Fuzzy Logic Controlled Simulation in Regulating Thermal Comfort and Indoor Air Quality Using a Vehicle Heating, Ventilation, and Air-Conditioning System

Conventional heating ventilation and air-conditioning (HVAC) controllers have been designed to mainly control the temperature of a confined compartment, such as a room or a cabin of a vehicle. Other important parameters related to the thermal comfort and indoor air quality (IAQ) of the confined compartment have often been ignored. In this project, IAQ in the vehicle cabin was represented by its carbon dioxide (CO2) concentration, and the occupants’ thermal comfort levels were estimated with the predicted mean vote (PMV) index. A new fuzzy logic controller (FLC) was designed and developed using the MATLAB fuzzy logic toolbox and Simulink to provide a nonlinear mapping between the measured values, i.e., PMV, temperature, CO2, and control parameters (recirculation flaps, blower’s speed, and refrigerant mass flow rate) of a vehicle HVAC system. The new FLC aimed to regulate both in-cabin PMV and CO2 values without significantly increasing overall energy consumption. To evaluate the effectiveness of the proposed FLC, a cabin simulator was used to mimic the effects of different HVAC variables and indoor/outdoor environmental settings, which represented the in-cabin PMV and IAQ readings. Results demonstrated that the new FLC was effective in regulating the in-cabin PMV level and CO2 concentration, at desirable levels, by adaptively controlling the opening and closing of the recirculation flap based on in-cabin temperature and CO2 readings, while maintaining an average-to-good energy consumption level. The proposed FLC could be applied to a large variety of HVAC systems by utilizing low-cost sensors, without the need to significantly modify the internal design of the HVAC system.


Introduction
While it is expected that 9 out of 10 new cars sold worldwide in 2030 will be electric vehicles, internal combustion engine vehicles will still dominate the road for a few more decades. Regardless of the type of vehicle, the mechanisms of their heating, ventilation, and air-conditioning (HVAC) systems are very similar [1]. In general, the HVAC systems in any vehicle serve to maintain ventilation and the health of the occupants in terms of thermal comfort and vehicle cabin indoor air quality (IAQ). In a conventional HVAC system, the occupant's preferred thermal comfort and IAQ are normally measured in terms of temperature and humidity. Thermal comfort is a subjective expression of an occupant's thermal sensation that is estimated from objective measurements collected from the surrounding environment. Besides temperature and humidity, thermal comfort can further include two other environmental factors, namely, mean radiant temperature and air velocity, and two human factors, namely, clothing insulation (clo) and metabolic equivalent (MET) rate, as mentioned in ASHRAE 55-2021 [2]. This paper aimed to design a vehicle HVAC controller for regulating the thermal comfort of occupants and in-cabin IAQ at desirable levels without causing a significant increase in energy consumption or requiring sophisticated modifications to the HVAC system. As illustrated in Figure 1, the blower, which is controlled by a controller, draws the cabin air or the fresh air from the outdoor environment into the HVAC system for cooling or heating. The recirculation flap controlled by the controller regulates the amount of fresh air entering the vehicle cabin through the HVAC system. The mixture of the cabin air and the fresh air then passes through the cooling subsystem, which reduces their temperatures, and then reaches the blend door which is also known as the temperature control flap. When heating is required, the blend door adjusts the flap such that a portion of the cold air can pass through the heating component. In internal combustion engine vehicles, the heating subsystem has two components, namely, (i) the radiator, which gains heat from the engine, and (ii) the positive temperature coefficient heater (i.e., an electric heater). The process is considered as efficient as when heating is required, the air can be heated up by the waste heat from the engine. Under extremely cold weather, only when the engine is not hot enough to heat the air, the positive temperature coefficient heater will need to be turned on to heat the cold air to the desired temperature level [11].

Fuzzy Logic Controller
A fuzzy logic controller (FLC) has been demonstrated as a good candidate for system control, which imitates the human decision-making processes, i.e., range-to-point or range-to-range control [12]. FLC has relatively low computational complexities for mapping input and output parameters of systems with non-binary natures and non-linear relationships. It is often applied for mapping non-linear input data captured from sensors to desirable control parameters for system components such as actuators. An overview of a typical fuzzy logic-based controller is shown in Figure 2. Fuzzification is used to assign numerical data to fuzzy sets. Fuzzy sets consist of elements that represent the degrees of their membership (i.e., the importance or severity of a reading). In general, the degrees of membership of the fuzzy sets lie between 0 and 1, which are expressed as membership functions. The fuzzy inference then generates outputs from the fuzzified data based on sets of pre-defined rules. Finally, defuzzification maps the outputs of the fuzzy inference process back into their numerical forms. Details on the operation principles of a generic fuzzy controller can be found in [12].  As illustrated in Figure 1, the blower, which is controlled by a controller, draws the cabin air or the fresh air from the outdoor environment into the HVAC system for cooling or heating. The recirculation flap controlled by the controller regulates the amount of fresh air entering the vehicle cabin through the HVAC system. The mixture of the cabin air and the fresh air then passes through the cooling subsystem, which reduces their temperatures, and then reaches the blend door which is also known as the temperature control flap. When heating is required, the blend door adjusts the flap such that a portion of the cold air can pass through the heating component. In internal combustion engine vehicles, the heating subsystem has two components, namely, (i) the radiator, which gains heat from the engine, and (ii) the positive temperature coefficient heater (i.e., an electric heater). The process is considered as efficient as when heating is required, the air can be heated up by the waste heat from the engine. Under extremely cold weather, only when the engine is not hot enough to heat the air, the positive temperature coefficient heater will need to be turned on to heat the cold air to the desired temperature level [11].

Fuzzy Logic Controller
A fuzzy logic controller (FLC) has been demonstrated as a good candidate for system control, which imitates the human decision-making processes, i.e., range-to-point or rangeto-range control [12]. FLC has relatively low computational complexities for mapping input and output parameters of systems with non-binary natures and non-linear relationships. It is often applied for mapping non-linear input data captured from sensors to desirable control parameters for system components such as actuators. An overview of a typical fuzzy logic-based controller is shown in Figure 2. Fuzzification is used to assign numerical data to fuzzy sets. Fuzzy sets consist of elements that represent the degrees of their membership (i.e., the importance or severity of a reading). In general, the degrees of membership of the fuzzy sets lie between 0 and 1, which are expressed as membership functions. The fuzzy inference then generates outputs from the fuzzified data based on sets of pre-defined rules. Finally, defuzzification maps the outputs of the fuzzy inference process back into their numerical forms. Details on the operation principles of a generic fuzzy controller can be found in [12]. A typical vehicle HVAC system ( Figure 1) normally comprises cooling and heating subsystems, a blower, and filter components. The main component of the cooling subsystem is the refrigeration cycle loop, which contains a compressor, condenser, thermostatic expansion valve, and evaporator. As illustrated in Figure 1, the blower, which is controlled by a controller, draws the cabin air or the fresh air from the outdoor environment into the HVAC system for cooling or heating. The recirculation flap controlled by the controller regulates the amount of fresh air entering the vehicle cabin through the HVAC system. The mixture of the cabin air and the fresh air then passes through the cooling subsystem, which reduces their temperatures, and then reaches the blend door which is also known as the temperature control flap. When heating is required, the blend door adjusts the flap such that a portion of the cold air can pass through the heating component. In internal combustion engine vehicles, the heating subsystem has two components, namely, (i) the radiator, which gains heat from the engine, and (ii) the positive temperature coefficient heater (i.e., an electric heater). The process is considered as efficient as when heating is required, the air can be heated up by the waste heat from the engine. Under extremely cold weather, only when the engine is not hot enough to heat the air, the positive temperature coefficient heater will need to be turned on to heat the cold air to the desired temperature level [11].

