consumption of plug-in electric vehicles

Kiran R. Kambly, Thomas H. Bradley*

Colorado State University, Department of Mechanical Engineering, Fort Collins, CO 80523-1374, USA

h i g h l i g h t s

� Dynamic electric vehicle thermal comfort model based on control volume approach.� Light duty single and Fleet level HVAC electrical energy (including heater) requirements.� Impact of HVAC loads on electric vehicle range.� State-wise sensitivity of range for 24 kWh electric vehicle.

a r t i c l e i n f o

Article history:Received 14 November 2013Received in revised form7 February 2014Accepted 8 February 2014Available online 1 March 2014

Keywords:Plug in electric vehicleHVAC energyCabin conditioningElectric rangeThermal comfort model

* Corresponding author. Tel.: þ1 970 491 3539; faxE-mail address: Thomas.Bradley@colostate.edu (T.

http://dx.doi.org/10.1016/j.jpowsour.2014.02.0330378-7753/� 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

Plug in electric vehicles are vehicles that use energy from the electric grid to provide tractive andaccessory power to the vehicle. Due to the limited specific energy of energy storage systems, the energyrequirements of heating, ventilation, and air conditioning (HVAC) systems for cabin conditioning cansignificantly reduce their range between charges. Factors such as local ambient temperature, local solarradiation, local humidity, length of the trip and thermal soak have been identified as primary drivers ofcabin conditioning loads and therefore of vehicle range. The objective of this paper is to develop adetailed systems-level approach to connect HVAC technologies and usage conditions to consumer-centric metrics of vehicle performance including energy consumption and range. This includes consid-eration of stochastic and transient inputs to the HVAC energy consumption model including localweather, solar loads, driving behavior, charging behavior, and regional passenger fleet population. Theresulting engineering toolset is used to determine the summation of and geographical distribution ofenergy consumption by HVAC systems in electric vehicles, and to identify regions of US where thedistributions of electric vehicle range are particularly sensitive to climate.

� 2014 Elsevier B.V. All rights reserved.

1. Introduction

Plug-in Electric Vehicles (PEVs) are an increasingly importantcomponent of the US light-duty passenger fleet [1]. PEVs generallyenable lifecycle cost savings [3], reduced lifecycle energy con-sumption [2], and lower environmental impacts [4], but thesebenefits most often come at an incremental purchase cost.

In PEVs, the energy storage system must generally provide all ofthe energy to power the full function of the vehicle includingtraction loads, accessory loads, and cabin thermal comfort condi-tioning loads. Cabin thermal comfort conditioning loads especiallyhave been the focus of continuing technological and systemdevelopment to reduce their energy consumption, but the benefitsof these systems are difficult to assess. Because of differences in

: þ1 970 491 3827.H. Bradley).

driving behavior and climate conditions, the effect of cabin thermalcomfort conditioning loads on PEVs varies regionally and tempo-rally. Despite its importance in understanding the performance,robustness, and consumer acceptability of PEVs, the role of thermalcomfort conditioning loads has been relatively under-researched. Afew studies have modeled the device-level function and energyconsumption of vehicle heating, ventilation, and air conditioning(HVAC) systems [6e9], but none have attempted to translate thesedevice-level performance metrics into geographically- ortemporally-realized, transportation-system-level energy con-sumption metrics for PEVs.

The most relevant recent work was performed by researchersat the National Renewable Energy Laboratory (NREL). Theseresearchers have found that the energy required to provide cabincooling for thermal comfort can reduce the range of PEVs from35% to 50% depending on outside weather conditions [6]. TheseNREL models did not consider the role of cabin heating on PEVenergy consumption, [6,8,9] and used a temporally- and

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seasonally-averaged model of temperature and climate. Thismodel of HVAC actuation disregards the dynamic thermal comfortconditioning requirements that result from changing environ-mental conditions (i.e. hourly changes in irradiation and ambienttemperature). In this previous work, the definition of thermalcomfort was based on Fanger’s description of person’s thermalsensation vote [10], wherein the person’s thermal sensation isrelated to the heat balance on the body as a whole. The metric of‘predicted percent dissatisfaction’ (PPD) was defined as a functionof deviation in person’s heat balance from a thermally neutralsensation. A PPD > 0 was used to be representative of a personlikely to feel too hot. Under these assumptions, PPD was treated asa statistical representation of the fraction of time the air condi-tioning (AC) is turned on [8,9,11,12], whereas in real-world PEVsheating loads (due to the lack of engine waste heat) and transientHVAC loading may be important contributors to overall energyconsumption [13].

