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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