Modeling, Analysis, and Optimization of Electric Vehicle HVAC Systems
Mohammad Abdullah Al Faruque and Korosh Vatanparvar
Department of Electrical Engineering and Computer Science
University of California, Irvine
Irvine, California, USA
E-mail: {alfaruqu, kvatanpa}@uci.edu
Abstract— Major challenges of driving range and battery life-
time in Electric Vehicles (EV) have been addressed by designing
more efficient power electronics, advanced embedded hardware,
and sophisticated embedded software. Besides the electric motor
in EVs, Heating, Ventilation, and Air Conditioning (HVAC) has
been seen as a significant contributor to the EV power consump-
tion. The main responsibility of automotive climate controls has
been to control the HVAC system in order to maintain the passen-
gers’ thermal comfort. However, the HVAC power consumption
and its dynamic behavior may influence the battery lifetime and
driving range significantly. Therefore, modeling and analyzing
the HVAC system and its thermodynamic behavior may benefit
the control designers to integrate the HVAC control and optimiza-
tion into Battery Management Systems (BMS) for better battery
lifetime and driving range. In this paper, the EV architecture,
HVAC system dynamic behavior, and battery characteristics are
explained and modeled. Automotive climate controls (e.g. bat-
tery lifetime-aware automotive climate control) and the benefits
gained by system modeling and estimation for different condi-
tions in terms of battery lifetime and driving range are illustrated.
Moreover, present and future challenges regarding the HVAC sys-
tem and control design are explained.
I. INTRODUCTION AND BACKGROUND
Electric Vehicles (EV) have been introduced as a zero-
emission mean of transportation [1] in order to address the en-
vironmental issues such as: GreenHouse Gas (GHG) emission,
air pollution, and noise pollution [2]. EVs have become pos-
sible due to the significant advancement in battery and power
electronic design and manufacturing [3, 4].
However, the EVs pose new design challenges in terms of
driving range and battery lifetime. The driving range is lim-
ited to the available battery capacity which is restricted by
the battery pack design constraints, e.g. size, cost, and vol-
ume [5, 6]. The limited driving range and its erroneous es-
timation may make the drivers cut their daily trips shorter in
order to avoid getting stranded (range anxiety) [7, 8]. On the
other hand, State-of-Health (SoH) a metric for battery lifetime
represents the battery capacity compared to the rated value
which degrades due to the battery stress over time. The bat-
tery stress is significantly dependent on the power consump-
tion of the whole EV [9–11]. The battery lifetime degrada-
tion diminishes the driving range further. Moreover, when
20% of battery capacity degrades, the battery becomes useless
which enforces the significant cost of battery replacement on
the drivers [9, 12].
These challenges have been addressed by implement-
ing more advanced and efficient power electronics such as:
energy- and power-dense battery cells and efficient drive train.
Moreover, sophisticated Battery Management System (BMS)
is implemented to monitor the battery cells’ state and control
their utilization. The BMSs are responsible for ensuring safe
battery operation by preventing over charge, over discharge,
and thermal violation [11]. Moreover, they attempt to balance
the battery cells’ utilization for improving the available bat-
tery capacity, increasing the driving range, and extending the
battery lifetime [13, 14].
In most of the solutions to improving the driving range and
battery lifetime, the amount of the power required by the elec-
tric motor has been considered in details by estimating and
measuring the driving forces on the vehicle [14–16]. How-
ever, the total EV power consumption is not limited to only the
electric motor. There are other auxiliary components in EVs
which contribute to the EV power consumption.
Fig. 1. EV and ICE Vehicle Power Consumption Comparison and EV
Driving Range Analysis for Different Ambient Temperatures.
Heating, Ventilation, and Air Conditioning (HVAC) is a
common auxiliary component in vehicles nowadays. The
HVAC system may consume significantly depending on the
vehicle environment condition [17, 18]. The architecture de-
sign in Internal Combustion Engine (ICE) vehicles helps the
HVAC system to use the heat generated from the engine for
heating the cabin. Therefore, only fan may consume power
for maintaining the cabin temperature in cold weather. How-
ever, due to the architecture difference in EVs compared to
ICE vehicles, there is no heat generated from the electric mo-
tor to be used by the HVAC system. This may increase the
HVAC power consumption significantly, since more power is
required by the heating coils for heat generation. We further
analyzed the HVAC power consumption for different ambient
temperatures.
