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An application of air conditioning in a car for hot climate country such as Malaysia is important due to guarantee the comfortable of both driver and passenger. Many factors must be concern in a car to maintain the temperature because temperature control in an automobile is more complex than a static room in the building. Non-linear characteristic of a temperature is the main problem to control the desired temperature of car air-conditioning.
Fuzzy logic provides an option to non-linear control because it is closer to real world . Non-linearity is handled by rules, membership functions and inference process, which results in improved performance, simpler implementation and reduced design costs . Most control application have multiple inputs and require modeling and tuning of a large number of parameters, which makes implementation very tedious and time consuming. Fuzzy rules can simplify the implementation by combining multiple inputs into single if-then statements while still conduct non-linearity .
Fuzzy logic rules and membership function will be adapted in the car air conditioner controller by using MATLAB Fuzzy logic Toolbox. This system will required user to key in the desired temperature in the car and the Fuzzy Logic controller will controlled the temperature in the car according to the temperature user key in. This system also automatically changes temperature due to the increases of number of passengers in the car.
Therefore, the purpose of this paper is to evaluate the usage of non-linear air conditioning in a car using fuzzy logic controller to enhance the efficiency climate control system in an automobile and increase the comfort for both driver and passenger. Several of computer simulation experiment using MATLAB Simulink will be conducted with some variables manipulated. The collected data of Fuzzy Logic controller for air conditioner will be analyzed and discussed at the end of this paper.
1.1 Problem Statement
Problem occurred in the car when the driver feels hot; driver will increase the temperature in the car by set on the blower switch to the higher position, so that the blower motor rotates at higher speed and cooling the temperature inside the car faster. Several minutes later, driver will feel cold and uncomfortable with the temperature inside the car, the driver will slow down the blower switch to the lower position to decrease the coldness in the car. After several minutes later, the driver will feel hot again due the temperature control in an automobile is more complex than a static room in the building. This action is wasting energy, temperature in the car need times to achieve the idle temperature and it cannot maintain to the idle temperature that driver desire.
The number of passenger also influences the performance of air-conditioning.Â When the number of passenger increases the heat produced is also increasing, this is because humans produce heat.Â When there were 4 passengers in the car, driver needs to switch the blower on to the highest position so that the temperature inside the car cool down and meet the passenger and driver comfort.Â It is different when only 2 or fewer passengers, the driver only need to switch the blower on to the highest position.Â Additional of passenger in a car will slow down the performance of car air conditioner to cool down.Â This shows the efficiency of the car air-conditioner controller when the number of passenger increase in the car.
Environment temperature and the temperature inside the car are the factor that must be taken in account to ensure the comfort and safety of both driver and passenger. On rainy days, the surrounding temperature is lower than the hot, sunny day, drivers will slow down the temperature inside a car air conditioner.
1.2 Project Objectives
There are several main objectives for this project:
To identify the car air conditioner system and the current controller.
To design fuzzy logic controller for non-linear car air conditioner.
To evaluate the performance of fuzzy logic non-linear air conditioner in car.
1.3 Project Scope
Types of car in Malaysia.
By using types of car that being use in Malaysia to analyze the car air conditioner system and the current controller in the car.
Range of temperature.
In range of 16- 25 degree Celsius and taken in account the effect of the environment temperature to the temperature inside the car.
The efficiency of cooling time.
To determines the efficient of cooling time for the controller when the number of passenger increasing.
Conduct experiments at the constant idle speed. (200rpm)
1.4 Report Outline
This report consists of five chapters. Each chapter elaborates different stage development of this project until to conclusion.
The first chapter of this report presents the background of the project, problem statements, objectives, and scopes of the project.
The second chapter of the report is the literature review to explain the overview of automobile electrical system and climate control system and types of controller that been used for air conditioner.
The third chapter of the report describes the methodology used to ensure the smooth running of this project which is done step by step.
The fourth chapter shows the result acquired from the experiment while developing the air conditioner controller using fuzzy logic and result obtain from the simulation conducted in MATLAB.