In order to understand the detailed role of climate and drivingpatterns on the energy consumption of PEVs, this paper presents astudy of the regional differences in PEV cabin thermal comfortconditioning loads across the US. This study synthesizes dynamicmodels of thermal comfort, driver behavior, HVAC system function,and hourly weather data to model the geographical distribution ofcabin thermal comfort conditioning loads for US PEVs. Discussionfocuses on the implications of including heater loads on PEVenvironmental performance, on comparison of PEVs to conven-tional vehicles, and on the effect of HVAC energy consumption onvehicle range.

2. Methods

In this paper, we seek to understand the effects of thermalcomfort conditioning (cabin heating and cabin cooling) on PEVperformance based on a bottom-up control volume approach in-clusive of the dynamics of climate, driving behavior, cabin tem-perature, and HVAC system control and load [14]. The relationshipamong these models is represented in Fig. 1 and described in thefollowing sections.

2.1. Climate and solar irradiation modeling

The first database input to the model characterizes the envi-ronmental conditions under which the vehicle will be simulated. Inthis model, the cabin space is subjected to hourly changes inenvironmental conditions (Tamb, relative humidity, Q00

solar) and thecabin temperature (Tcabin) and HVAC loading varies accordingly. Theinput environmental conditions are derived from the National SolarResource Database (NSRDB), which contains hourly environmental

Fig. 1. Simulation architecture for cabin comfort conditioning thermal model.

data for 365 days of the year across 1019 locations within US. Theinput to the model is based on the NSRDB typical meteorologicalyear (TMY) 3 dataset.

2.2. Vehicle trip modeling

During an automobile trip, HVAC systems may or may not beused based on factors such as the timing of previous trips in thevehicle, and the ambient conditions as a function of time of day. Tocapture these interactions, the HVAC energy consumption modelmust be informed by a model of driver behavior including tripstart and stop times, trip type, type of vehicle used, and type ofdriving encountered. These data are extracted from a subset of the2009 National Household Transportation Survey (NHTS). TheNHTS is a periodic, federally-funded survey of the U.S. populationwhose purpose is to gather information on daily and long distancetravel. For the 2009 NHTS, 150,147 households completed thesurvey. Individuals are surveyed regarding their householdmakeup, personal demographics, vehicle characteristics and travelduring an assigned travel day. The NHTS survey data arenormalized using appropriate weights to represent the US popu-lation as a whole.

For this study, the NHTS subset made up of light-duty vehiclesthat were 5 years old or younger was chosen to reduce the prob-ability of including inoperable vehicles in the sample. Each driver isassumed to have the same distribution of driving behavior as doesthe NHTS, independent of geography, demography or any potentialvehicle range limitations. Each light-duty vehicle trip is performedin a vehicle with an tractive energy consumption of 300 DC Whmile�1 (186 DCWh km�1). Each vehicle is assumed to charge beforeeach trip from home.

2.3. Geographical light-duty passenger fleet modeling

In order to establish the geographical and therefore climaticdistribution of PEVs across the US, this model extracts the state-wise vehicle population distribution from the US Census Bureau,2009 and allocates EV energy consumption to the geographic re-gions of the US. A synthesis of this dataset is shown in Fig. 2, Cal-ifornia has approximately 14% of the 241.8 million registered lightduty vehicles in US. For this study, EVs are assumed to be distrib-uted among the state of the US in proportion to their conventionallight-duty vehicles population.

2.4. Vehicle thermal comfort model

The transient inputs from the NSRDB and NHTS databases arefed into the dynamic vehicle thermal comfort model which is builtusing the Matlab/Simulink simulation platform. The vehicle char-acteristics such as size of the vehicle, window configurations, andmaterial properties are used to evaluate losses by conduction,convection, and radiation. This control volume based approachdynamically evaluates the power required by the AC and the heaterto maintain the passenger’s thermal comfort for every hour inwhich the driver is driving.