According to existing data, we have analyzed the power con-
sumption in an EV (Tesla Motor S 60KWh [19]) and an Inter-
nal Combustion Engine (ICE) vehicle (Toyota Corolla [20])
while cruising at 65mph when the HVAC is powered on and
maintaining the cabin temperature (Fig. 1). The electric motor
efficiency in EVs and engine efficiency in ICE vehicles change
for different ambient temperatures, however, their consump-
tion stays the same compared to the HVAC system. Moreover,
other accessories in the vehicle (e.g. entertainment, steering,
lighting) consume the same insignificant amount, regardless of
the ambient temperature. While, the HVAC system has to con-
sume power in hot/cold weather to cool/heat the cabin. The
percentage, the HVAC contributes to the total power consump-
tion in EVs (upto 20%), is more significant than in ICE vehi-
cles (upto 9%). Therefore, this may increase the battery stress
and thereby affect the battery lifetime and EV driving range
significantly (decrease upto 13% in driving range). Due to the
longer recharging time and relatively less number of charging
stations, it may further worsen the situation for the driver and
causes range anxiety [7, 8].
II. ELECTRIC VEHICLE ELECTRIC MOTOR
Electric Vehicles utilize electric motors in order to provide
the tractive force required for propelling the vehicle. Vari-
ous electric motor designs are available for EVs with different
torque, efficiency, and power map [21]. Moreover, the EVs in
contrast to ICE vehicles benefit from the regenerative braking
system to extend their driving range. Using the regenerative
braking system, part of the backward force can be provided by
the electric motor instead of the braking pads. Hence, the main
responsibility of the electric motor is to provide mechanical en-
ergy by consuming electrical energy or convert the mechanical
energy to electrical energy in the regenerative mode. The effi-
ciency of this conversion is mostly dependent on the requested
torque and the electric motor rotation speed.
The tractive force (Ftr) is provided by the electric motor
to overcome the driving forces or the road load forces on the
EV (Frd) to propel the vehicle (mass m) forward at a desired
speed and acceleration (a) [22]. Frd consists of the aerody-
namic drag, the gravitational force, and the rolling resistance:
Frd = Faero + Fgr + Froll (1)
Ftr = Frd +ma (2)
where the aerodynamic drag (Faero) is the viscous resistance
of the air working against the vehicle motion which is quadrat-
ically proportional to the vehicle speed (v). The gravitational
force (Fgr) is the force caused by the gravity and is mainly de-
pendent on the road slope (α). The rolling resistance (Froll) is
produced by the flattening of the tire at the contact surface of
the road which is also dependent on the vehicle speed [9, 10].
The electric motor power consumption (Pe) is calculated as:
Pe =Ftrv
ηm(3)
where ηm represents the electric motor efficiency when con-
verting electrical to mechanical energy in the motor mode and
converting mechanical to electrical energy in the regenerative
mode. ηm is highly dependent on the motor rotational speed
and the generated torque.
The model parameters are adjusted based on the specifica-
tions of the EV, Nissan Leaf [16]. Its driving range and power
consumption have been verified in different conditions by our
model. The dynamic variables such as: the vehicle speed, ac-
celeration, and road slope, are extracted from the drive profile
which models the driving route [9]. It needs to be noted that
the driving behavior is affected not only by the driving route,
but also by the driver’s behavior [15]. However, considering
the driver’s behavior is out of the scope of this paper.
Fig. 2. ADVISOR - Automotive Design, Simulation, and Analysis Tool [23].
Ordinary Differential Equations (ODE) are utilized to model
and estimate the dynamic behavior of the EV, especially the
electric motor power consumption while driving. There are
various design automation tools that implement these equa-
tions (or similar) for simulation, validation, and analysis of the
EVs (see Fig. 2); ADVISOR is a MATLAB/Simulink-based
simulation program for rapid analysis of the performance and
fuel economy [11, 23]. Moreover, AMESim is a commercial
system-level multi-physics automotive design tool [9, 24].
III. HVAC SYSTEM
The HVAC system is monitored and controlled by the au-
tomotive climate control in order to provide the thermal com-
fort for the passengers. There are various methodologies of
automotive climate control in literature with different perfor-
mances in terms of energy consumption and thermal comfort
maintenance for the passengers [25]. Typically, automotive
climate controls attempt to provide uniform thermal environ-
ment for the passengers in the cabin. They are also known
as single-zone automotive climate controls which maintain the
whole cabin temperature in the thermal comfort range around
a target temperature [26–28]. In these methodologies, multi-
ple variables, e.g. the cabin temperature, ambient temperature,
and solar radiation, may be monitored and the HVAC system
is controlled accordingly to cool/heat the cabin [29]. More-
over, the thermal comfort of a human has been modeled using
Predicted Mean Vote (PMV) and the influence of the HVAC
system on this model has been analyzed for different condi-
tions [30]. It has been shown that the passengers may not ex-
perience the most thermal comfort in a uniform thermal envi-
ronment. Hence, other methodologies have been introduced
to provide non-uniform thermal environment for different pas-
sengers in order to improve the thermal comfort and reduce
the energy consumption of the HVAC [25]. These are also
known as multi-zone automotive climate controls which uti-
lize sophisticated ventilation system with Variable Air Volume
(VAV) control. The advantage of these is the precise control of
the temperature, humidity, and airflow for individual passen-
gers (each zone), which may improve the thermal comfort and
reduce the energy consumption significantly.