The fifth chapter of the report will analyze and discuses the result obtain from the MATLAB Simulink to ensure the project is achieve its objectives.
The last chapter of the report briefly explains the conclusion and recommendation for future works of the project
This chapter explains literature review that will be conduct in order to complete this Fuzzy Logic air conditioner controller. Study and research of published materials like case studies, technical documents and online library play an important role in literature review. Generally, the purpose of a review is to search, collect, analyzed and draw conclusion from all the material that have been read and studied. All these materials will help in the study and also to obtain a good result based on the previous project that had been done by others. The result later will become a supportive reference for the project topic which consist compilation of series of materials and sources. This material also can be a comparison between this project and also their project. If there are weaknesses in this project, the solution of it can be found in this material.
2.2 Overview of Automobile Electrical System and Climate Control System.
Figure 2.1 shows a model that interfaces the vehicle climate control system with a model of the electrical system to examine the loading effects of the climate control systems on the entire electrical systems of a car .
Figure 2.1: Vehicle electrical and climate control system
Figure 2.2 is an electrical system. This electrical system models the car at idle speed. The PID controllers ensure that the car's alternator (modeled by a simplified synchronous machine which has its field current regulated to control the output voltage) is also operating at the required speed. The output of voltage is then fed through a three phase 6 pulse diode bridge to supply the voltage needed to charge the battery which supplies the voltage for the car's DC bus. The power supply for the fan obtained from the DC bus is also used to power the windscreen wipers, radio, etc. The speed of the fan and thus the loading on the DC bus, are proportional to the difference between the set (or reference) temperature and the actual temperature inside the car. The inclusion of feedback in the electrical system ensures that regardless of the load, the voltage on the bus remains at a constant 12V. The changing of the input voltage to the engine is modeled as a DC machine in the car's electrical model. By changing the input voltage, we are able to see how the speed of the engine changes without affecting the voltage on the DC bus .
According to the statement, the speed of car changes will not affected the output result of the temperature inside the car because the voltage on the bus that supply to the blower fan will remains at a constant 12V. Thus, this system can provide the same satisfaction and comfort to the user in any speed of their vehicle going. The satisfaction of passenger is important, if the user satisfied with the services that given by the system than the system can sell to the market. To comfort also important to the user, this will prove that the system is good or not by satisfying the need of user.
Figure 2.2: Car electrical system
2.3 Air Conditioner Controllers
An Air Conditioner system is basically an MIMO (multiple inputs multiple outputs) system. However, sometimes it may be considered as an SISO (single input and single out) put system in the design of the controller, but if the aim was full control of the system, the interaction between temperature control and humidity control loops is important and must be taken into consideration . There are many controller modes are used in Air Conditioner systems nowadays, these are the controller that usually found in Air conditioner control mode;
2.3.1 Traditional On/Off Controller
On/Off control provides only two plant outputs, maximum (on) or zero (off). The control sensor usually takes the form of an on/off thermostat, humidistat and pressure switch
On/off control is a simple and low-cost method and it does not have enough accuracy and quality .Because of this Traditional On/Off Controller are simple, there a lot of disadvantages can be found in this system. One of the disadvantages is accuracy. The data that produce is not accurate make the system is not good enough to fulfill the requirement of user needs. Not only that, the quality of this Traditional On/Off Controller, also not good. It make the air conditioner cannot perform well enough and for future there must be a lot of error can occur. Figure 2.3 shows the action of an on/off controller.
Figure 2.3: The action of an off/on controller
2.3.2 Traditional PID Controller
Proportional, Integral and Differential (PID) control has been commonly used in many HVAC applications. There are a number of advantages in using PID control, such as its simplicity of implementation. This simplicity of implementation makes the controller easier to handle than the traditional on/off controller. The distinct effect of each of the three terms in the PID functions is probably the most important impetus for its survival in the world of ever sophisticated modern control algorithms. Several types of Proportional, Integral and Derivative controllers (P, PI, and PID) are used in the control of HVAC systems. The Proportional plus Integral plus Derivative (PID) control combines the advantages of (P+I) control with derivative to combat sudden load changes, while maintaining a zero offset under steady state conditions as show in Figure 2.4 .