Fuzzy Logic Controller
A fuzzy logic controller (FLC) has been demonstrated as a good candidate for system control, which imitates the human decision-making processes, i.e., range-to-point or range-to-range control [12]. FLC has relatively low computational complexities for mapping input and output parameters of systems with non-binary natures and non-linear relationships. It is often applied for mapping non-linear input data captured from sensors to desirable control parameters for system components such as actuators. An overview of a typical fuzzy logic-based controller is shown in Figure 2. Fuzzification is used to assign numerical data to fuzzy sets. Fuzzy sets consist of elements that represent the degrees of their membership (i.e., the importance or severity of a reading). In general, the degrees of membership of the fuzzy sets lie between 0 and 1, which are expressed as membership functions. The fuzzy inference then generates outputs from the fuzzified data based on sets of pre-defined rules. Finally, defuzzification maps the outputs of the fuzzy inference process back into their numerical forms. Details on the operation principles of a generic fuzzy controller can be found in [12].  Once the system has been set up, the whole fuzzy logic inferencing process will only involve simple arithmetic operations, which makes it highly desirable for real-time control applications and to be implemented in low-cost low-power microcontrollers. While the majority of existing fuzzy controllers for HVAC systems have been designed for reducing energy consumption, adaptive intelligent controller structures and algorithms that incorporate human thermal comfort are still lacking.

Literature Review
Over the years, vehicle HVAC system control strategies have evolved from rigid rule-based switch control mechanisms to intelligent and adaptive control algorithms such as neural networks, fuzzy logic, reinforcement learning, etc. [13]. Table 1 presents the current HVAC controllers that attempted to regulate the thermal comfort and IAQ inside a vehicle cabin in terms of temperature and relative humidity in an energy-efficient manner. In [14], it has been reported that an HVAC system with a rigid rule-based control strategy, i.e.,on-off switching based on predefined thresholds, is inefficient in regulating non-linear and conflicting parameters such as in-cabin PMV and CO 2 levels. Conventional control mechanisms, such as proportional integral derivative (PID) control, are also not suitable for applications with non-linear external environmental parameters and they can lead to unsatisfactory results in maintaining thermal comfort and IAQ inside the vehicle cabin simultaneously [15]. Behrooz et al. [14] stated that most conventional HVAC systems are equipped with proportional-integral (PI)/proportional-integral-derivative (PID) controllers; however, in some scenarios, more than one controller is required to achieve some nontrivial objectives, and can, consequently, significantly increase the complexity and cost of the implementation [16]. In [17], simulated results showed that an FLC is superior to different PID controllers in terms of response time, performance, and preserving stability. Furthermore, the FLC has reduced overshoot and steady-state error when compared to PID controllers [17]. Nevertheless, FLCs enhanced with machine learning techniques, such as machine learning-based automated fuzzy-set fine-tuning, can deliver even more promising results [14].  Table 1 presents the thermal comfort and IAQ parameters considered in existing works on vehicle HVAC system controllers designed in the past five years. Of these, only a few have considered the PMV index and IAQ simultaneously when designing vehicle HVAC system controllers, while the remaining focused on either thermal comfort or cabin IAQ. Among them, studies that involved air velocity and relative humidity often considered them as constants throughout the simulated or tested period. Moreover, approaches based on machine learning algorithms used in-cabin temperature, as the primary factor, to predict the PMV index of the occupants and, thus, regulated their thermal comfort levels without incorporating other factors. In the literature, only a few of the previous works have considered regulating CO 2 concentration as one of their design objectives. Interestingly, when other IAQ parameters, such as PM and VOCs, have been considered as the parameters to be regulated instead of CO 2 , the corresponding controller would tend to enforce recirculation. This is because recirculation will increase the chance for PM and VOCs to be captured by the filter in the HVAC system. However, such a design can be catastrophic when regulation of CO 2 concentration has become one of the design criteria. Among all the works presented in Table 1, only Wei et al. [5] considered all three IAQ parameters. However, their controller did not control the recirculation flap and, thus, the level of air recirculation in a compartment.
In addition to the limitations presented in Table 1, parameters such as the mean radiant temperature, human metabolic rate, and clothing factors, which are required to calculate the PMV indices, have often been assumed to be constants, as follows: 1.
The mean radiant temperature was assumed to be equal to the cabin temperature; 2.
The occupants could talk, sit, type, read, etc., where the average metabolic rate (MET) was approximated to be between 1.2 to 1.4 MET; 3.
The clothing thermal insulation (clo) was set as less than 1.5 clo as sweaters, suit jackets, and vests were considered not to be worn.
From the literature, regulating occupants' thermal comfort and IAQ inside the cabin in an energy-efficient manner has not been reported. In this paper, we, therefore, customdesigned a smart FLC that could regulate the estimated PMV index of the occupants and the in-cabin CO 2 concentration by performing a sensible trade-off in terms of energy consumption of the HVAC system. Model development, evaluation, and tuning of the controller were conducted using MATLAB Simulink 2022a [24]. On the one hand, to regulate the occupants' desirable thermal comfort, it is essential to control the air velocity and temperature. This can be achieved by controlling the blower, refrigerant flow (cooling), and hot coolant flow (heating). To regulate the cabin IAQ, fresh air from the outside environment needs to be circulated into the vehicle cabin. This can be achieved by controlling the opening and closing of the recirculation flap. The results obtained were analyzed and discussed for further improvement of intelligent HVAC controllers.

Materials and Methods
In this paper, the new FLC was designed to control the blower, heating, and cooling of the HVAC system, the mass flow rate of the refrigerant and hot coolant, and the opening and closing of the recirculation flap in the vehicle HVAC system simultaneously, as shown in Figure 3. The HVAC controller was used to regulate and meet some predefined criteria based on readings and measurements that represented the intrinsic and extrinsic factors of the cabin and its occupant(s).

Materials and Methods
In this paper, the new FLC was designed to control the blower, heating, and cooling of the HVAC system, the mass flow rate of the refrigerant and hot coolant, and the opening and closing of the recirculation flap in the vehicle HVAC system simultaneously, as shown in Figure 3. The HVAC controller was used to regulate and meet some predefined criteria based on readings and measurements that represented the intrinsic and extrinsic factors of the cabin and its occupant(s).

