INTRODUCTION

When operated, the air-conditioning (A/C) system is the

largest auxiliary load on a vehicle. A/C loads account for

more than 5% of the fuel used annually for light-duty

vehicles in the United States [1]. A/C loads can significantly

impact electric vehicle (EV), plug-in hybrid electric vehicle

(PHEV), and hybrid electric vehicle (HEV) performance.

Mitsubishi reports that the range of the i-MiEV can be

reduced by as much as 50% on the Japan 10-15 cycle when

the A/C is operating [2]. The advanced powertrain research

facility at Argonne National Laboratory has reported a nearly

20% reduction in range in the Nissan Leaf operating on the

UDDS cycle. A hybrid vehicle tested at NREL showed 22%

lower fuel economy with the A/C on [3]. Increased cooling

demands from the battery thermal management system in an

EV may impact the A/C system. Heavy-duty vehicles also

need a tool to evaluate the impact of A/C on both “down-theroad” and idle conditions. Cabin climate conditioning is one

of the primary reasons for operating the main engine in a

long-haul truck during driver rest periods. In the United

States, long-haul trucks (trucks that travel more than 500

miles per day) use 838 million gallons of fuel annually for

rest period idling [4]. A flexible open-source analysis tool is

needed to assess the A/C system's impact on advanced

vehicles. The industry has expressed a need for both a

standalone A/C system model as well as an A/C model that

can co-simulate with a vehicle simulator such as Autonomie

[5]. This model expands the capability of vehicle simulation

tools, including Autonomie, and addresses industry needs.

The A/C system contains complex flow, thermodynamics,

and heat transfer. On the refrigerant side, the flow is transient

and both compressible and two-phase. Calculating refrigerant

properties near the phase transitions can also be

computationally difficult. Air flow through the condenser can

vary widely depending on vehicle speed and condenser fan

2013-01-0850

Published 04/08/2013

doi:10.4271/2013-01-0850

saepcmech.saejournals.org

A New Automotive Air Conditioning System Simulation Tool

Developed in MATLAB/Simulink

Tibor Kiss and Lawrence Chaney

National Renewable Energy Laboratory

John Meyer

Visteon Corporation

ABSTRACT

Accurate evaluation of vehicles' transient total power requirement helps achieving further improvements in vehicle fuel

efficiency. When operated, the air-conditioning (A/C) system is the largest auxiliary load on a vehicle, therefore accurate

evaluation of the load it places on the vehicle's engine and/or energy storage system is especially important. Vehicle

simulation models, such as “Autonomie”, have been used by OEMs to evaluate vehicles' energy performance. However,

the load from the A/C system on the engine or on the energy storage system has not always been modeled in sufficient

detail. A transient A/C simulation tool incorporated into vehicle simulation models would also provide a tool for

developing more efficient A/C systems through a thorough consideration of the transient A/C system performance. The

dynamic system simulation software MATLAB/Simulink® is frequently used by vehicle controls engineers to develop

new and more efficient vehicle energy system controls. A MATLAB/Simulink-based transient A/C system simulation

model is easier to incorporate into MATLAB/Simulink-based vehicle simulation software; therefore, the availability of a

transient A/C system simulation tool developed in the MATLAB/Simulink platform is important.

NREL has recently developed an A/C simulation tool to address these needs. This paper describes in detail the

modeling methods used for this new simulation tool. Comparison with measured data is provided to demonstrate the

validity of the model. The agreement between simulation and measurement was shown to be good on both the component

and system level. The capabilities of the model are also demonstrated by the example of simulating the SC03 cycle.

CITATION: Kiss, T., Chaney, L. and Meyer, J., "A New Automotive Air Conditioning System Simulation Tool Developed

in MATLAB/Simulink," SAE Int. J. Passeng. Cars - Mech. Syst. 6(2):2013, doi:10.4271/2013-01-0850.

____________________________________

NREL/CP-5400-57488. Posted with permission.

Presented at the SAE 2013 World Congress and 

Exhibition
speed. The heat transfer in the heat exchangers is a very

complex process mainly addressed by complex correlations

based on measured data. These correlations sometimes reduce

the stability of the code. The effects of humidity are difficult

to account for, not only in the model but also for the

experimental data necessary to calibrate the code. A cabin

model is also needed to provide a realistic load on the

evaporator. The cabin model must consider all the major

pathways of heat transfer into the cabin, including solar and

convective loads from the environment, heat from the engine

compartment, and sensible and latent heat loads in the air

stream. Realistic control methods similar to ones actually

used in automotive A/C systems also have to be

implemented. The cycling of the compressor can introduce

quick transients that are sometimes difficult to handle by the

numerical solver. The simulation model also has to be fast

enough for the purpose of evaluating vehicle power

performance and/or the design of A/C systems and their

controls.

There are numerous challenges in developing a suitable

automotive A/C system simulation tool. Some examples of

previously developed non-commercial full system-level

simulation include [6,7,8]. However, to the best of our

knowledge, none of them is both developed in the widely

used dynamic system simulation platform MATLAB/

Simulink (which is helpful for controls engineers) and

available to the public at the same time. Commercially

available software tends to be very expensive, and typically,

their accuracy for predicting the transient processes is

difficult to assess based on available documentation. The

simulation model subject of this paper addresses these issues.

Also, with the model being built on the MATLAB/Simulink

platform, most of the inner workings of the model are visible

to the user, and the user has considerable freedom to modify

the model to better represent their specific A/C system

hardware and controls. Only the source codes for some of the

basic building blocks of the model remain inaccessible to the

user.

SELECTING THE MODELING

METHOD

Numerically, the most difficult part is modeling the twophase refrigerant flow circuit. A number of different methods

have been used in the past, and the selection of which method

to use was based on the intended purpose of the simulation

model.

With the transient nature of automotive A/C systems, the

authors thought it was most important that the modeling

method capture the transient processes accurately and

robustly, even if model execution speed was compromised.

The model should be robust for fast transients such as

compressor cycling. It should also accurately predict

refrigerant redistribution after shut-down and after start-up. It

has to conserve mass accurately during simulations of long

test cycles.

Another desirable feature of the model was that it should

be versatile enough that various complex heat transfer

correlations can be programmed into the main algorithm

relatively easily. Finally, the model formulation had to be one

that was well suited for the simulation modeling platform,

MATLAB/Simulink.