Figure 2.4: The action of PID (Proportional, Integral, Derivative)
However, traditional controllers have the relatively acceptable functions, but due to the low efficiency and the high maintenance, it proves to be of high cost. Because of this controller are low efficiency, the controller might have a lot of disadvantages and this need a high cost to maintain the controller. The cost might be higher than buy a new one. Therefore, these traditional controllers are not suitable to be selected as the air conditioner controller.
2.3.3 Auto-tuning StateflowÂ® Controller
This Air Conditioner controller is implemented in StateflowÂ® as show in Figure 2.5. TheÂ Heater and Air ConditionerÂ state shows that when the user enters a setpoint temperature which greater than the current temperature in the car by at least 0.5 deg C, the heater system will be switched on. The heater will remain active until the current temperature in the car reaches to within 0.5 deg of the setpoint temperature. Similarly, when the user enters a setpoint which is 0.5 deg C (or more) lower than the current car temperature, the Air Conditioner is turned on and stays active until the temperature of the air in the car reaches to within 0.5 deg C of the setpoint temperature. After which, the system will switches off. The dead band of 0.5 deg has been implemented to avoid the problem of continuous switching. In theÂ BlowerÂ State, the larger the difference between the setpoint temperature and the current temperature, the harder the fan blows. This ensures that the temperature will reach the required value in a reasonable amount of time, despite the temperature difference. Once again, when the temperature of the air in the car reaches to within 0.5 deg C of the setpoint temperature, the system will switches off .
Figure 2.5: Climate control system
Figure shows the result of Auto-tuning StateflowÂ® Controller for the AC state with different number of passengers and the Heater state for different no of passengers. The result of the Auto-tuning StateflowÂ® show that the desired temperature cannot be achieved because state flow is off either in the air conditioning control or heater control when the current temperature in the car is 0.5Â°C above or low than the set the dead band of 0.5Â°C from desired temperature. Once the current temperature reaches 17.5Â°C, the air conditioning state will switch on and the current temperature will decrease immediately to 16.5Â°C. This will make the heater state switched on for a while and then both states will be switched off again when the difference value is less than 0.5Â°C or more then -0.5Â°C. This action will respond continuously until the desired temperature is reached .This take s a lot of time to switch from one action to another action and make the controller respond more slower than actual time it should respond. When the time of respond is slower, the user wills not satisfied with the services. This makes the controller no longer interesting to the air conditioner developer.
Figure 2.6 (a) Stateflow air conditioner performance with different number of passenger (b) Stateflow heater performance with different number of passenger.
2.4 Fuzzy Logic
2.4.1 Introduction of Fuzzy Logic
The Fuzzy Logic system deals with approximate information by using set theory for dealing with imprecise, fuzzy and vague information. Fuzzy controllers apply fuzzy sets and operations on fuzzy sets to model process nonlinearity, to establish a link between linguistic information and mathematics of the controller, to capture heuristic knowledge and rules of thumb, and to model the approximate behavior of systems .
There are specific components characteristic of a fuzzy controller to support a design procedure. In the block diagram in Figure 2.7 below is explains about the controller between a preprocessing block and a post-processing block. The following explains the diagram block by block.
Figure 2.7: Fuzzy Logic diagram
The Fuzzy Logic is controlled by the rule base. This rule make the controller operate with smoothly without an error. If the rule is wrong or have a mistake, the result produce will wrong and will give a complication in the graph.A data base included the information of the fuzzification module, the rule base and the defuzzification module. The Fuzzification translates the fuzzy outputs provided by the inference engine into a numerical (crisp) representation. Sometimes this value must be denormalized, i.e., the values of the control output must be mapped onto their physical domains. The most common deffuzification are the centre-of-gravity methods. The Fuzzy Logic system must be designed such that it can cope with these disturbances, but unfortunately this problem is not always considered. In Fuzzy Logic system, it uses the If-Then rules method where it contains the performance criteria and the choice and settings of the controller meeting the desired specifications.