Vehicle HVAC System and Cabin Simulator
Simulink (2022a) was used to model and simulate the vehicle cabin and its HVAC system. The vehicle HVAC system model was developed based on [25,26]. As illustrated in Figure 3, the cabin simulator continuously returned in-cabin environmental variables, namely, PMV index, cabin temperature, air velocity, and CO2 concentration under the inference of different simulated outdoor environmental conditions. Using the parameters obtained from the cabin simulator, the FLC then controlled the blower speed, refrigerant mass flow rate, on/off control of the heating and cooling elements, and the opening and closing of the recirculation flap of the simulated vehicle HVAC system, which was connected to the simulator, to regulate the thermal comfort and IAQ inside the vehicle cabin.

PMV Model
The latest PMV model in ASHRAE Standard 55 [2] was adopted. The PMV model predicted the mean thermal sensation of a group of people in a closed compartment according

Vehicle HVAC System and Cabin Simulator
Simulink (2022a) was used to model and simulate the vehicle cabin and its HVAC system. The vehicle HVAC system model was developed based on [25,26]. As illustrated in Figure 3, the cabin simulator continuously returned in-cabin environmental variables, namely, PMV index, cabin temperature, air velocity, and CO 2 concentration under the inference of different simulated outdoor environmental conditions. Using the parameters obtained from the cabin simulator, the FLC then controlled the blower speed, refrigerant mass flow rate, on/off control of the heating and cooling elements, and the opening and closing of the recirculation flap of the simulated vehicle HVAC system, which was connected to the simulator, to regulate the thermal comfort and IAQ inside the vehicle cabin.

PMV Model
The latest PMV model in ASHRAE Standard 55 [2] was adopted. The PMV model predicted the mean thermal sensation of a group of people in a closed compartment according to (i) two human factors, namely, clothing insulation and metabolic rate, which were based on some typical values obtained from the ASHRAE Standard 55, Tables 5.1 to 5.4 [2]; and (ii) four environmental factors, namely, cabin air temperature, mean radiant temperature, air velocity, and relative humidity, which were obtained from the cabin simulator.

Temperature and CO 2 Models
The temperature model used in the simulator was designed to dynamically update the cabin air temperature based on the heat exchange between the simulated cabin and the environment, the number of occupants in the cabin, and the CO 2 concentration and moisture content generated by the occupants. The heat exchange process was simulated with convective and conductive heat transfer models.
Each occupant was assumed to generate 0.04 g/s of water vapor throughout the simulation period. The outside environment air was assumed to have a CO 2 concentration of 400 ppm [27]. The CO 2 concentration generated by the human body was a function of oxygen consumption level, respiratory system, and human metabolic rate [28,29]. The CO 2 exhaled by a passenger inside the vehicle cabin was assumed to be 0.01 g/s, as that in [28,29]. An overview of the temperature and CO 2 models implemented in MATLAB Simulink is shown in Figure 4.
Each occupant was assumed to generate 0.04 g/s of water vapor throughout the simulation period. The outside environment air was assumed to have a CO2 concentration of 400 ppm [27]. The CO2 concentration generated by the human body was a function of oxygen consumption level, respiratory system, and human metabolic rate [28,29]. The CO2 exhaled by a passenger inside the vehicle cabin was assumed to be 0.01 g/s, as that in [28,29]. An overview of the temperature and CO2 models implemented in MATLAB Simulink is shown in Figure 4.

Fuzzy Control Architecture
The proposed FLC was designed to regulate the thermal comfort, in terms of PMV index, and allowed the PMV to fluctuate between −1 and +1, the cabin indoor temperature to be regulated between 21 and 25 °C, and CO2 concentration to be maintained below 650 ppm without significantly increasing the overall energy consumption of the HVAC system.

Fuzzy Control Architecture
The proposed FLC was designed to regulate the thermal comfort, in terms of PMV index, and allowed the PMV to fluctuate between −1 and +1, the cabin indoor temperature to be regulated between 21 and 25 • C, and CO 2 concentration to be maintained below 650 ppm without significantly increasing the overall energy consumption of the HVAC system.

Input and Output Variables
In the simulation model, PMV, CO 2 , and cabin temperature were the input variables ( Table 2) being fed into the FLC. These input variables were specified as multiple membership functions, where each input was mapped to a value between 0 and 1. These membership functions quantified the degree or level of the input elements in the fuzzy set. The output variables of the FLC were mapped to the control variables of the HVAC system (Table 2), namely, the blower velocity, the mass flow rate of the refrigerant (compressor control) and hot coolant, and the recirculation flap. The output variables were specified from 0 (representing the component was turned off or operating at the minimum level) to 1 (indicating the fully turned on or operating at the highest level).

Linguistic Variables
In fuzzy logic, the degree or level of a variable was first mapped to a linguistic variable. The input and output linguistic variables are shown in Tables 3 and 4, respectively.

Membership Functions
The membership functions are technically the mathematical functions that mapped the linguistic variables into membership degrees with values between 0 and 1 to represent their level of importance or severity. The ranges of the linguistic variables were specified based on the threshold limits specified in Table 4.
In this paper, triangular and trapezoidal membership functions were used in the fuzzy controller. The shape of a triangular membership function was defined by its lower limit (l), an upper limit (u), and a value (m) which represented where the function peaks out, thus l < m < u. Similarly, for the trapezoidal membership function, its shape was defined by its lower limit (l), the upper limit (u), a lower support limit (l s ), and an upper support limit (u s ) defined as l < l s < u <u s . The triangular and trapezoidal membership functions were chosen for their low computational complexities and ease of implementation [31,32]. The trapezoidal membership functions were used to represent the extreme threshold ranges at two ends, while triangular membership functions were used to represent threshold ranges in between. Table 4 has been deduced from Tables 2 and 3. In Table 4, according to the CO 2 concentration accumulated inside the cabin, the opening and closing of the recirculation flap will be executed. When the FLC executes as Low, the recirculation flap will be opened by 25%. Similarly, when it is High, the recirculation flap will be fully open. For the mass flow rate of the refrigerant and coolant, since the total mass flow rate is specified in the evaporator, when the FLC executes a linguistic variable value, the mass flow rate in the HVAC system will be the product of the output value and the total mass flow rate.

Fuzzy Rules
The primary component of the fuzzy logic system was the fuzzy rules. It was crucial to define the fuzzy rules properly otherwise the objective of the controller would not be achieved. The fuzzy rules were designed with a primary function to regulate in-cabin PMV and CO 2 levels within the pre-defined desirable ranges. Its secondary function was to achieve that without overspending energy on the blower and the cooling unit. To achieve that, a set of 140 rules were defined for the FLC. Some examples are listed as follows: 1.
If PMV is Cool AND cabin temperature is Cold AND CO 2 concentration is High, THEN blower velocity is High, AND Heating is turned ON AND recirculation flap opening is Large.

2.
If PMV is Hot AND cabin temperature is Hot AND CO 2 concentration is High, THEN blower velocity is High, AND Cooling is turned ON AND recirculation flap opening is Large.

3.
If PMV is Comfort AND cabin temperature is Comfort AND CO 2 concentration is Low, THEN blower velocity is OFF, AND HVAC is turned OFF AND recirculation flap opening is Small.