The authors decided to use the finite volume formulation

for calculating the refrigerant flow. The finite volume

formulation is well suited for accurate conservation of mass

momentum and energy. Also, in the finite volume

formulation, immediately after each step of the integrator, the

state variables and the velocity are readily available. Then,

heat transfer rates even with the more complex methods can

be calculated explicitly. Finally, Simulink is geared toward

solving ordinary differential equation systems and, in the

finite volume formulation, the problem is established as a set

of ordinary differential equations. Therefore, this formulation

is very well suited for Simulink.

Challenges were expected with the “stiff” nature of this

modeling method, especially in the condenser where pure

liquid is present.

DESCRIPTION OF THE MODEL

The transient A/C simulation model consists of two main

sub-models that include the cooling circuit model and the

cabin model. In the cooling circuit model, the larger volumes

containing refrigerant, such as the accumulator, the receiver/

dryer, and the headers of the heat exchangers, are modeled

with the zero-dimensional volume simulation block (0-D

volume block). The refrigerant pipes and the flat tubes /

plates of the heat exchangers are modeled with various

versions of the one-dimensional pipe simulation block (1-D

pipe block). The general structure of the model on the

refrigerant side is a network of 1-D pipe blocks connected to

each other with 0-D volume blocks.

Using the 0-D volume and the 1-D pipe blocks, models of

various multi-pass multi-row compact heat exchangers can be

created relatively easily. Models for other A/C system

components, such as the compressor and the thermostatic

expansion valve, were incorporated with the goal of

maintaining sufficient accuracy and acceptable execution

speed. The model currently uses R134a exclusively as the

refrigerant. However, other refrigerants can be used if the

user builds the required property tables in a specified text file

format.

In the following sections, the 0-D volume block, the 1-D

pipe block and various other modeling blocks are described

in detail.

The 0-D Volume Simulation Block

The 0-D volume blocks serve as models for real volumes

in the system as well as the connectors between the 1-D pipe

blocks. In the 0-D volume block, the sum of the incoming

refrigerant mass flow rates minus the sum of the outgoing

refrigerant mass flow rates determine the time derivative of

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
the refrigerant mass in the volume, and the sum of the

incoming refrigerant enthalpy flow rates minus the sum of the

outgoing refrigerant enthalpy flow rates plus the net heat

transfer to the volume determine the time derivative of the

internal energy in the volume. Note that when saturated

conditions exist in the 0-D volume block, saturated liquid,

saturated vapor, or a saturated mix can be selected for the

output flow, according to the system component represented

by the simulation block. An accumulator, for example, is

physically designed to only let out saturated vapor.

Since internal energy and mass are the state variables for

the simulation, all other material properties are looked up

from two-dimensional tables based on specific internal

energy and density. Such lookup tables provide the best

numerical performance as no iteration on properties is

required.

The 1-D Pipe Simulation Block

In all versions of the 1-D pipe simulation blocks, on the

refrigerant side, equations for conservation of mass,

momentum and energy are solved through a finite volume

method. As with the 0-D volume block, all refrigerant

material properties are obtained from two-dimensional tables

based on specific internal energy and density. The evaluation

of heat transfer between the pipe wall and the refrigerant, and

between the pipe wall and air is incorporated through local

heat transfer coefficient correlations. The effectiveness-NTU

method is applied on the air side, which ensures that the exit

air temperature does not overshoot the wall temperature. The

time derivative of the wall temperature is calculated from the

net heat flow rate to the wall. Therefore, the thermal capacity

of the wall has been accounted for. However, the temperature

drop across the pipe wall has not been accounted for. It was

determined that the introduced error is small for the relatively

thin walls of the flat tubes / plates found in typical compact

heat exchangers in an A/C system. In the direction of air

flow, the temperature of the wall is constant. From the

applied finite volume method, it is inherent that in the 1-D

pipe simulation block, refrigerant flow can take place in both

directions.

The simplest version of the 1-D pipe simulation block is

used to model the refrigerant lines connecting the various

A/C system components. Here a circular pipe is simulated in

a perpendicular cross-flow of air. For these connecting lines,

the effects of the relative humidity and condensation of water

on the pipe are neglected.

More complex versions of the 1-D pipe simulation block

are used for simulating the refrigerant and air flow and heat

exchange process for the flat tubes of the condenser and

plates of the evaporator. These 1-D pipe model block

versions include the options of multiple “parallel channels”

on the refrigerant side and more complex fin geometry on the

external air flow side. Humidity in the air and condensation

of water from air is accounted for. It is assumed that the

refrigerant flow properties inside the parallel channels are

identical. Also, the wall/fin temperatures in the direction of

the air flow are assumed to be constant. This is certainly an

approximation, but one necessary to maintain reasonable

simulation execution speed.

The finite volume formulation that is used for the

refrigerant flow is a conservative method in the sense that the

mass, momentum and energy are conserved very accurately.

For the air flow, there are no conservative terms and the flow

is described with purely algebraic equations.

Refrigerant-side equations

The general control volume equations for the conservation

of mass, momentum and energy as presented in [9] are used

as a starting point. The general control volume equations are

then written for one dimension, as all flow variables are

assumed to change only in the direction of refrigerant flow,

and for finite volumes, that is small volumes, over which the

flow variables can be considered uniform. Note that the

refrigerant is in thermodynamic equilibrium in the finite

volumes, and velocity “slip” between the liquid and the vapor

phase is not factored in. The refrigerant is also considered

homogeneous, that is, all flow properties, including liquid

and vapor phase volume ratios, are the same everywhere

within the finite volume. Finally, the effects of gravity are

neglected.

Conservation of mass

We start out from the conservation of mass ([9], Eq.

3.3.1):

(1)

where ρ is the refrigerant density, is the velocity vector, ∫cv

dV is the volume integral and is the surface integral.

The first integral on the right is the mass in the finite volume,

and then for the one dimensional flow for the finite volume:

(2)

where A is the pipe cross sectional area, and the in / out

subscripts are for inlet and outlet boundaries of the finite

volume, defined also as the left-side and right-side

boundaries, respectively, as we use the convention that the

positive flow is from left to right.

