H1043 Final report 198808 1
The Impact of the HVAC System of an Electric Vehicle on the
Driving Range in Extreme Weather Conditions
Candidate Number: 198808
H1043 Final Report





Department of Mechanical Engineering
Supervisor: Dr. Kun Liang


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Summary
This report presents and investigation of the impact of the heating, ventilation, and air conditioning
(HVAC) system in extreme weather conditions on the driving range of an electric vehicle (EV). A
simulation program is established to assess the impact of cabin heating and cooling on the driving range.
For this a model of a HVAC system and EV driving range are developed. The simulation results indicate
that in extreme cold regions at a temperature of −50℃ the power required to run the HVAC system is
18.5 , resulting in a driving range loss of 45.93%. On the other hand, in extreme hot regions at a
temperature of 55℃, the power needed for the HVAC to run is 7.75 , leading to a driving range loss
of 19.22%. Moreover, the data trends show that an increase in the HVAC power, will increase the
percentage loss in driving range. Using this analysis, along with the background research conducted, two
models have been created of the HVAC system and the driving range of an EV using the
MATLAB/Simulink software.


































H1043 Final report 198808 3
Statement of Originality


I declare that the work in this dissertation titled “The Impact of an Electric Vehicle on the Driving Range
in Extreme Weather Conditions” has been carried out and completed by myself. The information
gathered from literature reviews have been acknowledged in both the text and the citation list provided.
No part of this dissertation was previously presented at the University of Sussex or any other institution.




Signed: Omar Elgouhary Date: May 11, 2021



































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Acknowledgements

I would like to acknowledge and thank:
- Dr. Kun Liang a professor at the University of Sussex and my supervisor for assisting and
supporting me throughout my project.
- Dr. Arash Dizpah a professor at the University of Sussex for providing me with guidance
regarding the HVAC MATLAB/Simulink model.

Nomenclature

Symbol Description Unit
EV Electric Vehicle -
CFCs Chlorofluorocarbons -
GHG Greenhouse Gases -
HVAC Heating, Ventilation, and Air Conditioning -
SOC State of Charge -
Total heat gain in the cabin
Ventilation load
Solar radiation load
Ambient load
Internal thermal load
Thermal load produced by the HVAC system
Resistance Force
Rolling Resistance Force
Aerodynamic Drag Force
Climbing Resistance Force
Gravity /2
Drag coefficient -
Rolling Resistance coefficient -
Frontal area
2
Vehicle velocity /
̇ Mass flow rate /
Compressor displacement
3/
Density of refrigerant at suction /
3
Cooling capacity
ℎ1 Enthalpy at the evaporator /
ℎ3 Enthalpy at the condenser /
Compressor volumetric efficiency -
Power delivered to the moist air flow through the source
Temperature
Gas constant / ∙
Area of the pipe surface
2
Density /3
Specific entropy at port A /
Specific entropy at port B /
ℎ Heat transfer coefficient /
2
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Temperature at an internal moist air volume
Conductivity at an internal moist air volume /
ℎ Hydraulic diameter of the pipe
̇ Rate of condensation /
Rate of energy loss from condensed water
Rate of energy that is added by the sources of gas trace and moisture
̇ Mass flow rates of water through port S /
̇ Mass flow rates of gas through port S /
Convection heat transfer coefficient /
Convective Heat Transfer
Distance between the layers (the thickness of the material)
Heating, ventilation, and Airconditioning output power
Outside ambient temperature ℃
Weight of the vehicle
Velocity of the vehicle /
Rolling resistance coefficient -


























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Table of Contents
Summary .................................................................................................................................... 2
Statement of Originality ............................................................................................................. 3
Acknowlegements ...................................................................................................................... 4
Nomenclature ............................................................................................................................ 4
Introduction ............................................................................................................................... 7
Project objectives.............................................................................................................. 8
Background research ................................................................................................................. 9
History of Electric Vehicles ............................................................................................... 9
Factors that influence Driving Range ............................................................................. 10
Electric Vehicle PID Controller ....................................................................................... 12
Electric Vehicle Energy Source ....................................................................................... 12
HVAC in an Electric Vehicle ............................................................................................ 14
Sustainability ............................................................................................................................ 17
Safety ....................................................................................................................................... 17
Project Planning ....................................................................................................................... 18
Electric Vehicle Selection ......................................................................................................... 18
Modelling of an Electric Vehicle .............................................................................................. 19
Modelling the Driving Range of a Tesla Model S ............................................................. 20
Driver Input ...................................................................................................... 20
Motor & Controller .......................................................................................... 21
Battery & SOC .................................................................................................. 22
Vehicle Body & Tires ........................................................................................ 25
Driving Range ................................................................................................... 26
HVAC System Modelled ........................................................................................................... 28
Blower Blockset ............................................................................................................... 28
Pipe Blockset .................................................................................................................... 29
Power required for the HVAC system .............................................................................. 30
Linking the HVAC system with the driving range of the EV ............................................. 32
Conclusion ................................................................................................................................ 40
Further Research ...................................................................................................................... 41
Citation ..................................................................................................................................... 41
Appendix .................................................................................................................................. 43
H1043 Final report 198808 7
Introduction

Recently, there has been an increase in global warming. A main contributor to global warming is the
transportation industry as the vehicles emit excessive amounts of greenhouse gases. The ‘greenhouse
effect’ is when warming of climate occurs, as a result of trapped heat radiation in the atmosphere from
Earth towards space. There are certain gases that are present in the atmosphere that resemble glass,
which allows sunlight to pass; however, it blocks heat from escaping earth to go to space. These gases
include water vaper, carbon dioxide, methane, chlorofluorocarbons (CFCs), and nitrous oxides [1].

Figure 1: The Global Greenhouse Gas Emission by Sector [2]
The above figure illustrates the global greenhouse gas (GHG) emission trend from 1990 to 2016. The
GHG emission is measured in tons of CO2. It is evident in figure 1 that the transportation industry is one
of the main contributors. The global GHG emissions was approximately 4.6 billion tons of CO2 in 1990,
eventually increasing by approximately 8 billion tons of CO2 by 2016, which is around 138.5 million tons
of CO2 emitted per year. Therefore, this results in a 4% global GHG emission increase per year
particularly from the transportation industry.
Over the recent years, there has been many technological developments that took place to help reduce
the greenhouse effect; one of the main solutions was the development of Electric Vehicles (EV). Electric
Vehicles can aid in reducing emission levels. However, their limited range, running costs and long
charging times are the some of their main drawbacks. These are some of the major factors that
differentiate between purchasing an electrical vehicle or a conventional vehicle powered by an internal
combustion engine. Many regions and countries around the world such as the UK have started giving
out grants as an incentive for people to purchase EVs. Some of the EVs can reach ranges of 500 km such
as Tesla, however, most of the EVs have ranges between 150-250 km such as the Peugeot Ion, BMW I3
and Nissan Leaf which can reach ranges of 150, 190 and 250 respectively [3]. These EVs stated are able
to abide by these ranges under ideal conditions that include constant mass, no air conditioning or
heating system is used, and standard driving cycle is implemented without slope. Regardless, there are
some conditions that can drastically impact the range travelled by an EV such as the environment, the
driver style and the vehicle design.
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Electric vehicles limited driving range is one of the main and biggest issues that this type of drivetrain
faces, especially in regions that are have extreme weather conditions, in particularly in cold region (e.g.
Russia, Canada, Sweden) during winter. This issue is also present in regions that are extremely hot
during summer such as the middle east. In winter, at low temperatures there are a few factors that
impact the Li-ion battery of an EV, such as harsher routes taken would require more power for traction
and heating is also demanded to ensure thermal comfort. The driving range of the electric vehicle can be
significantly reduced due to the combined effects of these conditions. In conventional vehicles, cabin
heating is not an issue as waste produce by the internal combustion engine is used for cabin heating. On
the other hand, the high efficiency of EVs means that the level of heat waste is very low, therefore
adding heating system becomes essential [4].
To study the thermal needs of a vehicles cabin, a model will be developed and analyzed. An existing EV
HVAC model developed by MATLAB will be used. The objective of this paper is to construct a model that
could predict the driving range of an electric vehicle considering weather conditions and the HVAC
system. The outcome will be to help drivers be aware of the actual driving range when the heat pump or
air-conditioning is on. The EV modeling will be simulated using MATLAB-Simulink program.