The HVAC system structure contains variable-speed fans to
provide supply air to the zone(s). There are multiple valve
dampers and blend doors to control the airflow in different
parts of the HVAC system. A valve damper is also used to
control the mix of the outside air and the recirculated air back
into the system. Some HVAC systems utilize a smog sensor to
close off the outside air inlet if it sniffs hydrocarbons or other
bad odors. The heater and cooler in the HVAC system (evapo-
rator, condenser, compressor, etc.) control the air temperature
by exchanging heat. The structure of a single-zone HVAC sys-
tem is shown in Fig. 3.
The thermodynamic and physical behavior of the compo-
nents inside an HVAC system can be modeled using low-
order ODEs. Despite the simplicity (compared to higher-order
thermodynamic equations), the model provides sufficient in-
formation for analyzing the transient behavior of the system.
Moreover, adding more control knobs, sensors, and controlled
zones, to the HVAC system makes the modeling and estima-
tion more complex and challenging. The humidity can be an
important factor affecting the HVAC power consumption, but
it is not typically directly measured or controlled [31]. There-
fore, the temperature represents an equivalent dry air tempera-
ture at which the dry air has the same specific enthalpy as the
actual moist air mixture.
Fig. 3. The Structure and Components of a Single-Zone HVAC in an EV [10].
The temperature inside cabin (zone) (Tz) is influenced by
the supply air (Ts) to the cabin, the heat exchange with out-
side, and the solar radiation. Energy balance equation is used
to describe the thermodynamic behavior of the cabin temper-
ature [10]. The cabin temperature changing is dependent on
thermal capacitance of the air, wall, and seats inside the cabin
(Mc) and the heat capacity of the air (cp). The air flow rate into
the cabin (mz) also influences the temperature changing.
The exchanged heat with outside and the solar radiation are
modeled as thermal loads (Q). The heat exchange through the
walls with outside is proportional to the difference between the
cabin temperature and outside temperature, the heat exchange
coefficient, and the area separating the cabin and outside (Ax).
The solar radiation and outside temperature are time-varying
factors which can be monitored.
The air returned from the cabin is mixed with the outside
air and recirculated back to the system. The fraction of the
returned air from the cabin is dr, which is controlled by a
damper. The returned air temperature is as the same as the
cabin temperature in a single-zone HVAC. Then, the energy
balance in the air mixer gives the temperature of the system
inlet air (Tm).
The power consumption of the HVAC system can be catego-
rized into three parts: 1) cooling power, 2) heating power, and
3) fan power. We consider the cooling and heating power con-
sumption in terms of the energy difference between their inlet
and outlet air flow. Moreover, the heat exchange between the
coolant/evaporator and air is modeled as efficiency parameters:
Ph =cp
ηhmz(Ts − Tc) (4)
Pc =cp
ηcmz(Tm − Tc) (5)
where Pc and Ph are cooling and heating power consumption,
respectively. ηh and ηc are the efficiency parameters describing
the operating characteristics of the heating and cooling pro-
cesses. The fan power consumption (Pf ) is quadratically re-
lated to mz:
Pf = kf (mz)2 (6)
where kf is a parameter that captures the fan efficiency and
the duct pressure losses.
The parameters for the model are set based on an HVAC
specifications [26, 27] and to match the thermodynamic be-
havior in different conditions accurately [20, 32].
IV. ELECTRICAL ENERGY STORAGE
The electrical energy storage is the primary storage in EVs
to provide and store energy [4, 5]. The energy storage in EVs
is typically designed to meet the primary design requirements,
e.g. maximum energy and maximum power request. More-
over, other design constraints on the package, e.g. size, cost,
and volume play a huge role in designing and optimizing the
electrical energy storage [6].
Typically, battery packs are utilized as the primary electri-
cal energy storage in EVs. The battery packs contain multiple
battery modules or cells connected in series or parallel. The
number of connected battery cells and their connections are
optimized based on the design requirements. Moreover, the
battery cells’ electrodes and electrolyte are manufactured using
various materials with different chemical characteristics [33].