Conservation of momentum

The conservation of momentum written for a control

volume ([9], Eq.3.3.8):

(3)

where ΣF is the sum of all forces including shear and

pressure forces on the control volume. We define the first

integral on the right side as the linear momentum, I. For finite

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
volume formulation for one-dimensional flow the following

can be written:

(4)

where pin and pout are the pressures on the inlet and outlet

boundary, respectively, and Fwf is the force from wall

friction. For the wall friction force, a version of the DarcyWeisbach formula ([9], Eq.5.8.7) was used in which the wall

friction factor was obtained with the Hagen-Poisseuille

equation ([9], Eq. 5.10.12) for laminar flow and from a

modified version of the Colebrook equation ([9], Eq. 5.10.13)

for turbulent flow. This latter equation does take into account

the effect of the relative roughness of the pipe wall. These

equations are used for both single and two-phase flow. In the

two-phase region, the viscosity was obtained as the quality

weighted average of the saturated liquid and saturated vapor

viscosity. A user-adjustable calibration coefficient was also

implemented.

Conservation of energy

The energy equation written for a control volume ([9], Eq.

3.3.6):

(5)

where is the rate of heat addition, is the rate of work

done by the system via shear forces, and e is the total energy

per unit mass: , where u is the specific internal

energy and ν is the velocity. Note that all components other

than the internal and kinetic energy have been neglected. This

equation also assumes no other boundary work than that on

the inflow and outflow surfaces. We define E as the first

integral on the right side, which is the total energy in the

finite volume. Then, assuming 1-D flow, no shear force work,

and heat addition strictly through the pipe wall (no

conductive heat transfer through inflow/outflow boundaries),

the following equation can be written for each control volume

in the pipe (also will be called as a “segment” of the pipe):

(6)

where Qwr is the heat transfer rate from the pipe wall to the

refrigerant flowing inside the pipe segment. Note that Qwr

can be written as

(7)

where is the average heat transfer coefficient over the pipe

segment, Ai is the inside area of the pipe segment wall, and

Tw is the pipe segment average wall temperature. To calculate

, the Dittus-Boelter equation ([10], Eq. 8.58) and/or the

Chen correlation [11] is used. Note that the transition points

between the single-phase and two-phase regions along the

length of the pipe need to be determined for the proper

application of the heat transfer rate correlations. These

transition points are easily obtained from the mass and total

energy, which are simulation state variables and therefore

available in the finite volumes at the start of each time step.

Spatial discretization

The spatial discretization was implemented as shown in

Fig. 1. Note that a “staggered” grid was used. While the

control volumes were defined for mass and energy as the

volumes shown between the 0-1, 1-2, 2-3, ... location indexes

(Type 1 finite volumes), for the linear momentum they were

defined as the volumes between the 0-0a, 0a-1a, 1a-2a, 2a-3a,

... location indexes (Type 2 finite volumes). So in fact there

are two sets of finite volumes, one set for the mass and

energy equations, and one set for the momentum equations.

This staggered spatial discretization method had to be used to

avoid instability problems. Other authors have also used this

method [12], perhaps for the same reason. Note that the first

and last Type 2 finite volumes (finite volumes for linear

momentum) are “half” size compared to the rest of the ones

in the middle of the pipe.

Fig. 1. Spatial resolution for the finite volume

formulation

To implement the conservation equations, the variables

ρin, ρout, pin, pout, νin, νout, uin and uout (the density, pressure,

velocity and specific internal energy on the finite volume

boundaries, respectively) have to be obtained from the

conserved variables m, I and E (the mass, the momentum and

energy, in the finite volumes, respectively). The conserved

variables are also the simulation state variables that are

available immediately after the integrator advances one time

step. From the definition of these variables:

(8)

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
(9)

(10)

(11)

Then a simple first-order interpolation of the flow

variables on the finite volume boundaries was implemented.

For example, for the conservation of mass, the density and

velocity on the left-side boundary of the i

th Type 1 finite

volume is estimated as

(12)

(13)

and for the conservation of momentum equation, the density

and velocity on the left-side boundary of the i

th Type 2 finite

boundary are estimated as

(14)

(15)

Note how the interpolations are different between

conservation of mass and conservation of linear momentum

due to the staggered Type 1 and Type 2 finite volumes.

Applying the boundary conditions

In this section we discuss the boundary conditions of the

overall computational domain, that is, the left-side boundary

of the first finite volume and the right-side boundary of the

last finite volume. Flow variables on these surfaces cannot be

calculated the same way as on the boundaries between two

finite volumes because they are attached to 0-D volume

blocks. The boundary conditions have to be treated

differently depending on whether inflow or outflow was

present on the boundary, determined by the sign of the linear

momentum, I. For the inflow, conditions from the connected

0-D volume block could be applied readily to obtain the

boundary conditions. Calculating the outflow boundary

conditions is more challenging as there are various ways of

extrapolating flow variables to the boundary and some of

these methods did not work well regarding stability, accuracy

or speed. In the end, the following method was found to work

well. To calculate the boundary velocity, the linear

momentum in the Type 2 boundary finite volume (available

as state variable) was divided by one-half the mass in the

Type 1 boundary finite volume. The boundary pressure for

outflow was simply the pressure in the connected 0-D

volume. The specific internal energy on the boundary was

extrapolated linearly from the first two regular Type 1 finite

volumes next to the boundary. Finally, once the boundary

internal energy and pressure were known, the boundary

density could be calculated through an iterative process using

the pressure vs. specific internal energy and density tables.

Air-side equations

The heat transfer rate from the air flow to the wall of a

pipe segment can be written as

(16)

where is the average air-to-wall heat transfer coefficient in

the pipe segment, Tw is the pipe segment average wall

temperature, and Atot is the total surface available for heat

transfer on the air side in the pipe segment.

The air-side equations are different for the various

versions of the 1-D pipe model blocks. For the transport

refrigerant pipes, the correlation for heat transfer on a pipe in

a perpendicular cross-flow of air was implemented according

to Eq. 7.53 of [10]. For air-side heat exchange on the finned

heat exchangers, the Chang correlation [13] was

implemented. It is noted that although the Chang correlation

did not incorporate the effects of humidity and condensation,

we still apply this correlation for moist air.

Chang proposed a detailed and a simplified correlation,

where the local heat transfer coefficient is calculated by using

a correlation for the Colburn j-factor, j. The simplified

correlation is

(17)

where ReLp is the Reynolds number based on the louver

pitch, Lp (see Fig. 2). The more complex version of the

Chang correlation incorporates a dependency on louver angle,

fin length, tube depth, louver length, tube pitch and fin

thickness. The model user can switch between the simplified

and more detailed correlation versions. The details of how the

heat transfer coefficient can be obtained from the Colburn jfactor can be found in [10] p.534.