Project Objective
The main objective of this project was to model and simulate the effect of using the HVAC system in
extreme weather conditions on the driving range of an Electric Vehicle. To achieve this objective, the
first step that had to be implemented was to research and have a better understanding of the
components of Electric Vehicles and the functionalities of them. After conducting the research, a model
of both the driving range and the HVAC system of an EV would be developed using the
MATLAB/Simulink software. The two models will be linked together, to further help in gathering data,
that would later be evaluated and discussed in this report.
Excessive research will take place to find a way to model the driving range of the EV. In addition to that,
parameters from an already existing EV in the transportation industry will be used in the driving range
model, to allow for a more accurate and realistic results. Thus, an EV in the transportation industry will
be selected at a later stage in this report.
A brief summary of electric vehicles history will be discussed. To give the reader a more in depth
understanding on the upcoming of EVs and how they were first developed. Further investigation
regarding factors influencing the driving range will be carried out. Then the energy sources of an EV will
be analyzed and a comparison between the different types of batteries of an EV and their advantages
and disadvantages will be discussed. Moreover, there will be a brief description about the PID controller
of an EV. Furthermore, information regarding the HVAC system and the thermodynamic concept behind
it will be investigated in this report. These critical points will be used as a reference and background
information to help in creating and analyzing the EV driving range and HVAC system model respectively.
The EV driving range modelling will then take place. The step-by-step guide of the process of assembling
the model will be presented and explained in detail in this report. This will include the how the model
was created and the reason for using specific parameter. In addition to that, the HVAC system model
used will also be analyzed.
The outcome of both models will be used to aid in clarifying the impact the HVAC system has on the
driving range of an electric vehicle in extreme weather conditions. These results will be displayed using
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tables and figures to further allow the audience to see the effect of the HVAC system consumption in
different weather conditions on the range achieved by the EV.

Background Research

Figure 2: First crude oil electric carriage invented by Robert Anderson [5]
History of Electric Vehicles
The Electric Vehicle concept was first developed by Robert Anderson around 1832. It was the first crude
electric vehicle. The vehicle was powered by primary cells which worked as a single use battery. To
power the electric vehicle, Anderson used crude oil to produce power in the battery he invented. It was
not until the late 1870s that electric vehicles were considered practical. The first successful electric
vehicle was built in the United States by William Morrison in 1891. This vehicle was equipped with a
motor that generated four horsepower and a 24-cell battery that weighed 768 pounds, which was more
than half of the total weight of the vehicle [6]. Eventually by 1899, electric vehicles gained popularity as
they were compared with steam powered automobiles at that present at that time. This was due to the
electric vehicles being quiet, their ease to drive and they did not emit any pollutants which was
convenient for urban residents. By 1900s, electric vehicles in the US accounted for one third of all
vehicles on the road [7]. There were many innovators at that time that took note of the high demand of
electric vehicles and saw it as an opportunity to improve and enhance the performance of the vehicle.
Thomas Edison was one of those innovators. He thought that EVs provided a superior mode of
transportation, therefore, this led him to work on developing a better battery for the vehicle. After
many experiments Edison was able to create an EV that had an extended battery range of 100 miles
between charges than any electric vehicle at that time. Although the electric vehicle could cover a
greater range, it had drawbacks such as increasing the weight of the vehicle and it was costlier than
other lead-acid batteries at that time. By 1901, the vast majority of electric cars were equipped with a
plug and connections for recharging purposes. Then the world’s first hybrid electric vehicle was invented
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by Ferdinand Porsche. Eventually there was a decline in EVs as they had many drawback and limitations.
However, Electric Vehicles started becoming popular and sustainable once again over the past few
years, this is due to the technological developments that have occurred over the years. As many of
today’s technology was not present when the concept of the electric vehicle was first developed, thus
there is more room for success and improvement. Moreover, governments of many different countries
initiated providing incentives and grants to people to purchase EVs instead of the conventional internal
combustion engine vehicles. One of the main concerns people have regarding EVs are their limited
driving range.

Factors that influence the Driving Range
The EV range predication essentially depends on three major factors: environment, driver style, and
vehicle design. This is presented in figure 3. These three factors tend to depend on a variation of direct
and indirect parameters. A few of the parameters have constant values such as: vehicle type, number of
seats, vehicle mass, battery type, transmission type, road infrastructure, etc. On the other hand, there
are also parameters that are variable such as: battery state of charge (SOC), driver behavior, traffic,
environmental factors, cabin interior temperature, etc. Both constant and variable parameter impact
the range of the vehicle.

Figure 3: Factors that influence EV range
Three main factors that influence the EV driving range are the driver style, the environment and the
vehicle design as shown in figure 3. The EV range is impacted directly from the external environment
(outside temperature, wind, precipitation, etc.) and by the internal environment (cabin temperature,
use of the HVAC system, auxiliary system, etc.). The EVs total thermal load is the sum of both the
internal and external thermal loads. The following formula represents the thermal loads of a vehicle:
= + + + + (1)
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Where is the total heat gain in the cabin of the vehicle, is the ventilation load, is the
solar radiation load, is the ambient load, is the internal thermal load and is the thermal
load produced by the HVAC system. According to research, the thermal load generated by the HVAC
system significantly reduces the range of an EV in the summer and winter. The cooling load in summer
can lead to 17.2%-37.1% range reduction, and in winter heating load will cause reduction range from
17.1%-54.0% [8].
The range is not only influenced by the environment but also the driver. There are two ways that the
driver style may influence the range travelled by the vehicle. Firstly, the efficiency of an EV varies
depending on the “driver aggressiveness”. This refers to how the driver uses the acceleration pedal.
Multiple simulations of a real driving cycle were implemented to determine the impact of the range of
the EV. There were four acceleration cases that were compared, with an initial condition of reaching
50km/hr after 10,15,26, and 36 seconds. There was a clear difference in energy consumption between
the fastest and the slowest acceleration by 2.7% when an electric vehicle with a weight of 1500 kg was
considered, and by 4% when a 1000 kg electric vehicle was considered. Secondly, according to research,
there is a human factor that provides the driver with a psychological perception of the distance that
could be reached by an EV. This will eventually lead to range anxiety. This leads the driver to avoid
certain range situations that are critical to reserve safety buffer range. Research indicated that
experienced EV drivers tend to have less negative range ratings and lower range stress than
inexperienced EV drivers. Thus, the driving experience of an EV driver has a direct correlation on range
anxiety at the emotional, behavioral and cognitive levels. Another factor that directly influences the
driver’s behaviors is the traffic conditions. Depending on the traffic conditions, this will involve the use
of regeneration braking and the use of auxiliary equipment (HVAC system, light usage, entertainment
system, windows, etc.).
The vehicle design is a vital aspect as it affects the overall energy efficiency and aerodynamics of the EV.
This is also related to several factors such as factors that are required by the specific requirements of the
consumers from the automotive market and the general laws and regulations. Vehicle design includes
factors such as: the overall dimensions, passenger capacity, body type, luggage capacity, tire shape and
size, type of HVAC system, etc. The design of a vehicle begins from the physical relation, which was
outlined by Newton’s second law of motion. It takes into account all the forces acting on a vehicle in
motion. The forces that are imposed on the vehicle due to the environment are three resistance forces,
which are: the rolling resistance force , the aerodynamic drag force and the climbing
resistance force [9].
= 0.5( − 0)
2 (2)
Where is the drag coefficient, is the frontal area, and is the vehicle velocity.
= cos (3)
Where is the tire rolling resistance coefficient, which varies according to the road surface.
= sin (4)
Where expresses the slope/gradient of the path in degrees.
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The resistance force is the sum of all three forces.
= + + (5)

Electric Vehicle PID Controller

Figure 4: PID Controller structure for the EV Model [10]
The PID controller allows the electric vehicle speed to be equal to the cycle speed. It generates an
output between -1 and 1. An output between -1 and 0 means that the vehicle is breaking, however an
output between 0 and 1 indicates that the gas pedal of the vehicle is being used.