Input Parameters
The proposed fuzzy controller was implemented using the fuzzy logic toolbox in MATLAB Simulink. In this paper, summer temperature data were chosen for evaluating the proposed fuzzy controller, as it required the controller to trade off between its cooling outcome and energy consumption. In the simulation model, PMV, CO 2 , and cabin temperature were the input variables for the FLC. These input variables were specified as membership functions, where each element of the input was mapped to a value between 0 and 1 (see Appendix A for further explanation).
The outdoor temperature fluctuation was simulated as a sinusoidal function, with its max (40 • C), min (15 • C), and mean (27.5 • C) temperatures obtained from the actual data of Melbourne, Australia, in the summer of 2021 [33]. The lowest and highest temperatures recorded were 15.9 • C and 39.5 • C, respectively. An outdoor environment with a relative humidity of 50% was considered throughout the simulation period. The simulation was performed by assuming that the volume of the cabin is 3 m 3 , the airflow duct area is 0.0150 m 2 , and the mass flow rate of the refrigerant and coolant is 0.2 kg/s with a heat transfer rate of 6.5 kW and 4.8 kW, respectively [25,26]. Additionally, the refrigerant entering the evaporator is assumed as 4 • C, and the engine coolant entering the heater radiator is 90 • C throughout the simulation [25]. In the first stage, the velocity of air inside the cabin is the measure of blower air velocity. In addition to that, engine heat transfer and solar radiation through the windshield have been ignored in this simulation. The heat generated by the occupants, convection, and conductive heat transfer between in-cabin and outdoor environments has been considered. The simulation was performed by further assuming the average metabolic rate between 1.2 to 1.4 MET and the clothing insulation was assumed less than 1.5 clo (ASHRAE 55 Table 5.1 and 5.3 [2]). In addition, when the recirculation flap was closed, the vehicle cabin behaved as a sealed compartment where the leakage was negligible, such that no leakage occurred during the simulation except for the heat loss through conduction and convection.

Simulation Testing Conditions
The proposed FLC was designed to regulate the thermal comfort in terms of PMV and the cabin IAQ in terms of CO 2 concentration as follows: 1.
Two scenarios with different numbers of occupants (one and three) inside the vehicle cabin were simulated to investigate the effect of different numbers of occupants and to determine the adaptiveness of the proposed smart HVAC controller.
Furthermore, for comparison purposes, two other FLC controllers with recirculation flaps being open or closed at all times were introduced in the simulations to investigate the importance of incorporating CO 2 concentration regulation in HVAC controller design.

Energy Consumption Measurement
In a conventional HVAC system, the design of the compressor parameters for controlling the refrigerant flow was driven by the HVAC load capacity. In the simulation, the energy consumption of the HVAC system was indicated using the average mass flow rate of the refrigerant in terms of kilogram per second (kg/s). The energy consumption for the heater was neglected as only waste heat from the engine was utilized during the summer season to heat the cabin air.

PMV Results
In Figures 5 and 6, when the recirculation flap was forced to be opened during simulation, it was observed that the PMV index started rising drastically at t = 300 s reached the maximum threshold, i.e., PMV = 3. In Figures 7 and 8, when the outdoor t perature started to decrease, the PMV index began to reduce at t = 1200 s.      For the other two simulation setup conditions, i.e., when the recirculation flap was either forced to be closed or the recirculation flap controlled by the proposed FLC, the PMV index was observed to be regulated between the predefined threshold values between −1 and +1.
Since cabin temperature was a key parameter of the PMV index, as shown in Figures  6 and 9, when the recirculation flap was forced to be always open, the indoor cabin temperature waveform shadows the outdoor temperature. Again, in Figures 6 and 9, under  For the other two simulation setup conditions, i.e., when the recirculation flap was either forced to be closed or the recirculation flap controlled by the proposed FLC, the PMV index was observed to be regulated between the predefined threshold values between −1 and +1.
Since cabin temperature was a key parameter of the PMV index, as shown in Figures  6 and 9, when the recirculation flap was forced to be always open, the indoor cabin temperature waveform shadows the outdoor temperature. Again, in Figures 6 and 9, under the FLC-regulated and recirculation flap closed conditions, their in-cabin temperature was When designing intelligent HVAC system controllers, previous studies [18][19][20][21] have only considered cabin temperature as the primary factor to measure thermal comfort and cabin IAQ. In this paper, cabin temperature, relative humidity, and air velocity were considered as variable input factors to predict the PMV index based on the simulated outdoor environmental parameters. At the same time, human metabolic rate and clothing insulation were designed to change manually during the simulation. The results (Figures 5 and  6) demonstrated that our novel approach was successful in maintaining the thermal comfort range by regulating the PMV index between −1 and +1.  As expected, for the setup with the recirculation flap forced to be always closed, the CO2 concentration kept increasing monotonically throughout the simulation period, while for the other two setups, the CO2 concentration was regulated below the predefined maximum threshold of 650 ppm. It was also observed that the proposed FLC could regulate the in-cabin CO2 concentration close to the setup with the recirculation flap forced to be always open. From Figure 9, when with one occupant presence, CO2 concentration was regulated at 430 ppm when the recirculation flap was forced open and at 528 ppm in the  When designing intelligent HVAC system controllers, previous studies [18][19][20][21] have only considered cabin temperature as the primary factor to measure thermal comfort and cabin IAQ. In this paper, cabin temperature, relative humidity, and air velocity were considered as variable input factors to predict the PMV index based on the simulated outdoor environmental parameters. At the same time, human metabolic rate and clothing insulation were designed to change manually during the simulation. The results (Figures 5 and  6) demonstrated that our novel approach was successful in maintaining the thermal comfort range by regulating the PMV index between −1 and +1.  As expected, for the setup with the recirculation flap forced to be always closed, the CO2 concentration kept increasing monotonically throughout the simulation period, while for the other two setups, the CO2 concentration was regulated below the predefined maximum threshold of 650 ppm. It was also observed that the proposed FLC could regulate the in-cabin CO2 concentration close to the setup with the recirculation flap forced to be always open. From Figure 9, when with one occupant presence, CO2 concentration was regulated at 430 ppm when the recirculation flap was forced open and at 528 ppm in the

PMV Results
In Figures 5 and 6, when the recirculation flap was forced to be opened during the simulation, it was observed that the PMV index started rising drastically at t = 300 s and reached the maximum threshold, i.e., PMV = 3. In Figures 7 and 8, when the outdoor temperature started to decrease, the PMV index began to reduce at t = 1200 s.
For the other two simulation setup conditions, i.e., when the recirculation flap was either forced to be closed or the recirculation flap controlled by the proposed FLC, the PMV index was observed to be regulated between the predefined threshold values between −1 and +1.
Since cabin temperature was a key parameter of the PMV index, as shown in Figures 6 and 9, when the recirculation flap was forced to be always open, the indoor cabin temperature waveform shadows the outdoor temperature. Again, in Figures 6 and 9, under the FLCregulated and recirculation flap closed conditions, their in-cabin temperature was regulated between 21 and 24 • C as intended.
When designing intelligent HVAC system controllers, previous studies [18][19][20][21] have only considered cabin temperature as the primary factor to measure thermal comfort and cabin IAQ. In this paper, cabin temperature, relative humidity, and air velocity were considered as variable input factors to predict the PMV index based on the simulated outdoor environmental parameters. At the same time, human metabolic rate and clothing insulation were designed to change manually during the simulation. The results (Figures 5 and 6) demonstrated that our novel approach was successful in maintaining the thermal comfort range by regulating the PMV index between −1 and +1. Figures 9 and 10 show the CO 2 concentration results of the proposed fuzzy HVAC controller versus its counterparts with one virtual occupant and three virtual occupants, respectively.