Recently, Lithium-ion has been seen as the best material
for the batteries used in EVs due to its high energy density
and sufficient power density. The charge available in the bat-
tery with respect to its available battery capacity is defined as
State-of-Charge (SoC) which changes as the battery charges
or discharges. There are various methodologies to estimate
the SoC of the batteries [34]. Accurate SoC estimation is es-
sential for maintaining the batteries in the safe operation state
and improving the battery lifetime. The major challenges of
SoC estimation are the noise resulted from monitoring the state
and the non-linearity characteristic of the batteries. More-
over, Lithium-ion batteries demonstrate less usable capacity
in higher discharge rates (rate-capacity effect). This charac-
teristic is described using the Peukert’s Law [3, 35]. Hence,
the effective current draining the chemical energy (Ieff) can be
used for SoC estimation. Moreover, Peukert’s constant (pc) is
typically measured empirically for each type of battery [10].
On the other hand, the battery capacity degrades overtime
as the battery ages. The aging is mainly due to the chem-
ical reactions in the battery and increase in the internal re-
sistance of the battery cell. The battery lifetime is measured
as State-of-Health (SoH) which demonstrates the current bat-
tery capacity compared to the rated value. The battery life-
time degradation (▽SoH) is mainly dependent on the battery
stress [12] and is estimated using various methodologies; the
battery stress can be modeled by the utilization behavior of the
battery cell, in other words, the SoC average (SoCavg) and SoC
deviation (SoCdev). SoCdev and SoCavg are calculated based on
a discharging/charging cycle. Typically, charging is conducted
efficiently according to a fixed and specific pattern. Hence,
the influence of the charging cycle on ▽SoH is modeled as
constant parameters. The battery cell capacity decreases with
the rate of ▽SoH. When the battery capacity degrades about
20%, it will be useless [12]. Therefore, the number of dis-
charging/charging cycles it can be used (the battery lifetime),
is dependent on ▽SoH.
Lithium-ion batteries generate internal heat due to the chem-
ical reactions. The heat generated is caused by the power
loss due to the internal resistance or the entropy change in the
ions [11, 36]. Based on the current battery utilization, the gen-
erated heat and the battery temperature can be modeled and
estimated. Moreover, the operating condition of the battery,
e.g. battery temperature, significantly affects the battery life-
time. However, the battery temperature is assumed to be main-
tained and its influence on the battery operation [37] is out of
the scope of this paper.
To validate the models describing the battery behavior, a test
bed hardware can be implemented. The required EV power
consumption/generation is generated by the mentioned design
automation tools and simulators. Then, the test bed hardware
(physical plant) uses the simulated data to emulate the EV
power requests using a programmable DC power supply and
DC load while utilizing a battery pack (scaled-down). The re-
quired battery operating parameters, e.g. current and voltage
are monitored using sensors and a data acquisition device.
V. EXPERIMENT RESULTS AND HVAC ANALYSIS
In the previous sections, the EV electric motor power con-
sumption (see Section II), the HVAC system thermodynamics
and power consumption (see Section III), and battery lifetime
and behavior (see Section IV) are modeled and estimated us-
ing ODEs. The total power consumption of the EV is input to
the battery model and its behavior is estimated and analyzed.
Hence, the HVAC thermodynamics, energy consumption, and
its influence on the battery is analyzed.
The HVAC system is modeled in the continuous-time do-
main. However, the modeling, simulation, and (typically) con-
trolling of the system are done in discrete-time domain. Hence,
the current condition of the HVAC system is modeled using
multiple state variables. Also, the equations modeling the be-
havior of the system need to be discretized according to the
sampling period (△t).
Hence, the automotive climate control monitors the state
variables and adjust the control inputs according to the
methodology. Various methodologies of automotive climate
control exist which define the relationships between the con-
trol inputs and state variables.
For instance, in a simple On/Off methodology [26, 27], the
cabin temperature (the state variable) is monitored and if it is
in an specific range, the heating and cooling get turned off.