Electric Vehicle Energy Source
Electric Vehicles receive the energy that allows them to run from different sources. There are two focal
criteria’s that the source must satisfy. The first criteria is having high power density and the second
criteria is having high energy density. Moreover, other characteristics might be considered to make an
ideal energy source that involves containing long service and cycle life, fast charging, fewer costs and
undergoing less maintenance service. To achieve a long driving range, a high specific energy source is
required. For an EV the main source of energy provided by the battery. There are several types EV
batteries; the following table outlines these types and their benefits and limitations.
Table 1: Battery types, components, and their advantages and disadvantages [11]
Battery Type Components Advantages Disadvantages
Lead-acid - Positive electrode
contains lead oxide
- Negative electrode
contains metallic
lead
- Relatively low cost
compared to other
battery types
- Easily produced as the
technology used to
- It has a limited life
cycle when its run
on a deep rate of
SOC
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- Electrolyte is diluted
sulfuric acid
create it has been
available for over fifty
years
- Mass produced
- Low power density
and energy density
- It is heavy
- May require
maintenance
occasionally
- It cannot discharge
more than 20% of
its capacity
NiMH (Nickel-
Metal Hydride)
- Positive electrode
contains nickel
hydroxide
- Negative electrode
has alloys of nickel,
vanadium and
titanium
- Electrolyte is alkaline
solution
- It has double the energy
density of the lead-acid
battery
- Recyclable
- Can operate over a
wide range of
temperatures
- It has a longer life cycle
than lead-acid
- Harmless to the
environment
- It is safe to operate at
high voltage
- The lifetime of the
battery is reduced
by 200-300 cycles,
if it is discharged
rapidly at high load
current
Li-Ion (Lithium
Ion)
- Positive electrode
made up of oxidized
cobalt
- Negative electrode
made up of carbon
material
- Electrolyte contains
lithium salt solution
in organic solvent
- Has energy density that
is twice of NiMH
- Recyclable
- Battery life is long,
approximately operates
for 1000 cycles
- High specific power and
density
- Low memory effect
- Performs well at high
temperatures

- It is costly
- Takes a long time
to charge,
however, it is still
better than the
majority of
batteries
Ni-Cd
(Nickel-
Cadmium)
- Positive electrode
contains nickel
hydroxide
- Negative electrode
contains cadmium
- Long lifetime
- Recyclable
- Discharges fully without
getting damaged
- Costly
- Cadmium may
cause pollution if it
is not disposed
properly
Ni-Zn
(Nickel-Zinc)
- Positive electrode
contains nickel
oxyhydroxide
- Negative electrode
contains zinc
- Has high energy and
power density
- Materials used are not
costly
- They have rapid
growth of dendrite,
which prevents
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- Environmentally
friendly
- Operates at
temperatures that
range from -10℃ to
50℃
them from being
used in vehicles

HVAC of an Electric Vehicle
The heating, ventilation, and air conditioning system is manufactured in such a way to achieve better
environmental comfort in the automobile. Thus, the indoor air quality is maintained [12]. All modern
vehicles are equipped with a HVAC system. This system controls the heating and cooling inside the
vehicle. The modern HVAC system includes three essential components. The first component accounts
for the heating element which refers to the furnace or boiler. Moreover, it consists of a pipe system for
the fluid moving the heat from the engine coolant and transfers it to the incoming air. The ventilation
element is the second component which could be natural or forced, with a purpose to direct and move
the air within the cabin. The final component of the heating, ventilation and air conditioning system is
the air conditioning which intends on removing the existing heat from the interior of the vehicle to
enable having a cool cabin temperature [13].

Figure 5: EV Driving Range Analysis for Different Ambient Temperatures [14]
The above figure illustrates the correlation between the outside ambient temperature and the
percentage driving range loss due to the HVAC system. It is clear that in cold regions at a temperature
between −18℃ and 0℃, the range loss due to the HVAC system is between 11% and 13% since the
heating system will be in use. On the other hand, when the outside ambient temperature in hot regions
between 30℃ and 40℃, the driving range loss is between 8% and 11%, this is due to using the air
conditioning system. When the ambient temperature is 10℃, the driving range loss due to the HVAC
system is at its minimum level. This is because at that point the HVAC system is not being used.
The HVAC system in a vehicle has four main components that include a compressor, condenser,
expansion valve and evaporator. This process takes place over four steps. The first step involves
compressing the refrigerant by the compressor and eventually turns into a hot gas. The gas then moves
to the condenser in order to be cooled which leads it to turn into liquid form. The liquid then travels to
the expansion valve. As the refrigerant undergoes the expansion stage, it becomes a low-pressure gas
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and cools rapidly in the evaporator. In the final step, a fan is used to blow air at the evaporator cools
down the air that is comes out of the vehicle vents [15]. The thermodynamic components of an HVAC
system are shown in figure 6.

Figure 6: Schematic of an automotive air conditioning system [16]
A study was conducted that investigated the effect the engine speed had on the performance of R134a
and R152a as refrigerants for a vehicles air conditioning system. The cooling capacity represents the
amount of heat absorbed by the evaporator. There are two equations that are taken into account to
identify the relationship between the mass flow rate of the HVAC system on the engine speed.
̇ =

60
× × × (6)
= ̇(ℎ1 − ℎ3) (7)
Where ̇ is the mass flow rate (/), is the compressor displacement (
3/),
(/3) is the density of refrigerant at suction, is the compressor volumetric efficiency and
is cooling capacity. ℎ1 and ℎ3 (/) represent the enthalpy at the evaporator and condenser
respectively. These equations show a clear relationship between the mass flow rate and the cooling
capacity, that they are directly proportional to one another.

H1043 Final report 198808 16

Figure 7: Cooling Capacity vs Engine Speed of R134a and R152a
The figure above showcases the relationship between the cooling capacity and the engine speed of the
refrigerant R134a and R152a [16]. It is clear that the cooling capacity of both refrigerants increase, as
the engine speed increases. Refrigerant R134a has a slightly greater cooling capacity than the refrigerant
R152a, as R134a curve is slightly shifted up in figure 7. The gradient of both curves is approximately
1.92. According to Eq. (1), the high of cooling capacity of R134a compared to R152a was caused by the
high of mass flow rate of R134a compared to R152a, because the density of R134a is higher than that of
R152a. According to equation (6), the R134a has a higher cooling capacity than R152al, because it has a
larger density. From equation (7), it is evident that the cooling capacity and the mass flow rate are
directly proportional to one another, thus this indicates that the mass flow rate and the engine speed
are proportional to each other. Therefore, as the mass flow rate increases the engine speed will increase
accordingly.