Fig. 2. Louver pitch used in the Chang correlation

The local heat transfer coefficient, ha obtained by either

the simpler or the more complex Chang correlation has to be

adjusted to get an average heat transfer coefficient, by

incorporating the effects of the fins not being able to maintain

the same temperature as the flat tube itself

(18)

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
where η0 is the fin effectiveness, which can be written as

(19)

Here Atot is the total area for heat transfer, Af

 is the finned

area available for heat transfer and ηf is the fin efficiency.

The fin efficiency for the straight fins between two flat

tubes / plates can be expressed as ([10], Eq.11.4):

(20)

where L is the half fin length, m = (2h/kt), k is the thermal

conductivity of the fin material, and t is the fin material

thickness.

With that, the equation (16) can be evaluated, but it will

be accurate only as long as the temperature change of the air

across the flat tube is small relative to the difference between

the incoming air temperature and the flat tube wall

temperature. In general that cannot be assumed and therefore

the effectiveness-NTU method is used as applied to a parallel

flow heat exchanger ([10], Eq.11.29a):

(21)

where Cr is the ratio of the lower and the higher heat capacity

rates:

(22)

where the heat capacity rate is the mass flow rate of the

medium participating in the heat transfer times its constant

pressure specific heat. Because the wall temperature is

constant, the refrigerant side can be represented by infinitely

large heat capacity flow and heat transfer rate that ensures

constant metal temperature along the air flow direction.

Therefore, in our special case Cr approaches zero, and the

above equation simplifies to

(23)

Furthermore, assume that the heat transfer takes place

from the air to the flat tube / plate wall. Then

(24)

can be written ([10], Eq. 11.26), where the subscripts a, w, i

and o mean air, wall, in and out, respectively. Finally, per

[10], Eq. 11.25,

(25)

where is the air mass flow rate and Cp,a is the constant

pressure specific heat for the incoming “wet” air, and after U

was replaced with justified by an infinitely large heat

capacity flow rate assumed on the refrigerant side. Also, it

can be easily shown that

(26)

where Cp,adry and Cp,w are the constant pressure specific

heats for dry air, and water vapor, respectively, and ω is the

absolute humidity. Then, from the above three equations, Ta,o

can be expressed as

(27)

The air-to-wall heat transfer rate now can be obtained as

(28)

where is the mass flow rate of the water vapor carried by

the wet air. The result would be the same if we had assumed

the heat transfer to take place from the flat tube to the air.

At this point the issue of condensation needs to be

addressed. Water vapor condensation in the air flowing

through the evaporator has a significant effect on the

temperature and relative humidity of the wet air leaving the

evaporator; therefore, it needs to be accounted for. In the

condenser, there is no water condensation, but the relative

humidity still has some effect on the results. The same airside model is used for both evaporator and condenser. When

used for the condenser or for an evaporator seeing low

humidity incoming air, the majority of condensation-related

calculations do not get executed; therefore, the performance

penalty is insignificant compared to using a dedicated noncondensation pipe model.

Mass flow rate, temperature, and the relative humidity of

the incoming wet air for each pipe segment are input

variables. The air flow velocity is calculated using the

minimum area for air flow inside the heat exchanger, which

is an input parameter.

The air-side heat transfer equations are solved for each

segment of the pipe. First, the heat transfer rate is calculated

as it would be without condensation. The wet air enthalpy is

reduced using the heat transfer rate and an air-out

temperature is calculated. The partial pressure of the water

vapor in the exit wet air is calculated with the exit pressure,

exit temperature and the incoming relative humidity. The

saturated water vapor pressure is also calculated for the exit

air temperature. If the saturated water vapor pressure is

higher than the exit air water vapor partial pressure, then

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
there is no condensation. In this case all previously calculated

properties will be valid, and the calculation can proceed to

the next pipe segment. If the saturated water vapor pressure is

lower than the exit air water vapor partial pressure,

condensation of water does take place, and the flow

properties have to be recalculated for the pipe segment

considering condensation. The algorithm for condensation

conditions assumes that the exiting atmospheric air is

saturated with water vapor, and this exit air-vapor mix is at

the same temperature as the condensed water. The algorithm

then invokes an iteration on the exit wet air temperature,

while the heat transfer to the heat exchanger wall is still

assumed to be the same as it would be without condensation.

It is not known to the authors how much error this

assumption introduces. Other treatment of condensation

would involve much more complicated heat transfer

equations that incorporate the presence of a liquid water layer

over the heat exchanger surfaces.

Once the exit wet air temperature is obtained, all other air

flow properties can easily be calculated for the current pipe

segment. Next, the same process will be applied to all the

remaining pipe segments.

Parallel channels

In order to model a typical condenser configuration,

multiple “parallel” channels for refrigerant flow are allowed

in a 1-D pipe block. These channels are identical in terms of

refrigerant flow. Because the wall temperature is assumed

constant within a pipe segment, the wall-to-refrigerant heat

transfer rate is also identical in the parallel channels.

Therefore, the refrigerant in and out mass flow rates and the

wall-to-refrigerant heat transfer rates for a single parallel

channel are simply multiplied by the number of parallel

channels in the flat tube to get the aggregate numbers for the

entire flat tube.

Coupling the air and refrigerant sides

The air and refrigerant-side equations can be solved on

their own separately, because the two sets of equations are

not algebraically coupled. The pipe wall temperature, Tw,

appears in both sets of equations, but it is a simulation state

variable, which means its value is obtained as a result of an

integration step, not from algebraic equations. Therefore, it is

available at the beginning of each time step to calculate the

heat transfer rates from the air to the pipe wall and from the

pipe wall to the refrigerant.

It is assumed that the thermal resistance of the wall is

zero. In other words, the inner and outer surfaces of the pipe

wall are at the same temperature. This is typically a good

approximation for compact heat exchangers as they use thin

walls.

The equation for the wall temperature comes from the

conservation of energy, which states that the net heat flux

into the wall segment is stored as thermal energy in the wall

segment:

(29)

where Qaw is the heat transfer rate from air to the wall, Qwr is

the heat transfer rate from the wall to the refrigerant, Qx is the

heat transfer rate in the pipe wall in the refrigerant flow

direction, Cpw is the wall material specific heat, and Δm is the

mass of the wall segment including all the fins. The 

term represents the imbalance in conductive heat flow rates

from the neighboring wall segments. This equation is written

for each pipe segment, and the wall temperature is obtained

for each pipe segment.

Heat Exchangers

Currently, the typical heat exchangers in automotive A/C

systems are compact heat exchangers with a general structure

that can be described as a number of headers, with “passes”

between these headers. Nearly all of the heat transfer takes

place in the passes. Using conventional terminology, one pass

in the condenser consists of a number of flat tubes and one

pass in the evaporator consists of a number of plates.