Previously thermal comfort predictions were based on experi-ments conducted in a climate-controlled chamber. The physical(air temperature, mean radiant temperature, relative humidity andair velocity) and physiological variables (metabolic rates andclothing) were measured experimentally. However, numerousstudies [16,17] have shown large measurement errors in predictingactual thermal comfort, resulting from difficulty in controlling all 6variables accurately. As opposed to the approach proposed inFanger and the previous NREL studies, in this study, cabin thermalcomfort is assumed to be achieved if the cabin is maintained

Fig. 2. Passenger light duty vehicle fleet distribution across the US.

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between 23C and 27C with <50% relative humidity (RH). Thethermal set points were set based on ASHRAE map of thermalcomfort [18]. Some of the vehicle thermal comfort model pa-rameters are presented in Table 1 (see Appendix for a full listing ofmodeling parameters).

2.5. Summary

These models are synthesized to construct a temporally-,geographically- and climatically-realized cabin comfort condition-ing thermal simulation. The outputs from these simulations areused to synthesize A) the energy consumed by PEVs both at vehicle-

Table 1Simulation parameters.

Value Units Notes

Vehicle model parametersTotal cabin volume 3.29 m3 Characteristic of a mid-sized carTotal fenestration area 3.02 m2 Characteristic of a mid-sized carFront wind shield area 0.80 m2 Characteristic of a mid-sized carRear wind shield area 0.68 m2 Characteristic of a mid-sized carSide glass windows area 0.76 m2 Characteristic of a mid-sized car

HVAC power consumption model parametersAC COP 2.33 e [6], Includes compressor and

motor efficiencyHeater COP 1 e Representative of a resistance

heater

Thermal comfort model parametersRH setpoint <50 % [18]Temperature upper setpoint 27 C [18]Temperature lower setpoint 23 C [18]

level and fleet-level, B) the geographical variations in net HVACenergy consumption across US, C) the performance of EVs across USusing all electric range as a metric, and D) the net gasoline dis-placed as a result of EVs replacing conventional light-duty vehiclefleet.

3. Results

3.1. Single vehicle thermal comfort modeling

This section provides results on the function of the dynamicthermal comfort model for a single light-duty vehicle. The tran-sient inputs into the cabin comfort conditioning thermal model arethose shown in Fig. 1. The cabin space temperature is regulatedautomatically by operating the cabin heater and AC system suchthat the temperature inside the cabin is controlled to be between23 �C and 27 �C with <50% relative humidity. For an example ofthe function of dynamic thermal comfort model, two scenarios aresimulated on January 1st (0 he24 h) at 29 Palms, California. Fig. 3apresents the result of a “no trip scenario” (representative of vehiclestaying at home all day) and of a sample trip occurring between11:00 and 13:00. The resulting cabin temperatures and the hourlytrace of local ambient temperatures are presented as a function oftime of day (TOD). During the sample trip, the cabin temperature(Tcabin) is pulled down from the soak temperature (Tcabin: No Trip)at 11 h (w35 C) to the thermal comfort condition (27C) by oper-ating the AC. The HVAC electrical output power (Q00

AC and Q00heater),

local solar irradiation (Q00solar inclusive of direct and diffuse com-

ponents), and thermal losses (Q00losses inclusive of conductive and

convective losses to the environment) as a function of TOD arerepresented in Fig. 3b. The AC cooling output consists of 7800 W ofpeak transient cooling power and w1200 W of steady-stateoutput. The total HVAC electrical energy (0.52 kWh: transient,1.91 kWh: steady-state, inclusive of system COP) for the sampletrip is computed by integrating the HVAC loads during the lengthof the trip.