CO 2 Concentration
As expected, for the setup with the recirculation flap forced to be always closed, the CO 2 concentration kept increasing monotonically throughout the simulation period, while for the other two setups, the CO 2 concentration was regulated below the predefined maximum threshold of 650 ppm. It was also observed that the proposed FLC could regulate the in-cabin CO 2 concentration close to the setup with the recirculation flap forced to be always open. From Figure 9, when with one occupant presence, CO 2 concentration was regulated at 430 ppm when the recirculation flap was forced open and at 528 ppm in the system controlled by the proposed FLC. Similar results were observed in Figure 10 when three occupants were present. The FLC regulated with the recirculation flap was forced to be open, and the CO 2 concentration was regulated at 490 ppm and 615 ppm for setups with one and three occupants, respectively.
Studies by Russi et al. [8,9] traded off energy efficiency for regulating the CO 2 concentration to maintain better cabin IAQ. We developed a fuzzy logic-controlled HVAC system to address the issue. Table 5 demonstrated that the occupant's thermal comfort and cabin IAQ could be regulated with the proposed FLC without causing a significant increase in the energy consumption indicator, which was estimated using the refrigerant mass flow rate (kg/s). The results ( Table 5) further showed that the mass flow rate values were 1 2 times higher when compared with the recirculation flap closed condition and 9/10 times lower when compared with the recirculation flap open. As shown in Figures 5-10, the proposed FLC, which was designed and developed using the fuzzy logic toolbox in Simulink, has successfully regulated the PMV index and CO 2 concentration within their predefined desirable thresholds.
In the simulation setup with the recirculation flap always closed and with one occupant, at t = 2450 s ( Figure 5), the PMV index reduced relatively slower than the other two setups (i.e., FLC-regulated and recirculation flap always closed) when the external temperature started to drop. This was due to the in-cabin air continuing to recirculate regardless of the changes in the outdoor temperature. Therefore, the only possible way for the in-cabin air to be cooled down was through convective and conductive heat transfer, which was a relatively slow process. As such, when t was between 2450 s and 3350 s, even when the outdoor temperature increased rapidly from 15 to 20 • C, the in-cabin temperature reacted slowly as the vehicle compartment was assumed to be perfectly sealed. In contrast, for the other two setups, outdoor air was allowed to flow in, and as the outdoor air at that instance was relatively cold (15 • C to 20 • C), that cooled down the in-cabin temperature relatively quickly. As a result, their PMV values could be regulated around 0 during that simulated period. Furthermore, in Figure 7, during that simulated period, when the in-cabin temperature went below 21 • C, the blend door was opened to allow some amount of air to pass through the HVAC radiator to increase the temperature such that the in-cabin temperature was regulated at the desirable level, which explained why the in-cabin temperature and, thus, the PMV index, did not reduce further. When the recirculation flap was always closed and with three occupants (Figure 6), the PMV index was regulated at 0.5 with negligible fluctuations of +/−0.1. Even though the cabin was cooled down due to convective and conductive heat transfers, such a process was slow. Nevertheless, there was extra heat generated by the two new additional occupants, which caused the in-cabin temperature and, thus, the PMV index, to increase.
In a conventional vehicle cabin, even when the cabin is assumed to be completely sealed, there will be leakage from the indoor to an outdoor environment. In the simulated results, the PMV index was analyzed with +/-0.1 tolerance. In the future, during experimental validation, the tolerance for the PMV index can be determined using standard deviation.
When the recirculation flap was closed, there was no alternative channel for the incabin air to escape and the CO 2 concentration started to accumulate and reach an alarming level (Figures 9 and 10). The increase in CO 2 concentration was due to the presence of occupants who generated CO 2 at a rate of 0.01 g/s per person. In reality, the vehicle cabin is not perfectly sealed, and such exceeding high values as shown in the results should not be realistic. However, with the recirculation flap closed, the in-cabin CO 2 concentration could still significantly increase to a level that could cause drowsiness or other health hazards to the occupants. Hence, to avoid such circumstances, CO 2 concentration should be monitored and regulated below a desirable threshold of 650 ppm. The proposed FLC was able to regulate the in-cabin CO 2 concentration below such a limit. Moreover, the proposed FLC could regulate the CO 2 concentration closely as that in the setup with the recirculation flap always open. The results demonstrated that the proposed FLC was effective in regulating the in-cabin CO 2 concentration by adaptively controlling the opening and closing of the recirculation flap according to in-cabin temperature and CO 2 readings.

Energy Consumption Measurement
In the first stage, the average area under the curve of the product of the HVAC system on-time (1-On, 0-Off) and its corresponding refrigerant mass flow rate (% of the max mass flow rate) was used as an indicator to estimate the HVAC system energy consumption. In conventional vehicles, the energy consumption will be measured based on the power utilized by the blower fan, compressor, and electric heater. The energy consumption was evaluated under the influence of different extrinsic factors and different controllers in different test conditions. The evaluation was used to compare the energy efficiency and the effectiveness of the proposed FLC versus its counterparts in regulating PMV and CO 2 levels.
The corresponding average area under the curve (in kg/s) is tabulated in Table 5. It was observed that when the recirculation flap was forced to be always open, its corresponding indicator could be higher than those in setups with the proposed FLC and the recirculation flap always closed, by an order of magnitude. In contrast, the refrigerant mass flow rates for the system with the proposed FLC and recirculation flap always closed were lower, thus showing that energy consumption could be lower if the recirculation flap was partially or completely closed.