Figure 8: Discharge Temperature vs Engine Speed
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The discharge temperature does not have a direct correlation to the air conditioning performance. The
discharge temperature is greater than the ambient temperature in normal conditions, because when it
operates the compressor produces heat. However, if the discharge temperature is too high, this will
decrease the compressor isentropic efficiency and overheating of the compressor. When the
compressor overheats, this causes the cylinder of the compressor and the piston ring to wear.
Moreover, overheating also may result in lubrication to break down which causes acceleration wear in
the compressor. Figure 8 shows the relationship between the relationship between the discharge
temperature of the during operation on the engine speed [16]. It is evident that as the discharge
temperature of the both refrigerants increases the engine speed also increases. This means that they
are directly proportional to each other. The discharge temperature for R152a is higher than that of
R134a for all engine speeds. The difference in discharge temperature between the two refrigerants
increases with increasing engine speed. The highest discharge temperature reached by R152a and R134a
are 93.1℃ and 74.3℃ respectively. These temperatures were achieved at an engine speed of 4000 rpm.

Sustainability
As mentioned in the introduction, vehicle manufacturers have been dealing with large pressures
regarding improving efficiency and emission levels. These emissions include GHG emissions which result
in global warming. It is estimated that since the late 19th century, the average global temperature has
risen by 1.18℃ [17]. This is due to increasing carbon dioxide emissions into the atmosphere and it is also
as a result of other human activities. As discussed previously in the introduction, the global greenhouse
gas emissions in terms of 2 emissions in 1990 was 4.6 billion tons. The 2 emissions have risen since
then and were measured to be 8 billion tons in 2016. This is illustrated in figure 1. With many countries
and governments around the world providing grants and incentives for people to purchase electric
vehicles rather than internal combustion engines, this may be a solution and a way to reduce global
warming and emission levels globally.


Safety
There are no physical safety issues in this investigation as the modeling involves using MATLAB/Simulink
software. There are several issues that may arise during the process. This could involve the components
of the electric vehicle that is being modelled are not compatible with one another. Therefore, this will
result in delays as more research must be conducted to figure out an alternative method to link the
components together. In addition to that, checking the formulas and units used on a regular basis to
ensure that the correct measurements are being used. Another issue that could be that the
MATLAB/Simulink software does not work. This has occurred multiple times throughout the process,
and therefore another laptop was used to complete the modelling process. Moreover, there laptop
could crash due to unforeseen reasons. This could eventually result in delays. Hence, there will be access
to more than one laptop for these unexpected circumstances.



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Project Planning
This project was divided into 5 key aspects to ensure that it addresses all the crucial points of the
investigation and that it is completed in a timely manner. These key aspects are outlined below.
1) Initiation: During the initiation phase research and background information was conducted
regarding the topic being investigated in this report. Progress of the project was monitored, and
feedback was provided from the supervisor. Weekly meetings with the supervisor were carried
out with a purpose of addressing any concerns or doubts about the project.
2) Planning: This stage involved planning the schedule, risk management, quality management and
resources. All the required facilities and equipment were handled. The planning stage was
completed in week 4. The planning phase included creating a Work Breakdown Structure (WBS),
laying out all the factors to be considered to conduct a successful project. The WBS is shown in
appendix 2. The WBS included all the steps that had to be taken at different stages of the
project to ensure that the project is going as planned. In addition, the project was tracked using
a Gantt chart which is illustrated in appendix 3.The Gantt chart created divided the project
workload throughout the two semesters. The first semester focused on planning and
researching about the topic. However, the second semester involved using the information
gathered to develop the MATLAB/Simulink models.
3) Monitoring and controlling: The time spend on the project will be monitored so that any
adjustments can take place if necessary. Progress of the project will be recorded weekly.
4) Execution: The execution phase will involve the construction of the electric vehicle model and
the HVAC system, which originally intended to take up to 4 weeks. However, due to initially
changing the EV Simulink model twice, this caused a delay in the execution stage of the project
which took up to 12 weeks.
5) Closing: The outcome will be to develop a model to help drivers to become more aware of the
actual driving range when there is heating and cooling in the cabin.

Electric Vehicle Selection
There are many EVs have been established in the transportation industry over the past few years. Some
managed to succeed with outstanding sales values, and others did not. One of the most well-known
companies for their EVs are Tesla. Tesla was founded by a group of engineers in 2003. This group of
engineers wanted to prove that people did not need to compromise in order to drive electric vehicles
and that the electric vehicles can be better, quicker and more fun to drive than internal combustion
engine cars. Today, Tesla does not only build all-electric cars but also ensure clean energy generation
products. Tesla believes that the quicker the world stops relying on fossil fuels and transitions to a zero-
emission future the better. Tesla first launched the Roadster in 2008, which had an electric drivetrain
and cutting-edge battery technology. Eventually, Tesla designed the first ever all electric sedan vehicle
from ground up, the Model S, which became the best car in its class in all categories. This car had
everything from safety to performance to efficiency and it reset the world’s expectations for the car
manufacturers of the 21st century. The Model S had the longest range of any electric vehicle, software
updates that made it better with time, and an acceleration time of 2.28 seconds from 0 to 60 mph [18].
Since the Model S is the leading electric vehicle sedan in the car industry, the EV model that will be
designed in this report will be a Tesla Model S. The parameters and specifications of the Tesla Model S
will be used as a reference to help in creating an Electric Vehicle.
H1043 Final report 198808 19

Figure 9: Tesla Model S [19]

Modeling of an Electric Vehicle
Electric Vehicles are composed of many different components such as: battery pack, a
controller, electric traction motor, thermal system (for cabin heating and cooling), transmission, etc. The
EV that will be discussed and analyzed in this paper is a Tesla Model S. The parameters of the Tesla
Model S will be used for creating the simulation. The main components of this vehicle that will be
modelled using MATLAB/Simulink will be the HVAC system which is in charge of the heating and cooling
in the cabin, and the driving range of the vehicle. The components of an EV are illustrated in figure 10.

Figure 10: Main components of an electric vehicle [20]
H1043 Final report 198808 20
Modeling the Driving Range of a Tesla Model S

Modelling Approach:

Driver Input → Motor & Controller → Battery → Vehicle Body & Tires → Driving Range

The first aspect that will be considered in this model is the driver input. The driver input refers to the
implementation of a drive cycle or having a constant vehicle velocity as the input. In this particular
model it is assumed that the vehicle would have a constant input velocity. The second aspect that will be
considered is the motor and controller of the electric vehicle. The modeling of the vehicle motor and
control will consist of a DC motor and a PI controller. The DC motor will require some adjustments as
well as tuning the PI controller to ensure that it fits the Tesla Model S being modelled. Then, the battery
of the EV will be put in place. This battery in use will be a Li-Ion battery, which is the type of battery used
in a Tesla Model S. Moreover, the vehicle body and tires will be modelled. This includes different aspects
of the EV such as a simple gear, differential simple body and the vehicle tires. Many of these parameters
are adjusted to fit the criteria and parameters for modeling a Tesla Model S. The final component and
most critical component for this investigation is the driving range. All four components discussed will be
used to estimate the driving range achieved by the EV modelled.



Driver Input


Figure 11: The Driver Input

The above is a figure that showcases the driver input. The vehicle has an input constant velocity of
80 /ℎ. This velocity also denoted as the reference velocity and it is connected to the longitudinal
driver block. The output velocity of the vehicle is connected as the feedback velocity as shown in figure
11. The longitudinal driver block includes parameters such as the grade, feedback velocity, reference
velocity, acceleration command and deceleration command. The grade of the vehicle will be 0 as the
vehicle will be tested on a flat surface, therefore there will be no incline angle. A multiport switch is
used to allow easier shifts between the inputs. Two controlled voltage sources are added to provide the
H1043 Final report 198808 21
same voltage throughout the circuit. The controlled voltage source has an output value between 0
and 1 , since they are connected to the controlled PWM voltage, therefore the input scaling is changed
to values between 0 and 1 , and the output voltage of 1 . The type of controller implemented in
this model in the longitudinal driver block set is a PI controller. This controller was tuned accordingly.
The following table showcases the values chosen for the PI controller.