Two versions of the above described 1-D pipe model

simulation block, with air-side heat transfer according to the

Chang's model, are the basic building block for the heat

exchangers in the A/C system model. For the version used to

model the flat tubes of the condenser, the Dittus-Boelter

equation is used on the refrigerant side along the full length

of the pass, even across the phase boundaries. In the saturated

mix region, quality weighted average of the saturated liquid

and saturated vapor properties of the refrigerant are used.

This simple model provided a good match with measurement.

For the version used to model the plates of the evaporator, on

the refrigerant side, the Dittus-Boelter equation [10] is used

for the superheat region and the Chen correlation [11] is used

for the two-phase region.

In the following paragraphs we use the example of an

evaporator; therefore, we will use the term “plates” but the

same could be said about the condenser, using the “flat tubes”

terminology. With the help of the 0-D volume and 1-D pipe

simulation blocks, multiple row and multiple pass heat

exchangers can be built relatively easily. The simplest

approach is to have one 0-D volume block for each of the

headers and connect them with one 1-D pipe model block,

each representing one pass. In order to account for the

number of plates in each pass, the mass flow rate and heat

transfer outputs have to be multiplied by the number of plates

in the pass before being routed into the 0-D volume blocks

representing the headers. Therefore, in this approach all the

plates in a given pass are treated as identical in terms of their

flow and heat transfer. A sub-version of this approach is

when the plates in a pass are split up into “sub-passes”

depending on which pass is upstream of them in terms of

airflow. Each of these sub-passes is then represented with one

plate model, and mass flow and heat transfer through a subKiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
pass are once again obtained by mass flow and heat transfer

through one plate multiplied by the number of plates in the

sub-pass. An example of how this works is shown in Figs. 3a

and 3b for the case of a two-row, two-passes-per-row

evaporator for which one of the passes, pass 2, gets the

airflow from two different upstream passes. As explained

above, this pass is then split up into pass 2a and pass 2b,

which receive their air flow from passes 3 and 4, respectively.

This is the configuration of the evaporator in the current

model implementation.

Fig. 3a. Schematic of the implemented evaporator

showing air flow dependency of passes

Fig. 3b. The “exploded view” schematic of the

implemented evaporator showing the headers

A more rigorous way to build a compact heat exchanger

model would be to represent each plate with a dedicated 1-D

pipe simulation block. Accuracy would be expected to

increase, but the much increased simulation execution time

reduces the practicality of this approach.

Routing the airflow is also relatively easy with the applied

modeling methodology. The segment-wise exit air flow

variables from a plate are passed as an output vector from the

1-D pipe simulation block representing the plate. This vector

can be directly fed into the inlet air flow variables input port

of the simulation block representing another plate in another

downstream row/pass. For some cases this is done with a

direct connection, for example, when the plates in the

upstream row and in the downstream row are aligned and the

refrigerant flow direction is the same in them (see pass 3 air

exit flow to pass 1 air inlet flow). In other cases, the order of

the passed air flow variables vector needs to be reversed (see

pass 3 air exit flow to pass 2a air inlet flow).

Alternatively, the exit air flow output vector can be fed

through another simulation block, the “mixing” block that

calculates the flow properties of the mixed out stream. This

option can be used for the last row of the heat exchangers

where the airflow from the plate is also exiting the whole heat

exchanger. The variables of the mixed out-flow are calculated

from the variables of the individual pipe segment exit flows

based on the conservation of mass and energy. Additional

condensation may take place during the mixing, and it is

accounted for in the calculation. The mixing block can also

be used when the plates do not obviously align or different

numbers of pipe segments are desired to be used in the

different passes due to numerical stability considerations.

Thermostatic Expansion Valve

The expansion device implemented in the model is an

externally balanced thermostatic expansion valve (TXV).

Delayed response of the bulb temperature to changes in the

evaporator exit temperature is achieved with a first-order

delay simulation block. The characteristic time of this

response is an input parameter to the model. The bulb

pressure is the saturated pressure of the refrigerant at the bulb

temperature.

This response is the only dynamically modeled detail.

Otherwise, the position of the valve ball is determined from

static force balance. The moving masses inside the TXV are

so small that the characteristic time of the valve ball position

response to changing pressures is very small. Some wave

phenomenon may make a difference here; however, the

model would not be very efficient if such detail is included -

the simulation time step would have to be very small. The

input parameters that play into the TXV static force balance

calculation include the spring preload, the spring rate, the ball

diameter, ball seat angle, actuating pin diameter, and the bulb

actuator diameter. Furthermore, the ball stroke limits the

maximum opening area and can be adjusted for a given ton

rating of the TXV.

Once the flow area through the flow restriction device of

the valve is known, the refrigerant flow rate is calculated

from the two-phase orifice flow equations. In addition to the

flow area, the upstream conditions and the downstream

pressure are used in this calculation. First the model

determines the critical pressure, p*, and critical unit area

mass flow rate, q*, from lookup tables, based on upstream

pressure, pu, and enthalpy, hu:

(30)

(31)

These lookup tables were generated for R134a from other

property tables with the conditions that the flow is isentropic

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
between the inlet and the throat, and that the Mach number

equals one in the throat for choked flow. Then the calculation

of the mass flow rate, q, depends on whether the downstream

pressure, pd, is lower or higher than the critical pressure. If pd

≤ p* (choked flow), the mass flow rate, q, is

(32)

where Ax is the flow area in the throat and Cd is the discharge

coefficient. On the other hand, if pd > p* (non-choked flow),

the mass flow rate is calculated from the conditions that the

flow is isentropic between the inlet and the throat, and that

the enthalpy in the throat is the enthalpy at the inlet minus the

flow kinetic energy at the throat (the steady state flow version

of the energy equation with zero inlet kinetic energy

assumption).