Performance Indicators
Ideally, an HVAC controller should aim to regulate PMV within the comfort range around PMV = 0 and to keep CO 2 concentration lower than 650 ppm. Therefore, in this paper, the root mean square error (RMSE) was used to evaluate the performance of the HVAC controllers in achieving the comfort range, whereas the mean average error (MAE) was used for quantifying their CO 2 regulating performances. The RMSE (Table 6) measured the deviation of the PMV index from the absolute thermal comfort (i.e., PMV index = 0) under various testing setups and conditions. The MAE (Table 7) measured the deviation of the CO 2 concentration above the baseline concentration presented in the outdoor environment air (i.e., 400 ppm). From the results, it was observed that the proposed FLC has a similar performance in regulating PMV as that of the setup with the recirculation flap always closed. The RMSE value was almost 1/3 of that of the setup with the recirculation flat always open. Regarding the MAE for in-cabin CO 2 , as expected, the setup with the recirculation flap always open yielded the lowest value. While the proposed FLC was five times higher, nonetheless, its corresponding ppm value was still below the target threshold of 650 ppm. In contrast, the setup with the recirculation flap always closed has an exceedingly high value of MAE for CO 2 . In Table 8, the proposed FLC was compared with other controller setups in regulating thermal comfort and in-cabin IAQ, together with their energy consumption indicators. To ease comparisons, each controller was assessed using a star rating system. The rating was intended to visualize the relative performances of the controllers in different aspects. It can be seen that when the recirculation flap was forced to be closed at all times, the PMV index and cabin temperature were regulated within the occupant's thermal comfort threshold ranges. In return, the cabin IAQ degraded in terms of CO 2 concentration as it started accumulating above the occupant's safety threshold limit. When the recirculation flap was forced to be always open, a better cabin IAQ was achieved in terms of CO 2 concentration. As shown in Table 8, the proposed FLC showed significantly improved results in regulating thermal comfort and cabin IAQ with average energy consumption in terms of mass flow rate when compared with the other two setups that lacked control of air recirculation. This indicated the importance of incorporating CO 2 concentration in the controller design process. In Table 8, the proposed FLC was compared with other controller setups in ing thermal comfort and in-cabin IAQ, together with their energy consumption ind To ease comparisons, each controller was assessed using a star rating system. Th was intended to visualize the relative performances of the controllers in different It can be seen that when the recirculation flap was forced to be closed at all times, t index and cabin temperature were regulated within the occupant's thermal threshold ranges. In return, the cabin IAQ degraded in terms of CO2 concentrat started accumulating above the occupant's safety threshold limit. When the recir flap was forced to be always open, a better cabin IAQ was achieved in terms of C centration. As shown in Table 8, the proposed FLC showed significantly improved in regulating thermal comfort and cabin IAQ with average energy consumption of mass flow rate when compared with the other two setups that lacked contr recirculation. This indicated the importance of incorporating CO2 concentratio controller design process. From the energy consumption indicator, it was observed that the propos could achieve a reasonable tradeoff between regulating PMV and CO2 concentratio maintaining an efficient level of energy consumption. Although the energy consu indicator was higher than that of the setup with the recirculation flap closed, it co regulate the in-cabin CO2 concentrations at the desirable level, which its counter circulation flap always closed) failed to do. When compared to the setup with the lation flap open, the proposed FLC only consumed 1/5 of the total energy (in term indicator) and it was able to regulate the PMV and CO2 within the predefined d levels.

Future Work
This was preliminary work to study the importance of incorporating CO2 co tion and PMV in the design of vehicle HVAC controllers. FLC mechanisms were  In Table 8, the proposed FLC was compared with other c ing thermal comfort and in-cabin IAQ, together with their ener To ease comparisons, each controller was assessed using a sta was intended to visualize the relative performances of the con It can be seen that when the recirculation flap was forced to be index and cabin temperature were regulated within the oc threshold ranges. In return, the cabin IAQ degraded in terms started accumulating above the occupant's safety threshold lim flap was forced to be always open, a better cabin IAQ was ach centration. As shown in Table 8, the proposed FLC showed sig in regulating thermal comfort and cabin IAQ with average en of mass flow rate when compared with the other two setup recirculation. This indicated the importance of incorporating controller design process. From the energy consumption indicator, it was observ could achieve a reasonable tradeoff between regulating PMV an maintaining an efficient level of energy consumption. Althoug indicator was higher than that of the setup with the recirculati regulate the in-cabin CO2 concentrations at the desirable level circulation flap always closed) failed to do. When compared to lation flap open, the proposed FLC only consumed 1/5 of the t indicator) and it was able to regulate the PMV and CO2 withi levels.

Future Work
This was preliminary work to study the importance of inc tion and PMV in the design of vehicle HVAC controllers. FLC  In Table 8, the proposed FLC was com ing thermal comfort and in-cabin IAQ, toget To ease comparisons, each controller was a was intended to visualize the relative perfo It can be seen that when the recirculation fla index and cabin temperature were regul threshold ranges. In return, the cabin IAQ started accumulating above the occupant's flap was forced to be always open, a better centration. As shown in Table 8, the propos in regulating thermal comfort and cabin IA of mass flow rate when compared with th recirculation. This indicated the importan controller design process. From the energy consumption indica could achieve a reasonable tradeoff between maintaining an efficient level of energy con indicator was higher than that of the setup regulate the in-cabin CO2 concentrations at circulation flap always closed) failed to do. lation flap open, the proposed FLC only con indicator) and it was able to regulate the P levels.

Future Work
This was preliminary work to study th tion and PMV in the design of vehicle HVA  In Table 8, the proposed FLC was com ing thermal comfort and in-cabin IAQ, toge To ease comparisons, each controller was was intended to visualize the relative perfo It can be seen that when the recirculation fl index and cabin temperature were regu threshold ranges. In return, the cabin IAQ started accumulating above the occupant's flap was forced to be always open, a bette centration. As shown in Table 8, the propos in regulating thermal comfort and cabin IA of mass flow rate when compared with t recirculation. This indicated the importan controller design process. From the energy consumption indic could achieve a reasonable tradeoff betwee maintaining an efficient level of energy co indicator was higher than that of the setup regulate the in-cabin CO2 concentrations a circulation flap always closed) failed to do lation flap open, the proposed FLC only co indicator) and it was able to regulate the P levels.