Table 2: PI controller parameters
Proportional gain (Kp) 15
Integral gain (Ki) 1


Motor & Control


Figure 12: Motor & Control

A DC Motor block set is then introduced to the model. The output shaft of the motor is connected to the
input of the gearbox. The C port shown in the DC Motor should be connected to the body as it is a
reference, thus it will be connected to a mechanical rotational reference. There are several parameters
in the DC motor that had to be changed to fit the Tesla Model S criteria. The model parameterization
will be by rated load and speed, the no load speed will be 6150 and the rated speed will be
6000 respectively. The rated load power of the Tesla is 85 , and the rated DC voltage supply
will be 200 .

Table 3: DC Motor Parameters [21]
Armature inductance 12 × 10−6
No-load speed 6150
Rated speed (at rated load) 6000
Rated load (mechanical power) 85
Rated Dc supply voltage 200
H1043 Final report 198808 22
To be able to control the DC Motor, an H-Bridge block set was installed. The H-Bridge block set controls
different components such as the PWM port, REV port, and BRK port. The output generated from the H-
Bridge block is controlled by the voltage which depends on the input signal received from the PWM
port. The REV port signal determines the polarity of the output; this means that if the signal at the port
is less than the reverse threshold voltage parameter then the output has a positive polarity. However, if
the value of the signal at the REV port is more than the value of the reverse threshold voltage then it will
have a negative polarity. The third port is the BRK port, which is the braking threshold voltage. The
power supply of the H-Bridge will be internal.

To measure the current of the motor, a current sensor block was used, which was connected to a display
and scope block. The current sensor cannot be directly connected to the scope and display; hence
another PS-Simulink Converter was used. The DC motor parameter values are also illustrated in a figure
in appendix 1.


Battery & SOC

A simple battery was then added from the library browser, which provides a direct indication of the SOC.
A bus selector was put in place to help gather three crucial outputs from the battery which are the
voltage, the SOC and the current. The battery type was changed to Li-Ion battery, as this is the battery
that is used in a Tesla Model S. The Tesla Model S battery specifications are as follows:

Table 4: Li-Ion Battery Specifications of Tesla Model S [21]
Rated Capacity 3.2 ℎ
Nominal Voltage 3.6
Capacity Min. 3.25 ℎ
Typ. 3.35 ℎ
Weight 48.5
Charging CC-CV, Std. 1625 , 4.20 , 4.0 ℎ
Temperature Charge: 0 to +45℃
Discharge: −20 to +60℃
Storage: −20 to +50℃
Energy Density Volumetric: 676 ℎ/
Gravimetric: 243 ℎ/


The Tesla Model S is assembled with Li-Ion cells. It has a total capacity of 85 ℎ and has a total weight
of 540 which is accounted for in the vehicle mass. Tesla uses NCR18650B Lithium-Ion Battery cells.
Each cell has an average capacity of 3.3 ℎ as indicated in table 4 with a nominal voltage of 3.6 . It has
16 modules that are wired in series, each containing 6 groups of 74 cells which are wired in parallel.
Therefore, the nominal battery voltage calculated is:

= 3.6 × 16 × 6 = 346

Thus, the nominal voltage of the battery is 346 . The initial SOC of the battery was set to 90%.

H1043 Final report 198808 23

Figure 13: The Battery Model

The same current that is used in the motor will be used and supplied to the battery. Therefore, the
controlled current source was connected to the current output from the motor. When the model is run,
the current and voltage achieved by the battery are 23.75 and 292.8 respectively. In addition to
that, the final SOC reached by the battery is 0.1125.


Figure 14: Voltage variation with simulation time

Figure 14 showcases the relationship between the battery voltage and the simulation time. The initial
battery voltage is 375 . In this curve it shows that the voltage decreases with time. At the beginning
the voltage decreases at a constant rate. However, as the simulation time increases, the voltage
decreases rapidly, which eventually reaches 292.8 .
H1043 Final report 198808 24


Figure 15: Current variation with time

The figure above indicates the relationship between the battery current and time of the simulation. This
graph is zoomed in to show the variation of current at the start. It is evident from this curve that the
current is initially 0 at the beginning of the simulation. The current then shoots up to 430 . Eventually,
the current exponentially decreasing until it reaches a constant current of 23.75 at 50 seconds. The
current remains constant for the remaining time of the simulation.


Figure 16: SOC variation with time

The figure illustrated above shows the relationship between the SOC of the battery with the simulation
time. The initial SOC of the battery is 90%. As the simulation time increases, the SOC of the battery
decreases. This is because battery is used to run the EV and it is the main energy source that keeps the
vehicle running. Moreover, the battery is used to power the HVAC system. Therefore, the SOC of the
battery decreases as the EV is driven and as it powers the HVAC system the energy in the battery is
H1043 Final report 198808 25
reducing with time to ensure that these components are running and functioning successfully. The final
SOC reached by the battery is 0.1125%.


Vehicle Body & Tires


Figure 17: Vehicle Body & Tires

Figure 17 illustrates the vehicle body and tires block sets. There are four main components that are
shown in the above figure, which include the vehicle body, the tires, the differential and a simple gear.
The Vehicle body block set was the first block set to be implemented in the model. It was linked to both
tire block sets through the h-hub and the normal reaction force of the tires. The Vehicle Body block set
included parameters of the vehicle mass, fontal area, drag coefficient, and air density. These parameters
were changed to fit the characteristics of a Tesla Model S. The following table showcases these
parameters.

Table 5: Vehicle Body Parameters [22]
Mass 2108
Frontal Area 2.34 2
Drag Coefficient 0.24
Air Density 1.225 /3

Moreover, the tires parameters were also adjusted. The output of the simple gear was connected to the
input of the differential, which was then connected to the axle of the tires. The shaft rotation of the
simple gear was altered in the same direction as the input shaft to allow for the velocity to be positive.

To observe the output velocity of the vehicle body, a display and scope block sets were used. The output
velocity is a physical signal; therefore, a PS-Simulink Converter block set was used to convert it to a
digital signal in order to display the output velocity. The velocity is measure in /ℎ.

Tesla Model S Tires Parameters:

H1043 Final report 198808 26
The Effective Radius of Tires is 245/45R19 [22]. This means that the width of the tires is 245 , and
the 45 is the aspect ratio of the tires which means that the sidewall height of the tires is 45% of the
width. The radial tire construction is denoted by R and the 19 stands for the diameter of the inner rim
size of the tires which is measured in inches.

Converting inches to meters:

1 =
1 ℎ
39.37


∴
19 ℎ
39.37
= 0.482 ≈ 0.48

This means that 19 ℎ is approximately 0.48 . The following table represents the parameters that
were changed in the tires block set:

Table 6: Rolling resistance parameters [22]
Rolling Radius 0.48
Rolling Resistance Constant Coefficient 0.01
Rolling Resistance Velocity Threshold 0.001 /


Driving Range


Figure 18: EV Driving Range

The figure above indicates the part of the model that consists of the driving range established by the
Tesla Model S. The final constant velocity achieved by the vehicle is 31.54 /ℎ, which was then
integrated and divided by 3600 to display the vehicles estimated driving range. The overall driving range
of the vehicle modelled is 365.3 which is illustrated when the simulation is run.
H1043 Final report 198808 27

Figure 19: Relationship of initial and final velocity of the EV with time

Figure 19 demonstrates a graph of the variation of the initial and final velocities of the EV with time. The
initial velocity and final velocity are denoted by the blue and yellow curve respectively. The initial
velocity is 80/ℎ, whereas the final velocity of the EV is 31.54 /ℎ. The main reason for this
decrease is because several factors were taken into consideration such as the resistance force, weight of
the vehicle, passenger weight, etc.