The equations implemented in the orifice model are valid

for adiabatic one-dimensional homogeneous equilibrium twophase flow. However, adjustment was made to approximate

some non-equilibrium effect. Data measured for short orifices

was used for this purpose [14]. A discharge coefficient was

determined as the ratio of the actual mass flow rate to the

mass flow rate predicted by the above equilibrium calculation

method. It was noticed that the discharge coefficient was a

function of how far the upstream conditions deviated from

saturated liquid conditions. Therefore, the discharge

coefficient can be set as a function of pr, the ratio of the

upstream pressure and the saturated liquid pressure at

upstream enthalpy:

(33)

The success of this approach is demonstrated in Fig. 4a in

which 40 short-orifice R134a flow data points with various

upstream conditions and downstream pressures were plotted

on the p-h space, and the calculated discharge coefficients for

each data point were plotted as a function of the variable pr in

Fig. 4b. The correlation fit line that was developed and

implemented in the model is shown in Fig. 4b. The orifice for

this correlation was 1.22 mm in diameter and 13.1 mm long,

and arguably quite different in shape from the inner details of

the flow path in a TXV. However, there is a reasonable

freedom built into the input parameter list of the TXV

simulation block to adjust the shape of the Cd(pr) curve for

TXV data that the user may have available.

Note that with proper selection of input parameter values,

the TXV model can easily be reduced to an orifice tube

model. No modification to the Simulink model is needed for

this purpose.

Fig. 4a. Short-orifice data points on p-H space

Fig. 4b. Calculated Cd plotted against the variable Pr

Compressor

The compressor is a constant volume variable speed

displacement device. The rotational speed and the

displacement per revolution (both input parameters)

determine the ideal forwarded volume per second. Actual

forwarded volume per second is then obtained with the

application of a volumetric efficiency. Upstream conditions

and downstream pressure are input. The downstream enthalpy

is calculated with the help of an isentropic efficiency ([15],

Eq. 6-62).

Both the volumetric and the isentropic efficiencies are

functions of the compressor speed and the downstream-toupstream pressure ratios. These tables are input to the model.

There are two versions of the model, one for a mechanical

and one for an electric drive compressor. The compressor

efficiency tables are different for these model versions. For

the mechanical drive compressor version, efficiency tables

typical of a piston compressor were used. For the electric

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
drive compressor version, efficiency tables typical of a scroll

compressor were used.

Compressor cycling is accounted for, and there are certain

controls implemented for that in the model (see Controls).

These controls are different for mechanical and electric drive

compressors.

Variable displacement compressors are gaining ground in

mechanical drive A/C systems as they have an additional

degree of freedom in setting the refrigerant mass flow rate

compared with a constant displacement device. This

additional control reduces the need for the inefficient cycling

of the compressor. It is easy for the model user to modify this

compressor model to represent a variable displacement

device. At this time, the effects of lubricating oil on the

compressor - or the system as a whole - have not been

accounted for.

Cabin Model

The cooling system model is enhanced with a cabin

model. The purpose of the cabin model is to provide a

reasonably accurate estimate of the cabin conditions that can

serve as the boundary conditions for the cooling circuit

model. The cabin air is represented with a zero-dimensional

lump-sum air / water vapor mix volume. The cabin shell and

interior thermal masses are included. Heat transfer between

the thermal masses, the cabin air, and the ambient are

accounted for, as well as the solar energy absorption by each

thermal mass. The thermal masses and the heat transfer paths

that are included in the model are shown in Fig. 5.

Fig. 5. Schematic of the cabin model

The thermal masses are shown in the rounded boxes, and

the heat transfer paths are shown with solid arrows. All body

air leak flow rates are lumped together and calculated as an

adjustable constant times the pressure differential between

the cabin and the ambient. The cabin model schematic is

shown with the air passages for fresh and recirculated air.

Controls

Basic electronic controls have been implemented in the

model. Shut-off of the compressor due to downstream

pressure passing above a high pressure limit, or upstream

pressure passing below a low pressure limit is included.

Limits on how long the compressor has to stay off after each

shut-down is also implemented. The cabin air recirculation

rate is set based on whether the cabin temperature exceeds the

outside temperature or not. Alternatively, the recirculation

rate can be set constant over the entire simulation. These

basic controls are implemented for both the mechanical and

the electric drive compressor versions of the model.

In addition, for the model version with the mechanical

drive compressor, the compressor is cycled on or off

depending on whether the cabin temperature is below or

above the target temperature and whether the evaporator-out

air temperature is below or above a set target temperature (to

prevent freezing). A temperature dead band is also

implemented for both to reduce the frequency at which the

cycling occurs. The evaporator blower speed is set to one of a

number of settings at the beginning of the simulation and

remains constant throughout the simulation.

On the other hand, in the model version with the electric

drive compressor, where the compressor speed can be

controlled independently of the vehicle's engine speed, the

compressor speed is adjusted based on whether the

evaporator wall temperature is above or below a set target in

a region just upstream of where superheat is expected in the

evaporator. This way, freezing of the evaporator can be

avoided most of the time without having to cycle the

compressor on and off. Unlike in the model with the

mechanical drive compressor, the blower flow is adjusted

based on the cabin temperature relative to the set cabin target

temperature. There is an additional compressor cycling

algorithm: if the controller would otherwise command

bringing down the compressor RPM below a minimum, the

compressor will cycle off. When the compressor restarts, it

restarts at that same minimum RPM. This is done so that the

compressor does not operate in the inefficient low RPM

regime.

These controls are easily modified and additional control

algorithms can be added by the user given the block

simulation environment of Simulink. In fact, one of the best

applications of this model may be the development of control

systems for optimum A/C system performance and

efficiency. The fact that the model handles compressor shutdown and start-up transients robustly and at least in theory

accurately, is an important feature of the model in this regard.

Model Performance

During development, it was an important goal that the

model would run at an execution speed that is practical for

the development of A/C systems and in the evaluation of

vehicle power management, for example, when used in cosimulation with Autonomie. The execution speed is

dominated by the small time step required by the model to

run successfully and to be free of unrealistic oscillations. In

that regard, a 2e-5 sec simulation time step was found to be

the best for a typical automotive A/C model, and with that,

the execution speed is on the order of 10 times that of real

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
time speed on a 64 bit Windows computer. It is certainly

desirable to improve the execution speed, and current efforts

are aimed to create versions of the model that trade some of

the transient simulation accuracy for a much improved

execution speed.

Execution speed is also dependent on the number of

segments in the heat exchanger flat tubes or plates. In the

current model, 18 segments for the condenser flat tubes and

10 segments for the evaporator plates were used. Fewer

segments increase simulation speed due to both fewer

operations per time step and the possibility of using larger

time step, but at a cost of reduced accuracy.

Another measure of performance is how well the

conservative variables are preserved. This is important

because when highly transient processes for long simulation

times are modeled a loss of refrigerant mass due to numerical

inaccuracies may be experienced leading to unacceptable

errors. The finite volume formulation implemented in the

model preserves mass and energy of the refrigerant with high

accuracy. Evaluating mass and energy conservation on the

system level is relatively easily done and it provides a good

check on the error free implementation of the governing

equations. Momentum is lost as refrigerant enters the 0-D

volumes from the 1-D pipes, so it is not usable in this regard

on the system level.