3.2. State-averaged fleet PEV HVAC energy consumption

Operating the HVAC system to achieve thermal comfort con-sumes on-board energy and shortens the driving range of the PEV,but this range penalty associated with HVAC operation is a strongfunction of geographical location and local weather conditions. Forexample, the geographical distribution of the annual energyconsumed by the HVAC system for a single PEV is represented inFig. 4a for each of the US states and for 12,000 mi (19,312 km) ofannual driving distance. The HVAC energy consumption is dividedinto electrical energy expended for heating and for cooling the PEVcabin space. The ratio of AC load to heater load is highest in Arizona(6.3) and lowest in Alaska (0.48). Fig. 4b shows that for similardriving conditions, a PEV in Arizona requires approximately1000 kWh (w30 gallons gasoline equivalent) more electrical en-ergy per year for passenger comfort conditioning compared to aPEV in West Virginia. This translates into the need for a PEV user inArizona to chargew54 times more per year than a PEV user inWestVirginia.

3.3. US light-duty vehicle fleet HVAC energy consumption

To estimate the total annual HVAC energy required by a hypo-thetical 100% PEV US light duty vehicle fleet the HVAC energyconsumed by a single PEV across different states (Fig. 4a) can becombined with the vehicle fleet distribution (Fig. 2), as representedin Fig. 5. For this hypothetical 100% PEV fleet, we estimate that 565billion kWh of energy would be required annually for thermal

Fig. 3. (a) Sample temperature profiles for vehicle cabin and ambient at 29 Palms CA, over a period of 24 h on January 1 of a TMY, (b) Sample HVAC output and thermal loss profilesfor 29 Palms CA over a period of 24 h on January 1 of a TMY.

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comfort, as shown in Table 2. AC energy requirements are modeledto exceed heating energy requirements by w2.75 times. This isequivalent to 17 billion gallons of gasoline consumption (w13.7% of2011 US annual oil imports [1]).

Of course, a 100% PEV light-duty fleet is not representative ofany near-term feasible scenario, but the presentation of results inthis format allows for the simple calculation of the HVAC energyconsumption of near-term light-duty fleets through multiplication

of either national or state-level energy consumption by the PEVfleet penetration fraction. For example, at present, there are 59,952PEV in the US light duty vehicle fleet, which represents 0.03% of the2009 census value of 242 million vehicles. At present, the energyallocated to heating and cooling PEVs in the US is approximately0.14 billion kWhDC.

The scale of the energy involved in thermal comfort condi-tioning of PEVs justifies the treatment of PEV HVAC systems design

Fig. 4. (a) Annual average HVAC energy consumption per light-duty vehicle in individual states of the USA, (b) US states with minimum and maximum HVAC energy consumptionper light duty vehicle per year.

K.R. Kambly, T.H. Bradley / Journal of Power Sources 259 (2014) 117e124 121

as means for achieving significant transportation-system energyconsumption reductions.

4. Implications of HVAC energy consumption on PEV energyconsumption and range

The results of the cabin comfort conditioning thermal modeldemonstrate that HVAC energy consumption in PEVs is a strongfunction of climate and therefore varies considerably among theregions of the US.

The results above are presented in terms of the energyconsumption of the HVAC system have implications on the PEV asa vehicle. In this section, we discuss the implications of HVACenergy consumption of the PEV in terms of its range. This discus-sion has relevance for PEV engineers, analysts and consumers.

4.1. PEV energy consumption analysis

Theenergyconsumptionof PEVs isan important input into studiesof PEV performance, economics, and environmental impacts [19].

Fig. 5. (a) Annual total HVAC energy consumption by the light duty vehicle fleet in individual states of USA. (b) States with minimum and maximum Total HVAC energy con-sumption per year.

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A wide variety of studies [3e7,9] of electrified vehicles havemade comparisons between PEVs and CVs on the basis of their EPA5-cycle measured fuel economy, but the EPA 5-cycle fuel economytest does not measure the effect of heating loads on the fuel

Table 2Total annual energy required for operating a hypothetical 100% PEV US light-dutyfleet.