Future Work
This was preliminary work to study t tion and PMV in the design of vehicle HVA  In Table 8, the proposed FLC was compared with other controller setups in regulating thermal comfort and in-cabin IAQ, together with their energy consumption indicators. To ease comparisons, each controller was assessed using a star rating system. The rating was intended to visualize the relative performances of the controllers in different aspects. It can be seen that when the recirculation flap was forced to be closed at all times, the PMV index and cabin temperature were regulated within the occupant's thermal comfort threshold ranges. In return, the cabin IAQ degraded in terms of CO2 concentration as it started accumulating above the occupant's safety threshold limit. When the recirculation flap was forced to be always open, a better cabin IAQ was achieved in terms of CO2 concentration. As shown in Table 8, the proposed FLC showed significantly improved results in regulating thermal comfort and cabin IAQ with average energy consumption in terms of mass flow rate when compared with the other two setups that lacked control of air recirculation. This indicated the importance of incorporating CO2 concentration in the controller design process. From the energy consumption indicator, it was observed that the proposed FLC could achieve a reasonable tradeoff between regulating PMV and CO2 concentration while maintaining an efficient level of energy consumption. Although the energy consumption indicator was higher than that of the setup with the recirculation flap closed, it could still regulate the in-cabin CO2 concentrations at the desirable level, which its counterpart (recirculation flap always closed) failed to do. When compared to the setup with the recirculation flap open, the proposed FLC only consumed 1/5 of the total energy (in terms of the indicator) and it was able to regulate the PMV and CO2 within the predefined desirable levels.  In Table 8, the proposed FLC was compared with other controller setups in ing thermal comfort and in-cabin IAQ, together with their energy consumption in To ease comparisons, each controller was assessed using a star rating system. T was intended to visualize the relative performances of the controllers in differen It can be seen that when the recirculation flap was forced to be closed at all times, index and cabin temperature were regulated within the occupant's thermal threshold ranges. In return, the cabin IAQ degraded in terms of CO2 concentra started accumulating above the occupant's safety threshold limit. When the reci flap was forced to be always open, a better cabin IAQ was achieved in terms of centration. As shown in Table 8, the proposed FLC showed significantly improve in regulating thermal comfort and cabin IAQ with average energy consumption of mass flow rate when compared with the other two setups that lacked cont recirculation. This indicated the importance of incorporating CO2 concentratio controller design process. From the energy consumption indicator, it was observed that the propo could achieve a reasonable tradeoff between regulating PMV and CO2 concentrati maintaining an efficient level of energy consumption. Although the energy cons indicator was higher than that of the setup with the recirculation flap closed, it c regulate the in-cabin CO2 concentrations at the desirable level, which its counter circulation flap always closed) failed to do. When compared to the setup with the lation flap open, the proposed FLC only consumed 1/5 of the total energy (in term indicator) and it was able to regulate the PMV and CO2 within the predefined levels.  In Table 8, the proposed FLC was com ing thermal comfort and in-cabin IAQ, toget To ease comparisons, each controller was a was intended to visualize the relative perfo It can be seen that when the recirculation fla index and cabin temperature were regul threshold ranges. In return, the cabin IAQ started accumulating above the occupant's flap was forced to be always open, a better centration. As shown in Table 8, the propos in regulating thermal comfort and cabin IA of mass flow rate when compared with th recirculation. This indicated the importan controller design process. From the energy consumption indica could achieve a reasonable tradeoff between maintaining an efficient level of energy con indicator was higher than that of the setup regulate the in-cabin CO2 concentrations at circulation flap always closed) failed to do. lation flap open, the proposed FLC only con indicator) and it was able to regulate the P levels.  In Table 8, the proposed FLC was compared with othe ing thermal comfort and in-cabin IAQ, together with their en To ease comparisons, each controller was assessed using a was intended to visualize the relative performances of the c It can be seen that when the recirculation flap was forced to index and cabin temperature were regulated within the threshold ranges. In return, the cabin IAQ degraded in ter started accumulating above the occupant's safety threshold flap was forced to be always open, a better cabin IAQ was centration. As shown in Table 8, the proposed FLC showed in regulating thermal comfort and cabin IAQ with average of mass flow rate when compared with the other two set recirculation. This indicated the importance of incorporat controller design process. From the energy consumption indicator, it was obse could achieve a reasonable tradeoff between regulating PMV maintaining an efficient level of energy consumption. Altho indicator was higher than that of the setup with the recircu regulate the in-cabin CO2 concentrations at the desirable le circulation flap always closed) failed to do. When compared lation flap open, the proposed FLC only consumed 1/5 of th indicator) and it was able to regulate the PMV and CO2 w levels.  In Table 8, the proposed FLC was compared with other controller setups in ing thermal comfort and in-cabin IAQ, together with their energy consumption ind To ease comparisons, each controller was assessed using a star rating system. Th was intended to visualize the relative performances of the controllers in different It can be seen that when the recirculation flap was forced to be closed at all times, t index and cabin temperature were regulated within the occupant's thermal threshold ranges. In return, the cabin IAQ degraded in terms of CO2 concentrat started accumulating above the occupant's safety threshold limit. When the recir flap was forced to be always open, a better cabin IAQ was achieved in terms of C centration. As shown in Table 8, the proposed FLC showed significantly improved in regulating thermal comfort and cabin IAQ with average energy consumption of mass flow rate when compared with the other two setups that lacked contr recirculation. This indicated the importance of incorporating CO2 concentratio controller design process. From the energy consumption indicator, it was observed that the propos could achieve a reasonable tradeoff between regulating PMV and CO2 concentratio maintaining an efficient level of energy consumption. Although the energy consu indicator was higher than that of the setup with the recirculation flap closed, it co regulate the in-cabin CO2 concentrations at the desirable level, which its counter circulation flap always closed) failed to do. When compared to the setup with the lation flap open, the proposed FLC only consumed 1/5 of the total energy (in term  In Table 8, the proposed FLC was compared with other c ing thermal comfort and in-cabin IAQ, together with their ener To ease comparisons, each controller was assessed using a sta was intended to visualize the relative performances of the con It can be seen that when the recirculation flap was forced to be index and cabin temperature were regulated within the oc threshold ranges. In return, the cabin IAQ degraded in terms started accumulating above the occupant's safety threshold lim flap was forced to be always open, a better cabin IAQ was ach centration. As shown in Table 8, the proposed FLC showed sig in regulating thermal comfort and cabin IAQ with average en of mass flow rate when compared with the other two setup recirculation. This indicated the importance of incorporating controller design process.  Table 8, the proposed FLC was compared with other controller setups ing thermal comfort and in-cabin IAQ, together with their energy consumption To ease comparisons, each controller was assessed using a star rating system. was intended to visualize the relative performances of the controllers in differe It can be seen that when the recirculation flap was forced to be closed at all time index and cabin temperature were regulated within the occupant's therm threshold ranges. In return, the cabin IAQ degraded in terms of CO2 concent started accumulating above the occupant's safety threshold limit. When the re flap was forced to be always open, a better cabin IAQ was achieved in terms o centration. As shown in Table 8, the proposed FLC showed significantly impro in regulating thermal comfort and cabin IAQ with average energy consumptio of mass flow rate when compared with the other two setups that lacked co recirculation. This indicated the importance of incorporating CO2 concentra controller design process. From the energy consumption indicator, it was observed that the prop could achieve a reasonable tradeoff between regulating PMV and CO2 concentr maintaining an efficient level of energy consumption. Although the energy co indicator was higher than that of the setup with the recirculation flap closed, i regulate the in-cabin CO2 concentrations at the desirable level, which its coun circulation flap always closed) failed to do. When compared to the setup with t lation flap open, the proposed FLC only consumed 1/5 of the total energy (in t  In Table 8, the prop ing thermal comfort and To ease comparisons, ea was intended to visualiz It can be seen that when index and cabin tempe threshold ranges. In retu started accumulating ab flap was forced to be alw centration. As shown in in regulating thermal co of mass flow rate when recirculation. This indic controller design proces In Table 8, the proposed FLC was compared with other controller setups in regulating thermal comfort and in-cabin IAQ, together with their energy consumption indicators. To ease comparisons, each controller was assessed using a star rating system. The rating was intended to visualize the relative performances of the controllers in different aspects. It can be seen that when the recirculation flap was forced to be closed at all times, the PMV index and cabin temperature were regulated within the occupant's thermal comfort threshold ranges. In return, the cabin IAQ degraded in terms of CO2 concentration as it started accumulating above the occupant's safety threshold limit. When the recirculation flap was forced to be always open, a better cabin IAQ was achieved in terms of CO2 concentration. As shown in Table 8, the proposed FLC showed significantly improved results in regulating thermal comfort and cabin IAQ with average energy consumption in terms of mass flow rate when compared with the other two setups that lacked control of air recirculation. This indicated the importance of incorporating CO2 concentration in the controller design process. From the energy consumption indicator, it was observed that the proposed FLC could achieve a reasonable tradeoff between regulating PMV and CO2 concentration while maintaining an efficient level of energy consumption. Although the energy consumption indicator was higher than that of the setup with the recirculation flap closed, it could still regulate the in-cabin CO2 concentrations at the desirable level, which its counterpart (re-  In Table 8, the proposed FLC was compared with other controller setups in regulating thermal comfort and in-cabin IAQ, together with their energy consumption indicators. To ease comparisons, each controller was assessed using a star rating system. The rating was intended to visualize the relative performances of the controllers in different aspects. It can be seen that when the recirculation flap was forced to be closed at all times, the PMV index and cabin temperature were regulated within the occupant's thermal comfort threshold ranges. In return, the cabin IAQ degraded in terms of CO2 concentration as it started accumulating above the occupant's safety threshold limit. When the recirculation flap was forced to be always open, a better cabin IAQ was achieved in terms of CO2 concentration. As shown in Table 8, the proposed FLC showed significantly improved results in regulating thermal comfort and cabin IAQ with average energy consumption in terms of mass flow rate when compared with the other two setups that lacked control of air recirculation. This indicated the importance of incorporating CO2 concentration in the controller design process. From the energy consumption indicator, it was observed that the proposed FLC could achieve a reasonable tradeoff between regulating PMV and CO2 concentration while maintaining an efficient level of energy consumption. Although the energy consumption indicator was higher than that of the setup with the recirculation flap closed, it could still regulate the in-cabin CO2 concentrations at the desirable level, which its counterpart (re-   Table 8, the proposed FLC was compared with other controller setups in regulat-ing thermal comfort and in-cabin IAQ, together with their energy consumption indicators. To ease comparisons, each controller was assessed using a star rating system. The rating was intended to visualize the relative performances of the controllers in different aspects. It can be seen that when the recirculation flap was forced to be closed at all times, the PMV index and cabin temperature were regulated within the occupant's thermal comfort threshold ranges. In return, the cabin IAQ degraded in terms of CO2 concentration as it started accumulating above the occupant's safety threshold limit. When the recirculation flap was forced to be always open, a better cabin IAQ was achieved in terms of CO2 con-centration. As shown in Table 8, the proposed FLC showed significantly improved results in regulating thermal comfort and cabin IAQ with average energy consumption in terms of mass flow rate when compared with the other two setups that lacked control of air recirculation. This indicated the importance of incorporating CO2 concentration in the controller design process. From the energy consumption indicator, it was observed that the proposed FLC could achieve a reasonable tradeoff between regulating PMV and CO2 concentration while maintaining an efficient level of energy consumption. Although the energy consumption indicator was higher than that of the setup with the recirculation flap closed, it could still regulate the in-cabin CO2 concentrations at the desirable level, which its counterpart (re--Poor. From the energy consumption indicator, it was observed that the proposed FLC could achieve a reasonable tradeoff between regulating PMV and CO 2 concentration while maintaining an efficient level of energy consumption. Although the energy consumption indicator was higher than that of the setup with the recirculation flap closed, it could still regulate the in-cabin CO 2 concentrations at the desirable level, which its counterpart (recirculation flap always closed) failed to do. When compared to the setup with the recirculation flap open, the proposed FLC only consumed 1/5 of the total energy (in terms of the indicator) and it was able to regulate the PMV and CO 2 within the predefined desirable levels.