To demonstrate the conservativeness of the model, data

obtained with the mechanical drive compressor version for

the 600 second long SC03 cycle were used (see the ‘Results’

section). To check on conservation of mass, the total

refrigerant mass in the system was summed at the beginning

and at the end, and the difference was less than 0.001 %. A

similar check on the conservation of energy was also carried

out. According to the First Law of Thermodynamics, the final

total energy in the refrigerant must equal the initial total

energy in the refrigerant plus the total energy transferred to

the refrigerant via heat transfer and by the compressor work.

The deviation, once again, turned out to be less than 0.001%.

VERIFICATION AND RESULTS

System Level Verification

Measured data for calibration and verification are not easy

to obtain as they are guarded by the component

manufacturers. However, some limited calibration and

verification of the model with the use of steady state

performance data on an actual light-duty A/C system have

been carried out. Because the heat exchanger air inflow

parameters were known and they were constant in time, the

cabin model was essentially deactivated.

The data set used for verification included 22 steady-state

operating points. The bench data included pressures and

temperatures along the entire refrigerant circuit, properties of

the upstream and downstream air streams on the heat

exchangers, and the compressor speed and mass flow rate

data. The evaporator superheat was measured, and the TXV

model was set up for this nominal superheat. The internal

details of the TXV used in the tests were not known, but did

not matter for the calibration of the model for the rest of the

system.

From the measured compressor mass flow rate and from

the inlet and outlet refrigerant thermodynamic properties, the

volumetric and isentropic efficiencies of the compressor were

calculated as a function of compressor RPM and pressure

ratio. When the simulation was carried out, the compressor

RPM was set to the measured one. For a properly verified and

calibrated model, the compressor performance had to

converge to the measured pressure ratio and mass flow rate.

Showing a good match between measured and predicted

refrigerant mass flow rates over a significant range of

operating points also verifies that it is acceptable to use

volumetric and the isentropic efficiencies as functions of

RPM and pressure ratio.

Uncertainty analysis for the measurements was not

available; however, data on reproducibility provided a

measure of the integrity of the data. The relative deviation of

results between an original and a repeated test, averaged for

six operating points, were 1.0%, 3.2% and 1.1%, for

evaporator heat transfer, compressor power and refrigerant

mass flow rate, respectively. A source of inaccuracy in the

validation process was the fact that lube oil was present in the

measured system but was not accounted for in the simulation.

Since the actual geometry of the transport pipes

connecting the A/C components were not available, the

model version used for the verification did not include the

simulation blocks for the transport pipes. According to the

measured data, refrigerant property changes over most

transport pipes could be neglected except for the pipe

connecting the evaporator outlet and the compressor inlet.

The effect of the losses in this pipe on the system

performance was simply represented by a pressure/enthalpy

change simulation block between the evaporator refrigerant

outlet and the compressor inlet.

With these constraints, all 22 measurement points were

simulated, and some calibration was done on the heat transfer

correlation coefficients. Note that the need for calibration is

especially justified given that the evaporator plates utilized

“dimples” to enhance the heat transfer rate and on the air side

the presence of condensation most likely significantly altered

the heat transfer rates compared to the Chang correlation. In

Figs. 6.a and 6.b, the thermodynamic cycles for measurement

points 4 and 6, respectively, are shown on the pressureenthalpy space. These two points are representative of the full

22-point series in terms of the quality of the match, and the

match between the simulation and the measurement is seen as

quite good.

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
Fig. 6a. Thermodynamic cycle for Test Point 4

Fig. 6b. Thermodynamic cycle for Test Point 6

Component Level Verification

Four pieces of component data were also derived from

each of the full cycle results. These include the refrigerant

flow rate (constant through the system for the steady state

points), the refrigerant-side heat transfer rate on the

condenser, the refrigerant-side heat transfer rate on the

evaporator, and the evaporator-out air temperature. The

measured and simulated data for these four properties are

included in Figs. 7a, 7b, 7c and 7d, respectively.

Fig. 7a. Predicted and measured compressor mass flow

rate

Fig. 7b. Predicted and measured condenser heat

exchange rate

Fig. 7c. Predicted and measured evaporator heat

exchange rate

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
Fig. 7d. Predicted and measured evaporator air-out

temperature

Figs. 7a, 7b, 7c and 7d show that a good match between

the measured and the simulated data was achieved. The

average errors were 3.1%, 1.4%, 2.2% and 0.7°C,

respectively.

Results

The SC03 cycle was simulated with both the mechanical

and the electric drive compressor versions of the model. The

models were set up for the same system geometry and initial

conditions. The results for the mechanical drive compressor

are shown in Figs. 8a, b, c and d, and the results for the

electric drive compressor are shown in Figs. 9 a, b, c and d.

Fig. 8a. Compressor and drive speed, mechanical drive,

SC03 cycle

Fig. 8b. Compressor power and heat transfer rates

mechanical drive, SC03 cycle

Fig. 8c. Evaporator air-out temperature with compressor

switch limits and control signal, mechanical drive, SC03

cycle

Fig. 8d. Cabin temperatures with compressor switch

limits and control signal, mechanical drive, SC03 cycle

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
Note that for the mechanical drive compressor, the

compressor speed is either a pulley ratio times the drive

speed, when the compressor is on, or zero, when the

compressor is off. In Fig. 8c, the “Compr. On Level” is the

temperature that the evaporator exit air temperature has to

pass in the upward direction for a “compressor on” trigger,

and the “Compr. Off Level” is a temperature slightly lower

that the evaporator exit air temperature has to pass in the

downward direction for a compressor off trigger. In Fig. 8d,

the “Compr. On Level” is a temperature that the cabin air

temperature has to pass in the upward direction for a

“compressor on” trigger and the “Compr. Off Level” is a

temperature that the cabin air temperature has to pass in the

downward direction for a “compressor off” trigger.

Results for the SC03 cycle with the electric drive

compressor A/C model (shown in Figs. 9a,9b,9c,9d) are

presented slightly differently as the controls are different.

Note the lack of compressor cycling, which is achieved by a

compressor speed that is independent of the engine speed.