Air conditioning energy Heating energy Traction energy

415 billion kWhDC 151 billion kWhDC 1012 billion kWhDC

economy of either EVs or CVs. Other aspects of vehicle perfor-mance that are not measured on the EPA 5-cycle test have rela-tively equivalent effects on both EVs and CVs (e.g. daytime runninglight energy consumption), but as demonstrated by the results ofthis study, the use of the heater in EVs does increase EV energyconsumption, whereas the use of the heater in CVs has no effect onfuel consumption. By using the EPA 5-cycle fuel economy results tocompare the performance of EVs to the performance of CVs, theliterature ignores the sensitivity of PEV energy consumption toheater use. Using the results of this study, we can make a com-parison of the energy consumption of PEVs and CVs that doesconsider their real-world fuel consumption due to HVAC loads.

Table 3Comparing the annual energy consumed by conventional and electric vehicles(12,000 mi/yr, mid-sized car).

Estimated energyconsumptionusing 5-cycle EPAtest methods [3,9]

Estimated energyconsumption using 5-cycleEPA test methods includingreal-world heater energy

Conventional vehicle 14.6 MWhLHV 14.6 MWhLHV

Electric vehicle 3.6 MWhDC 4.3 MWhDC

K.R. Kambly, T.H. Bradley / Journal of Power Sources 259 (2014) 117e124 123

To construct this comparison, we can consider CV with ameasured 5-cycle energy consumption of 27.5 mi gal�1 (8.55 L(100 km)�1), and a EV with a measured 5-cycle energy consump-tion of 300 WhDC mi�1 (186 WhDC km�1).

Table 3 shows the results of this comparison. The left column ofresults shows the comparison between the example CV and EVusing the EPA 5 cycle results. The right column of results comparesthe CV and EV energy consumption inclusive of the US nationalaverage AC energy consumptions (for the CV and EV) and the USnational average heater energy consumption of 744.1 kWh per year(for the EV). This comparison shows that inclusive of the energyconsumption of PEV HVAC systems, the PEVs exhibit a 74% annualenergy savings. Using EPA 5-cycle test methods [3,9] overestimatesthe energy savings due to PEVs by 28%, resulting in inaccurate es-timations of the PEV range, cost, energy consumption, and lifecycleemissions.

Fig. 7. Geographical PEV range distribution across US states.

4.2. Implications of HVAC energy consumption on PEV range

One of the primary consumer concerns with mass-market PEVsis their relatively low range and the large observed fluctuation inthat range that comes with geography and climate [15]. The resultsof this study can be used to quantify these effects for ourmodel PEV.

For this discussion, we can compare the HVAC-inclusive energyconsumption, and PEV range across various locations in the US. Thesimulations were performed across all 1019 NSRDB locations. Forexample, Fig. 6 shows the distribution of PEV range available foreach of the 365 days of the year for Los Angeles, CA and Detroit, MI.The distribution clearly indicates that the range available from thismodel PEV will vary due to different environmental conditions atthese two locations. Los Angeles has more days where the HVACenergy consumption reduces the available PEV range, whereas

Fig. 6. Performance variation of PEV at Los Angeles, CA and Detroit, MI.

Detroit has a bimodal distribution wherein milder times of yearrequire less HVAC energy consumption and therefore have lessrange impact. Fig. 7 compares the geographical distribution of ex-pected range across the US, aggregated by state. The variation inPEV range within a state is due to the variation in climate withinthat state.

5. Conclusions

This study provides a detailed analysis of the system-level im-pacts of thermal comfort on the performance of PEVs. The results ofthis study are a novel contribution toward understanding thechallenges and benefits associated with passenger fleet electrifi-cation. The modeled connections between the low-level perfor-mance of these vehicles’ accessory systems and their system-levelenergy consumption and range performance will allow vehicledesigners, policy makers, and consumers to understand the in-teractions between vehicle technologies, their conditions of use,climate, the characteristics of the US vehicle fleet, and its energyuse. As examples, consider that regional electric utilities must planfor both the tractive and HVAC loads of EVs to be included in theirload growth scenarios, consumers will have to understand that theperformance of their vehicle will be sensitive to climate, andvehicle designers should understand that improvements in HVACsystem energy consumption will improve the effectiveness withwhich EVs can meet energy consumption and sustainabilityobjectives.

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Acknowledgments

This work was supported by the Electric Power Research Insti-tute (EPRI) under grant EP-P40407/C17926.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.jpowsour.2014.02.033.

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