Future Work
This was preliminary work to study the importance of incorporating CO 2 concentration and PMV in the design of vehicle HVAC controllers. FLC mechanisms were adopted due to their simplicity and effectiveness in handling problems with a relatively small number of nonlinear variables. Future work will be focused on testing the proposed FLC for other weather condition profiles and evaluating the energy consumption for HVAC systems with different architectures. Further, the proposed FLC would be experimentally validated using sensor technology to capture the relative humidity, cabin temperature, cabin velocity, and CO 2 concentration. The measured values will be given as input to the FLC and the outcome will be analyzed. Regarding the controller, machine-learning approaches will be adopted for fine-tuning the fuzzy logic membership functions automatically and adaptively according to different room settings and environmental parameters. Reinforcement learning will be incorporated with a fuzzy logic system to fine-tune the membership functions and fuzzy rules for regulating the PMV index and other IAQ parameters to be within the preferred levels in an energy-efficient manner.

Conclusions
The objective of this paper was to develop an intelligent controller to regulate the thermal comfort and IAQ of a vehicle compartment without significantly increasing the corresponding energy consumption. As discussed earlier, conventional vehicle HVAC systems are the primary energy consumers in vehicles. Here, the FLC-controlled HVAC system performed well in terms of regulating thermal comfort and IAQ with average energy consumption. This helps to increase the driving efficiency of the vehicles. Thermal comfort and IAQ were measured in terms of in-cabin PMV and CO 2 concentration, respectively. The control mechanism was realized with an FLC, which regulated both PMV and CO 2 concentration by adjusting the control parameters of the vehicle HVAC system. The performance of the proposed vehicle HVAC controller was evaluated using computer simulations via MATLAB Simulink and compared with two other setups that lacked control of air recirculation. Simulation results showed that the proposed controller was capable of regulating both PMV and CO 2 concentration within all predefined desirable threshold limits at the expense of only a small increase in energy consumption. In contrast, systems controlled by their counterparts have violated some of the thresholds.

Acknowledgments:
We would like to express our gratitude for the support from the School of Engineering, STEM College, RMIT University.

Conflicts of Interest:
The authors declare no conflict of interest.

Appendix A
This section explains the construction of membership functions in the proposed FLC.
In Figures A1 and A2, the x and y-axes represent the input variables, and the z-axis represents the output variables in the FLC. Figure A1 illustrates the mapping of the PMV index and cabin temperature to control the on/off and mass flow rate of the refrigerant and coolant of a vehicle HVAC system obtained from those 140 fuzzy rules. As per the fuzzy rules designed, when the cabin temperature was less than 18 • C or greater than 30 • C, the outputs from the FLC notified the HVAC system to deliver the maximum ratio of refrigerant or hot coolant to regulate the thermal comfort within the pre-defined level. At the same time, when the cabin temperature fluctuated between 18 • C and 30 • C, the FLC reduced the mass flow rate (Table 4) Figure A1. Mapping of the PMV index and cabin temperature membership function to control the HVAC system on/off and mass flow rate. Figure A2. Fuzzy logic membership function of the PMV index and CO2 concentration designed to regulate the HVAC system recirculation flap. Figure A2 illustrates the mapping of the PMV index and CO2 concentration to control the recirculation flap of the vehicle HVAC system through fuzzy rules. As described in Figure A2. Fuzzy logic membership function of the PMV index and CO 2 concentration designed to regulate the HVAC system recirculation flap. Figure A2 illustrates the mapping of the PMV index and CO 2 concentration to control the recirculation flap of the vehicle HVAC system through fuzzy rules. As described in Figure A2, based on the CO 2 concentration fluctuations, the recirculation flap will be opened or closed accordingly. When the CO 2 concentration inside the vehicle cabin reached 650 ppm, the recirculation flap opened (High (0.75-1)) to allow more fresh air flow into the vehicle cabin. As such, a better cabin IAQ can be maintained in terms of CO 2 concentration.