The evaporator wall temperature target was set at 3°C, and

the minimum for this temperature turned out to be 1.7°C. The

smooth compressor speed trace allows for significantly

reduced transient spikes in most variables compared to the

mechanical drive compressor A/C model. For a different

problem setup in which the target cabin temperature could be

achieved with much lower average evaporator load,

compressor cycling would be taking place as the compressor

speed would be driven down to below the minimum allowed

speed by the controller.

Fig. 9a. Vehicle velocity and scaled compressor speed.

electric drive, SC03 cycle

Fig. 9b. Compressor shaft power and heat transfer rates,

electric drive, SC03 cycle

Fig. 9c. Evaporator temperatures, electric drive, SC03

cycle

Fig. 9d. Ambient and cabin temperatures, electric drive,

SC03 cycle

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)
CONCLUSIONS

A new automotive A/C system simulation tool developed

on the MATLAB/Simulink platform has been described. The

model consists of a detailed cooling circuit model and a

relatively simple cabin model. The governing equations for

the key system simulation blocks have been provided. A

finite volume formulation of the governing equations was

used on the refrigerant side, which provided a very accurate

preservation of refrigerant mass and a very accurate energy

balance. The model can handle the fast transients that occur

in an automotive A/C system.

Comparison of simulated data with test data for a set of 22

steady state test points shows good agreement between

simulation and measurement. For the refrigerant mass flow

rate, the evaporator load, the condenser load, and the

evaporator-out air temperature, the average errors were 3.1%,

1.4%, 2.2% and 0.7°C, respectively. Two main versions of

the model exist at this time, one version with a mechanical

drive compressor and the other with an electric drive

compressor model. The main difference between the two

versions is the method of electronic controls, and some

results for both versions have been presented for the SC03

cycle. This model is well suited for co-simulation with

vehicle system analysis software and for development of A/C

system controls for optimized system performance. The

model is also expected to be a useful tool for designing

automotive A/C systems, although such use has not been

demonstrated yet.

As a final note, the U.S. Environmental Protection

Agency and the National Highway Traffic Safety

Administration have recently published greenhouse gas

(GHG) emission and fuel consumption regulations for 2012 -

2016 and for 2017 - 2025 [16, 17]. These regulations include

the acknowledgement that A/C fuel consumption contributes

significantly to overall vehicle GHG emissions. In an attempt

to reduce this amount, a credit scheme is being implemented

so that auto makers that employ highly efficient A/C

components are awarded GHG credits. While not an official

tool, the model presented in this paper can be useful for

evaluating the reduction in GHG emissions over any drive

cycle that results from using more efficient air conditioning

components. In this way the model can both aid regulators

define credit amounts and assist car manufacturers in

deciding among competing technologies.

REFERENCES

1. Rugh, J. P., Hoveland, V., and Andersen, S. O. “Significant Fuel

Savings and Emission Reductions by Improving Vehicle Air

Conditioning,” Earth Technologies Forum/Mobile Air Conditioning

Summit, 2004.

2. Umezu, K., and Noyama, H., “Air-Conditioning System for Electric

Vehicles (i-MiEV),” SAE Automotive Alternate Refrigerant Systems

Symposium, 2010.

3. National Renewable Energy Laboratory, Vehicle Technologies Program

2007 Annual Report, pp. 145.

4. Stodolsky, F., Gaines, L., and Vyas, A. Analysis of Technology Options

to Reduce the Fuel Consumption of Idling Trucks. Argonne National

Laboratory, ANL/ESD-43, June 2000

5. Computer Software “Autonomie”, www.autonomie.org

6. Hemami, T. L., “Development of a Transient System Model of Mobile

Air-Conditioning Systems,” ACRC Technical Report - 143, September

1998.

7. Cullimore, B. A., and Hendricks, T. J., “Design and Transient

Simulation of Vehicle Air Conditioning Systems,” VTMS 5

2001-01-1692

8. Anand, G., Mahajan, M., Jain, N., Maniam, B. et al., “e-Thermal:

Automobile Air-Conditioning Module,” SAE Technical Paper

2004-01-1509, 2004, doi:10.4271/2004-01-1509.

9. Streeter, V.L., and Wylie, E.B., “Fluid Mechanics” 7th edition,

McGraw-Hill, 1979.

10. Incropera, F., P., and DeWitt, D.P., “Fundamentals of Heat and Mass

Transfer,” 2nd edition, 1985, John Wiley and Sons, Inc.

11. Chen, J.C., “Correlation for Boiling Heat Transfer to Saturated Fluids in

Convective Flow,” I&EC Process Design and Development, Vol. 5. No.

3 July 1966 pp.322 - 329.

12. Morales-Ruiz, S., Rigola, J., Perez-Segarra, C.D., and GarciaValladeres, O., “Numerical Analysis of Two-Phase Flow in Condensers

and Evaporators with Special Emphasis on Single-Phase/Two-Phase

Transition Zones,” Applied Thermal Engineering, 2009 Volume 29, pp.

1032 - 1042.

13. Chang, Y., and Wang, C., “A Generalized Heat Transfer Correlation for

Louver Fin Geometry,” Int. J. Heat and Mass Transfer, vol. 40, No. 3,

pp. 533-544, 1997.

14. Singh, G.M., Hrnjak, P.S., and Bullard, C.W., “Flow of Refrigerant

134a Through Orifice Tubes,” HVAC&R Research, Vol. 7, No.3, pp.

245-262, July 2001.

15. Cengel, Y.A., and Boles, M.A., “Thermodynamics - An Engineering

Approach,” McGraw-Hill, 1994.

16. Federal Register, May 7, 2010.

17. Federal Register, August 28, 2012.

CONTACT INFORMATION

Tibor Kiss

tibor.kiss@nrel.gov

Lawrence Chaney

lawrence.chaney@nrel.gov

Jason Lustbader

jason.lustbader@nrel.gov

ACKNOWLEDGMENTS

The authors would like to thank John Rugh and Jason

Lustbader of NREL for their contribution to this project.

The authors would also like to thank Lee Slezak and

David Anderson, Technology Managers for the U.S.

Department of Energy's Advanced Vehicle Technology

Analysis and Evaluation for sponsoring this work.

DEFINITIONS/ABBREVIATIONS

A/C - Air Conditioning

EV - Electric Vehicle

OEM - Original Equipment Manufacturer

NREL - National Renewable Energy Laboratory

0-D - Zero Dimensional

1-D - One-Dimensional

TXV - Thermostatic Expansion Valve

RPM - Revolution per minute

GHG - Green House Gases

Kiss et al / SAE Int. J. Passeng. Cars - Mech. Syst. / Volume 6, Issue 2(July 2013)