This fuzzy logic controlled also being chosen is because it is easy to handle and will gave an accurate data. The accuracy leads to the good performance and always stable make the user satisfied. The product can be selling and it will become a worldwide.
Project Methodology describes a set of practices that will be carried out iteratively to produce the program. It is important to choose the right methodology because a successful and a quality end product depend on the method choose. This chapter will focuses on the studies about the accuracy temperature changes according to the data key in by user. User will key in the desired temperature in the car and the Fuzzy Logic controller will control the temperature in the car according to the key in data and the result of the data will perform in the graphs. In the graphs will based on the how the temperature reacts based on the data and also how it affect the times.
This chapter also including the block diagram of the climate control system that had been modified with fuzzy logic controller to fulfill the requirement of the study. The block diagram will be performing in MATLAB Simulink to obtain the result and every block of the diagram will be explain in details. There also include the steps to create the fuzzy logic controller which contain fuzzy rule base and the membership function.
Identify Climate Control System of Car
The figure 3.1 below show is the current climate control system of the car that will be modified with fuzzy logic to obtain the better result. In this section, the function of the climate control system will be elaborated precisely.
Figure 3.1: Climate control system.
TheÂ Heater_ACÂ state shows that when the user enters a setpoint temperature which greater than the current temperature in the car by at least 0.5 deg C, the heater system will be switched on. The heater will remain active until the current temperature in the car reaches to within 0.5 deg of the setpoint temperature. Same with this situation, when the user enters a setpoint which is 0.5 deg or more lower than the current car temperature, the Air Conditioner is turned on and stays active until the temperature of the air in the car reaches to within 0.5 deg C of the setpoint temperature. After which, the system will switches off. The dead band of 0.5 deg has been implemented to avoid the problem of continuous switching.
In theÂ BlowerÂ State, the larger the difference between the setpoint temperature and the current temperature, the harder the fan blows. This to make sure that the temperature will reach the required value in a amount of time that are reasonable to achieve, despite the temperature difference. Once again, when the temperature of the air in the car reaches to within 0.5 deg C of the setpoint temperature, the system will switches off.
The Air Distribution(AirDist) and Recycling Air States(Recyc_Air) are controlled by the two switches and the function of this switches are to trigger the climate control system (figure 3.1) chart. An internal transition has been implemented within these two states to facilitate effective defrosting of the windows when required. When the defrost state is activated, the recycling air is turned off.
3.2.1 The Heater and Air Conditioner Model.
The heater model shown below in figure 3.2 was built from the equation for a heater exchanger shown below;
Tout = Ts - (Ts-Tin)e^[(-pi*D*L*hc)/(m_dot*Cp)]
Ts = constant (radiator wall temperature)
D = 0.004m (channel diameter)
L = 0.05m (radiator thickness)
N = 30000 (Number of channels)
k = 0.026 W/mK = constant (thermal conductivity of air)
Cp = 1007 J/kgK = constant (specific heat of air)
Laminar flow (hc = 3.66(k/D) = 23.8 W/m2K )
In addition, the effect of the heater flap is taken into account to produce a better result. It is similar to the operation of the blower, the greater the temperature difference between the required setpoint temperature and the current temperature in the car, the greater the heater flap is opened and also the greater the heating effect will produce inside the car.
Figure 3.2: Heater control subsystem.
The Air Conditioner system model shown in figure 3.3 is example of one from the two places where the climate control model interfaces interact with the car's electrical system model. The compressor will become a burden because it is loads the engine car when the Air Conditioner system is active. The final temperature that exit from the Air Conditioner is calculated as follows:
y*(w*Tcomp) = m_dot*(h4-h1)
y = efficiency
m_dot = mass flow rate
w = speed of the engine
Tcomp = compressor torque
h4, h1 = enthalpy
Below, it is show that flow of the control of the Air Conditioner system where the temperature of the air that exits the Air Conditioner is determined by the engine speed and compressor torque.