Figure 20: Driving range
The driving range achieved by the Tesla Model S modelled is displayed in figure 20. The driving range
achieved by the model is 365.3 .


H1043 Final report 198808 28
HVAC System Modelled
The EV MATLAB HVAC model used in this report is an already existing model created by MATLAB. The
main components of this model are a cabin, recirculation flap, blower, evaporator, duct and a manual
system input. There are several equations that were used to develop this model.

Figure 21: HVAC System [23]

The first step involves the mass flow rate to go through the blower which is a controlled mass flow rate
source. It represents an ideal mechanical source in an area of moist air. The mass flow rate is controlled
at the input signal port M. The mass flow rate is maintained by the source regardless of the pressure
differential. There is no heat exchange with the environment and no flow resistance. When there is a
positive mass flow rate this causes the flow to move from port A to B. The subscripts a, w, and g are the
properties of dry air, water vapor and trace gas respectively [23].

Blower Blockset

The equations modelled at the blower:
Mass balance at the blower:
̇ + ̇ = 0 (8)
̇ + ̇ = 0 (9)
̇ + ̇ = 0 (10)
The subscripts A and B represent the appropriate port.
Energy balance at the blower:
+ + = 0 (11)
Where is the power delivered to the moist air flow through the source. If there is no work
performed by the source, then = 0. However, if the source is isentropic, then = ̇(ℎ +
ℎ)
Where
ℎ = ℎ +
1
2
(
̇

)
2

ℎ = ℎ +
1
2
(
̇

)
2

H1043 Final report 198808 29
Where ℎ and ℎ represent the specific total enthalpy at ports A and B respectively. and are the
density at ports A and B respectively. and are the specific entropy at the two ports.
The enthalpies ℎ = ℎ() and ℎ = ℎ() are constrained by the isentropic relation which states that
there is no change in entropy:
∴ ∫
1

ℎ() = (


)


(12)
Where T and R represent the temperature and the gas constant respectively. The density at ports A and
B are and respectively. The quantity specified at the input of the source port is:
̇ = ̇
The assumptions that are made:
- There is no heat exchange with the surroundings (environment)
- There are no irreversible losses


Pipe Blockset

The pipe block represents the pipe flow dynamics in an area of moist air due to convection heat transfer
with the pipe wall and viscous friction losses. The temperature and pressure changed depending on the
thermal capacity and the compressibility of the moist air volume. The liquid water condenses from the
moist air volume when it becomes saturated [23].

The net flow rate into the moist air volume inside the pipe is:
̇ = ̇ + ̇ − ̇ + ̇ + ̇ (13)
= + + − + (14)
̇, = ̇ + ̇ − ̇ + ̇ (15)
̇, = ̇ + ̇ + ̇ (16)
Where ̇ is the rate of condensation, is the rate of energy loss from condensed
water, is the rate of energy that is added by the sources of gas trace and moisture, ̇ and ̇ are
the mass flow rates of water and gas through port S respectively. The values ̇ , ̇ and are
determined by the trace gas and the moisture sources that are connected to port S of the pipe.

Convective Heat Transfer:
The convective heat transfer equation between the internal moist air volume and the pipe wall is as
follows:
= +

ℎ
( − ) (17)
Where is the area of the pipe surface, which is =
4
ℎ
. The subscript I represents the
properties of the internal moist air volume. Therefore and are the temperature and the
conductivity respectively at an internal moist air volume. ℎ is the hydraulic diameter of the pipe.
The distribution of the temperature alone the pipe is assumed to be exponential, therefore the
convective heat transfer is:
= |̇|,( − ) (1 −
ℎ
|̇|,) (18)
H1043 Final report 198808 30
Where ̇ = (̇ + ̇)/2, the average flow rate from port A to B, is the inlet temperature
which depends on the direction of the flow, and ,is the specific heat based on the average
temperature.
The heat transfer coefficient ℎ depends on the Nusselt number, which is defined using the
following equation:
ℎ =

ℎ
(19)
Where is the thermal conductivity at the average temperature.
Assumptions at the pipe:
- The pipe wall is rigid
- Gravity is neglected
- The inertia of fluid is neglected

Cabin Heat Transfer:
The cabin heat transfer is divided into three parts:
1) The cabin heat transfer at the vehicle glass
2) The cabin heat transfer at the vehicle door
3) The cabin heat transfer at the vehicle roof
The Convective Heat Transfer represents the convection between two bodies due to fluid motion. The
equation used to model the heat transfer at the cabin is governed by Newtons law of cooling:

= ( − ) (20)
where Q is heat flow, k is the convection heat transfer coefficient, A is the surface area, and are
the temperatures of the bodies at port A and B respectively.

The conductive heat transfer represents the heat transfer by conduction between two layers that of the
same material. This transfer is governed by the Fourier law and is shown in the following equation:
=


( − ) (21)
Where D is the distance between the layers (the thickness of the material).


Power required for HVAC system

In any HVAC system design, calculations of cooling needs must be implemented to ensure that the
system runs as expected. The main reason for carrying out the cooling capacity calculations is to allow
cooling and heating to take place and to serve its intended purpose of maintaining the required
conditions within the cabin. For this purpose, a model was developed to calculate the cooling capacity of
the HVAC system of the EV during its travel. This model is established based on identifying the power
required by the HVAC system as a function of the outside ambient temperature which is responsible of
the thermal exchange that occurs with the outside environment.
H1043 Final report 198808 31
Taking into consideration that the cabin temperature is kept constant, this indicated that the HVAC
power is directly proportional to the external temperature. Therefore, a linear equation is used “ =
+ ”. The power required to provide thermal comfort for the passenger within the cabin can then be
expressed by the following expression [24]:

= + (22)

where is the outside ambient temperature, a and b are constants. To determine the values of the
two constants a and b, the two following points are selected: HVAC system is of when < 24℃ to
= 24℃ (comfort temperature), which results with = 0.

The highest temperature for this application will be taken as 55℃ that represent areas that reach
extremely hot temperatures, and the lowest temperature will be taken as −50℃ for areas that reach
extremely cold temperatures. Thus, when the HVAC system is from > 24℃ to = 55℃ , the
power achieved is = = 7.75 . Solving this system, the HVAC system power is given by
the following expression:

= 0.25 − 6 (23)

This relationship is illustrated in figure 22.

Table 7: Impact of different outside ambient temperatures on HVAC power
Outside Ambient Temperature () HVAC Output Power ()
−50℃ −18.5
−45℃ −17.25
−40℃ −16
−35℃ −14.75
−30℃ −13.5
−25℃ −12.25
−20℃ −11
−15℃ −9.75
−10℃ −8.5
−5℃ −7.25
0℃ −6
5℃ −4.75
10℃ −3.5
15℃ −2.25
20℃ −1
25℃ 0.25
30℃ 1.5
35℃ 2.75
40℃ 4
45℃ 5.25
50℃ 6.5
55℃ 7.75