However, if the temperature violates the comfort level thresh-
old, the heating or cooling will be turned on based on the tar-
get temperature. In a Fuzzy-Based climate control [28], the
fuzzy rules are designed such that the actuators will settle when
the set-point temperature has been achieved within the com-
fort zone region of the relative humidity and climate. While,
a more complex climate control methodology [10] may utilize
a Model Predictive Control (MPC) to control the HVAC opti-
mally. The MPC algorithm enables the controller to look into
a receding horizon (control window), in each step, for esti-
mating and optimizing the HVAC system variables in order to
minimize a predefined cost function. The cost function can be
the cabin temperature fluctuation, passenger thermal comfort,
HVAC energy consumption, battery lifetime, or combination
of all. Then, the optimized control inputs are applied to the
HVAC system (physical plant) in the next time step. The larger
the control window of the MPC algorithm, more HVAC system
variables are estimated and more dynamics and behaviors are
considered in the optimization. This may benefit us to reach a
solution closer to the global optimum solution.
As in the battery lifetime-aware automotive climate con-
trol [10], the EV electric motor power consumption is esti-
mated using the drive profile input. Then, the HVAC system
variables are estimated for a control window based on the cur-
rent state variable and control inputs. The variables are opti-
mized for minimizing the HVAC power consumption, extend-
ing the battery lifetime by reducing the ▽SoH , and stabiliz-
ing the cabin temperature around a target temperature. The
controller needs to make sure that the physical limits and re-
strictions are met. Hence, the discrete-time equations and the
limits are defined as the control window constraints of the op-
timization algorithm.
The battery lifetime-aware automotive climate control re-
duces the HVAC power consumption when the electric mo-
tor is estimated to consume more. On the other hand, when
the electric motor power consumption is estimated to be low,
there is enough slack for the HVAC to adjust the cabin temper-
ature again or precool/preheat the cabin before the next peak in
power consumption arrives. Therefore, the SoC deviation and
the SoC average in a discharging/charging cycle will decrease
and thereby the battery stress reduces. This will improve the
driving range and the battery lifetime.
The automotive climate controls show different perfor-
mances for various environment conditions. Different climate
(ambient temperature) influences the HVAC power consump-
tion and thereby the battery lifetime and driving range. Fig. 4
illustrates the battery lifetime (SoH) improvement and HVAC
power consumption reduction compared to two other climate
control methodologies for three climate. The battery lifetime-
aware climate control may increase the HVAC power con-
sumption further in order to compensate the electric motor
power consumption for reducing the battery stress and improv-
ing the battery lifetime (as in hot weather).
Fig. 4. Automotive Climate Control Performance in Different Climate.
The driving route can influence the HVAC power consump-
tion, typically due to the driving time. However, in the battery
lifetime-aware automotive climate control, the HVAC power
consumption is optimized considering the electric motor power
requests which is enforced by the driving route. Hence, the cli-
mate control performance may vary for different drive profiles.
Fig. 5 illustrates the battery lifetime improvement compared
to two other methodologies for various standard drive cycles
(drive profiles). It is shown that, as the driving route fluctu-
ation increases, the battery lifetime-aware climate control can
perform better and leverage this fluctuation for further improv-
ing the battery lifetime by reducing the battery stress.
VI. CONCLUDING REMARKS AND CHALLENGES
We have seen that the HVAC system may have a signifi-
cant influence on the EV power consumption, battery lifetime,
and driving range. Moreover, sophisticated automotive climate
control such as battery lifetime-aware automotive climate con-
trol may be implemented in order to improve the battery life-
time and driving range.
The major EV climate control design challenge is the com-
plexity resulted from the integration and interaction between
diverse cyber and physical subsystems. However, the integra-
tion of these subsystems may benefit us to reach a system-level
Fig. 5. Automotive Climate Control Performance for Various Drive Profiles.
global optimum solution to our climate control (e.g. battery
lifetime). An EV climate controller may require sophisticated
modeling and estimation of the physical dynamic behavior of
the HVAC subsystem and other subsystems that might influ-
ence or get influenced by the HVAC subsystem, e.g. battery,
weather, and driving route. Although, the abstraction or com-
plexity of the modeling is important to its validity, it may affect
the control functionality in terms of performance, computation,
and memory resource requirements.
For instance, an MPC controller leverages from multivariate
estimation for constrained optimization based on the physical
model of the system in order to reach stable and optimum so-
lution for the system. However, the MPC controller may suffer
from poor online estimation performance and restricted mem-
ory resource available for all the estimated variable for a reced-
ing horizon [10, 31]. On the other hand, the growing complex-
ity enforces the design exploration of the cyber components’
architecture (e.g. Electronic Control Unit (ECU)) in terms of
performance, reliability, and power consumption. Moreover,
communication architecture functionality is essential for pro-
viding the required monitoring and controllability for the in-
teracting subsystems. The communication architecture defines
the timing and bandwidth parameters of the control and data
paths between subsystems, computing, sensing, and actuating
components [4].
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