H1043 Final report 198808 32

Figure 22: Relationship between the outside ambient temperature and HVAC output power
Linking the HVAC system with the Driving Range of the EV
Both MATLAB/Simulink models are supposed to be linked together through power. The output power of
the HVAC system must be subtracted from the battery power of the electric vehicle. However, since
there was no direct way to identify the battery power of the vehicle from the Simulink block, the HVAC
power was added to the rolling resistance of the vehicle.
An assumption will be made that the HVAC system will increase the resistance of the car. This will
increase the vehicle load. We will assume to compensate for that in the model, that the power of the
HVAC system will add to the resistance of the vehicle, which will eventually reduce the driving range
achieved by the car. Thus, the rolling resistance of the vehicle will increase as a result of adding the
power required to run the vehicle at different temperatures. The initial rolling resistance coefficient 1
of the vehicle was 0.01. The new rolling resistance will be calculated for each HVAC output power
accordingly to see the impact it has on the driving range of the vehicle. The initial driving range achieve
by the vehicle was 365.3 km. The following equation will be used to identify the new rolling resistance of
the depending on the HVAC power:
= 2 (24)
where is the weight of the vehicle, 2 is the new rolling resistance coefficient of the vehicle, and is
the final velocity of the vehicle which is 31.54 /ℎ as seen in figure 22.
Converting the velocity from /ℎ to /:
y = 0.25x - 6
-20
-15
-10
-5
0
5
10
-60 -40 -20 0 20 40 60 80
H
V
A
C
O
u
tp
u
t
P
o
w
er
(
kW
)
Outside Ambient Temperature (℃)
Outside Ambiet Temperature vs. HVAC
Power
H1043 Final report 198808 33
31.54

ℎ
×
1000
3600
=
31.54
3.6
= 8.76 /
Rearranging this equation, we get:
2 =



Calculating the rolling resistance coefficient for the HVAC power, we get:
2 =



Therefore, the total rolling resistance coefficient will be:
= 1 + 2
The rolling resistance coefficient at HVAC output power with a temperature of 55℃:
2 =
(7.75 × 103)
(2108)(9.81)(8.76)

2 = 0.0428
Therefore, the total rolling resistance coefficient will be:
= 0.01 + 0.0428

= 0.0528
The total rolling resistance calculated is 0.0528, which takes into account the power outcome generated
from the HVAC system of the vehicle. The initial rolling resistance of 0.01 in the Simulink model is then
changed to the calculated total rolling resistance of 0.0528 which takes into account the power required
for the HVAC system. When this value is updated in the model the driving range achieved by the vehicle
becomes 295.1 which is a decrease of 70.2 from the initial driving range of the Tesla S model.
The above calculation indicates that rolling resistance coefficient is directly proportional to the power
required to run the HVAC system of the vehicle. However, the rolling resistance coefficient is inversely
proportional to the product of the weight and the velocity of the vehicle. The same calculation will be
carried out for the remaining HVAC output power depending on their temperature accordingly.
However, some of the HVAC power outputs are negative, which will result in a negative rolling
resistance which is not possible, hence they will be taken as positive values to ensure a valid outcome.





H1043 Final report 198808 34
Table 8: Correlation of outside temperature, HVAC power, RR coefficient on driving range
Outside Ambient
Temperature (℃)
HVAC Output
Power ()
Total Rolling Resistance
Coefficient
Driving Range Achieved
()
−50 18.5 0.1121 197.5
−45 17.25 0.1052 208.9
−40 16 0.0983 220.2
−35 14.75 0.0914 231.6
−30 13.5 0.0845 242.9
−25 12.25 0.0776 254.3
−20 11 0.0707 265.6
−15 9.75 0.0638 277
−10 8.5 0.0569 288.3
−5 7.25 0.0500 299.6
0 6 0.0431 311
5 4.75 0.0362 322.3
10 3.5 0.0293 333.6
15 2.25 0.0224 344.9
20 1 0.0155 356.2
25 0.25 0.0114 363
30 1.5 0.0183 351.7
35 2.75 0.0252 340.3
40 4 0.0321 329
45 5.25 0.0390 317.7
50 6.5 0.0459 306.4
55 7.75 0.0528 295.1
The total rolling resistance coefficient increases at higher HVAC output power. This is because as the
power needed to run the HVAC system increases the more load it adds on the vehicle. Therefore, the
maximum rolling resistance coefficient can be seen when the temperature is −50℃ as it will require
heating power of 18.5, leading to rolling resistance coefficient of 0.1121. Whereas the least rolling
resistance coefficient is can be observed when the temperature is 25℃, as the power required from the
HVAC system is at a minimal level of 0.25, which result in a rolling resistance coefficient of 0.0114.

H1043 Final report 198808 35

Figure 23: Correlation Between Rolling Resistance Coefficient and Driving Range
Figure 23 shows the correlation between the rolling resistance coefficient and the driving range. As the
driving range increases, the rolling resistance decreases. It is clear that at higher rolling resistance
coefficient values the driving range is minimized due to the load of the vehicle increasing which will
directly impact the performance of the vehicle and the range achieved. The rolling resistance coefficient
and the driving range of the vehicle are inversely proportional to each other. From the curve it is evident
that the rolling resistance coefficient has a linear relationship with the driving range, which is outlined
by the equation “ = −0.0006 + 0.2324”. This equation represents a linear relationship, with a
gradient of −0.0006, which means that the rolling resistance decreases by an average of approximately
0.0006 as the driving range increases.


y = -0.0006x + 0.2324
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0 50 100 150 200 250 300 350 400
R
o
lli
n
g
R
es
is
ta
n
ce
C
o
ef
fi
ci
e
n
t
Driving Range (km)
Correlation Between Rolling Resistance Coefficient
and Driving Range
H1043 Final report 198808 36

Figure 24: Relationship between Outside Ambient Temperature and Driving Range
The figure above shows the relationship between the outside ambient temperature and the driving
range. It is clear that at temperatures between −50℃ to 25℃, the driving range achieved by the vehicle
increases gradually with increasing temperature. Therefore, as the climate temperature approaches
25℃ from negative temperatures, the driving range will increase. The driving range will reach its peak,
when the outside ambient temperature is 24℃. The maximum driving range achieved at that
temperature is 365.3 . As the climate temperature increases in hotter regions, this will decrease the
driving range as seen in figure 24. At temperatures between −25℃ to 55℃, the driving range decreases
linearly.

Figure 25: Relationship between HVAC Power and Driving Range
0
50
100
150
200
250
300
350
400
-60 -40 -20 0 20 40 60 80
D
ri
vi
n
g
R
an
ge
(
km
)
Outside Ambient Temperature (℃)
Relationship between Outside Ambient
Temperature and Driving Range
y = -9.066x + 365.31
0
50
100
150
200
250
300
350
400
0 2 4 6 8 10 12 14 16 18 20
D
ri
vi
n
g
R
an
ge
(
km
)
HVAC Power (kW)
Relationship between HVAC Power and Driving
Range
H1043 Final report 198808 37
Figure 25 showcases the relationship between the HVAC outcome power and the driving range of the
vehicle. It is evident from the figure, that as the HVAC power increases, the driving range decreases. At
the maximum HVAC power of 18.5 , the driving range achieved by the vehicle is at its lowest point of
197.5 . However, the minimum HVAC power is 0.25 which achieves 363 . This graph
indicates that the driving range decreases by an average of approximately 9.066 at each
temperature increment which is represented in table 8.
Table 9: Correlation of outside temperature and driving range
Outside Ambient Temperature (℃) Driving Range Achieved ()
−50 197.5
−45 208.9
−40 220.2
−35 231.6
−30 242.9
−25 254.3
−20 265.6
−15 277
−10 288.3
−5 299.6
0 311
5 322.3
10 333.6
15 344.9
20 356.2
25 363
30 351.7
35 340.3
40 329
45 317.7
50 306.4
55 295.1
Calculating the percentage driving range loss:

=
− ℎ ( ℎ )

× 100%
Calculating the percentage loss of driving range at −50℃:
=
365.3 − 197.5
365.3
× 100%
= 45.93%

H1043 Final report 198808 38
The percentage driving range loss at all the temperatures will be carried out using the same equation.
The results are presented in the following table.

Table 10: Percentage driving range loss with respect to the outside ambient temperature:
Outside Ambient Temperature (℃) Driving Range Achieved () Percentage driving range
loss (%)
−50 197.5 45.93
−45 208.9 42.81
−40 220.2 39.72
−35 231.6 36.60
−30 242.9 33.51
−25 254.3 30.39
−20 265.6 27.29
−15 277 24.17
−10 288.3 21.08
−5 299.6 17.99
0 311 14.86
5 322.3 11.77
10 333.6 8.68
15 344.9 5.58
20 356.2 2.49
25 363 0.63
30 351.7 3.72
35 340.3 6.84
40 329 9.94
45 317.7 13.03
50 306.4 16.12
55 295.1 19.22

The data in table 10 indicates the maximum range reduction occurs at −50℃ with a driving range of
197.5. This is 167.8 lower than the initial driving range of the electric vehicle which is a 45.93%
decrease. Whereas the minimum range reduction occurs at a 25℃ with a driving range of 363,
which is lower than the initial amount by 0.3. The percentage range loss in this case is 0.63%. When
the outside ambient temperature is lower than 0℃, the range reduction varies between 17.99% and
45.93%. The average range loss below 0℃ is approximately 31.95%. On the other hand, when the
temperature is higher than 0℃, the range reduction varies between 11.77% and 19.22%. The average
range loss below 0℃ is approximately 8.91%. The difference between when the temperature is lower
than 0℃ and when the temperature is higher than 0℃ is approximately 23.04% in range reduction. This
shows that the range is impacted much more in colder weather than in hotter weather. This is because
more power is required to run the heating system than the air conditioning system. This can be proven
by observing the percentage driving range loss figures in table 10. This is also illustrated in table 8 with
the HVAC output power values. It is evident that the power required to run the HVAC system increases
as the temperature lowers below 0℃.

H1043 Final report 198808 39

Figure 26: Percentage Driving Range Loss vs. Outside Ambient Temperature

The above figure illustrates the relationship between the outside ambient temperature and the
percentage driving range loss due to the HVAC system. It is evident that as the temperature increases
from −50℃ to 25℃, the percentage driving range loss decreases. Figure 26 indicates the impact of the
HVAC system on the driving range. As the heating system is in use at lower temperatures between
−50℃ to 0℃, it is evident that there is a large percentage of the driving range lost. The driving range
percentage loss is maximized in cold regions when the temperature approaches −50℃, which leads to a
driving range loss percentage of 45.93 %; this is due to the excessive use of heating which requires a
significant power of 18.5 . When the outside ambient temperature is 24℃, the driving range loss
due to the HVAC system is at its minimum level. This is because at that temperature the HVAC system is
not in use, which means that the power required for the HVAC system is 0. Therefore, the rolling
resistance coefficient and the driving range will not be affected. The percentage driving range loss then
increases as the temperature increases from 25℃ to 55℃. At high temperatures closer to a
temperature of 55℃, the need for using the vehicles air conditioning system will be required. Therefore,
leading the EV driving range to decrease dramatically with a driving range loss of 19.22%. The
percentage driving range loss decreases by 1.66% from temperatures varying from −50℃ to 25℃. On
the other hand, the percentage driving range loss increases by 1.61% from 25℃ to 55℃.






0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
-60 -40 -20 0 20 40 60 80
P
er
ce
n
ta
ge
L
o
ss
o
f
D
ri
vi
n
g
R
an
ge
(
%
)
Outside Ambient Temperature (℃)
Percentage Driving Range Loss vs. Outside Ambient
Temperature
H1043 Final report 198808 40
Conclusion

As the transportation industry transitions from using internal combustion engine to electrical drivetrain
in the future, this will have a great impact on global warming. Today, many countries and governments
are supporting the move to electrical drivetrains by providing incentives and grants to people to
purchase electric vehicles. As a result, this would decrease global warming figures by significant
amounts. Further, this will also dramatically reduce the spread and emission of greenhouse gases in the
atmosphere. This transition from ICE to electrical drivetrains would be a concern for some people. One
of the main limitations of electrical drivetrains is “range anxiety”. In particularly, the driving range of an
EV is affected by three main factors which are the driving style, environment, and vehicle design. In this
report the environmental factor is investigated, with a focus to identify the impact of using the HVAC
system in extreme weather conditions on the driving range of the electric vehicle. With the use of
MATLAB/Simulink software, two model were developed to further help in this analysis. This consisted of
an HVAC model and a driving range model of an EV. To explore the impact of the using the HVAC system
on the EV driving range several steps had to be taken. Firstly, the model of the EV driving range had to
be developed. Then, parameters from an already existing EV in the transportation industry were used to
ensure that the model runs effectively. Secondly, both models had to be linked to one another. This was
established by identifying the power required to run the HVAC system at various temperatures and
removing that power from the battery implemented in the driving range model. Unfortunately, there
was no direct way to remove the HVAC power from the battery. Instead, an assumption was made, that
the power of the HVAC system will add to the rolling resistance coefficient of the vehicle. This led the
vehicle load to increase, resulting in a reduction in the achieved driving range. The driving range of an EV
can be affected in extremely cold and hot areas respectively. With the use of both simulation models,
this issue was analyzed and investigated. This was conducted by using a variety of temperatures that
varied from −50℃ to 55℃ with increasing increments of 5℃. Each temperature generated a different
HVAC power, which resulted in having different rolling resistance coefficients, leading to various driving
ranges. The main results acquired from both models:
- The rolling resistance coefficient and the driving range of the vehicle are inversely proportional
to each other.
- At an outside ambient temperature of 24℃, the driving range will reach its peak at 365.3 .
This is because the driving range loss due to the HVAC system is minimal as the power required
to run the HVAC is 0. This means that heating and air conditioning system are not in used.
- However, in extremely cold regions when the temperature is −50℃, the driving range
established is at its lowest point of 197.5. This is a 45.93% decrease from the initial driving
range achieved by the EV. In addition to that, the HVAC power required due to the excessive use
of heating in the vehicle is 18.5 .
- On the other hand, in extremely hot region when the temperature is 55℃, the driving range
reached by the vehicle is 295.1, which is a 19.22% range loss from the initial value. This
range loss is due to the ongoing use of the air conditioning system which requires a HVAC power
of 7.75.
- It is clear that in both extremely hot and cold weather respectively, the driving range of an
electric vehicle decreases due to HVAC consumption. This is due to the excessive use of the
heating and air conditioning system in extreme weather conditions.




H1043 Final report 198808 41
Further Research

- The model created in this investigation is a simple model of an EV. However, there are many
more factors and components that make up an EV that were not considered in this model such
as traction control, regeneration braking, etc. By taking into account these components a more
accurate model could be developed.
- As discussed in the introduction, in the conventional powertrain waste heat is used to run the
HVAC system. However, in EVs they must provide heat to the control cabin climate using the
energy stored in the vehicle. The energy is typically provided from the EV battery source. In very
cold weather conditions heat is required to heat the cabin which is required from the propulsion
of the vehicle. This significantly reduces the driving range achieved by the vehicle. To minimize
the range issue associated with EVs a climate control system that includes thermal storage could
be implemented [25]. This system can be developed in such a way to use the stored latent heat
of an advanced phase change material (PCM) to generate cabin heating. The phase change
material is then melted while the EV is charging the battery, and the energy stored is eventually
transferred to the cabin when driving. The thermal losses can be minimized in this case by
ensuring that the PCM is encased in an insulation system of high performance. This method
could be a very convenient solution for reducing EV range anxiety in extremely cold areas that
require excessive heating.



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H1043 Final report 198808 43
Appendix

Appendix 1: DC Motor Parameters























H1043 Final report 198808 44
Appendix 2: Work Breakdown Structure (WBS)



Appendix 3: Gannt chart





























































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































































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