Figure 3.3: Air Conditioner control subsystem
3.2.2 The Heat Transfer in Cabin
Here, the list of factors that affected the temperature of the air that felt by the driver in the car. That factors are:
The temperature of the air exiting the vents
The temperature of the outside air
The number of people in the car
These factors are inputs into the thermodynamic model of the interior of the cabin. By take this factors into account the temperature of the air exiting the vents and the calculation of the difference between the vent air temperature and the current temperature inside the car is being calculate and it will then multiply it by the fan speed proportion (mass flow rate). Then 100W of energy is added per person in the car. Lastly, the difference between the temperature of the outside air and the interior air temperature is multiplied by a lesser mass flow rate to account for the air radiating into the car from the outside.
The output of the interior dynamics model is insert to the display block as a measure of the temperature read by a sensor placed behind the driver's head.
The implementation of Fuzzy Logic Controller in the System.
Figure 3.4 Climate control system with fuzzy logic controller.
There are two fuzzy logic controllers used in this model. Figure 3.4 is the climate control system which uses fuzzy logic as controller. To control blower speed proportion based on the range of difference between the set point and current temperatures and the number of passengers in the cabin, the FLCBLOWER is used. The speed of the blower will be equal or nearly equal to the temperature differences. But, the blower speed proportion values will be based on the number of passenger and the result of blower will be influence by this passenger. Meaning that, although the range of temperature difference is same, the output change if the number of passengers in the car is also change. The blower speed proportion will increase when the number of passengers increases.
The other one of fuzzy logic controller is FLCSWITCH. This FLCSWITCH is used to switch either AC State or Heater State and it depend on the temperature difference values either it is negative or positive. If the difference is negative, this will show the set point temperature is less than the current temperature, and the air conditioning state will be switched on. If the difference is positive, this means that the set point temperature is larger than the current temperature. Therefore, the heater state will be switched on. When the difference is zero, both of the states will be switched off by the fuzzy logic controller and the desired state will be maintained until the user changes the values. The values that set up by the user will determined that the controller will off or on again.
Fuzzy Logic Controller Design.
Figure 3.5: Configuration of fuzzy logic controller.
The figure 3.4shown above is a structure of the fuzzy controller for the car climate control system. In this section, the input and output linguistic variables will be determined base on the best experience while in the car.
A single fuzzy controller can have more than one input and output linguistic variables depend on the specification of the control system need. As shown in figure 3.4, there are two fuzzy logic controllers that control the climate control system of the car which is FLCBLOWER and FLCSWITCH.
The FLCBLOWER control the speed of the fan of the system. For FLCBLOWER, the inputs linguistic variables are the range of temperature difference, a, and the heat provided by the number of passengers, b, and the output linguistic variables is blower speed proportions, d. While FLCSWITCH will decided for the system either to on/off the air conditioner state or on/off the heater state. For FLCSWITCH, the input linguistic variable is the range of temperature difference, a, and the outputs linguistic variables, c, are switched to either Heater or AC State. T1 is the exit temperature from both states and T2 is the internal temperature.
Design of the Membership Function of the Controller.
In order to represent the fuzziness of the real operation of the climate control system operation, fuzzy sets with completely characterized by the membership function are used. Membership function is a curve that defines how each point in the input space is mapped to a membership value between zero to one. The membership value is the important procedure while designing the fuzzy logic controller and it will be performed by using Fuzzy Logic Toolbox in MATLAB.
188.8.131.52 FLCBLOWER (Fuzzy Logic Controller for Blower)
FLCBLOWER consist of two inputs and one output, for the first input which is the range temperature different in the car contain of seven quantization level that are negative large (NL), negative medium (NM), negative small (NS), zero (ZR), positive small (PS), positive medium (PM), and positive large (PL). The second input, the heat provide by the number of passenger are contain of five quantization level which are very small (VS), small (S), medium (M), large (L), and very large (VL). In the other hand the output, blower speed proportions are negative fast (NF), negative medium (NM), negative slow (NS), zero (ZR), positive slow (PS), positive medium (PM), and positive large (PL). All the inputs and outputs quantization level is written in a table as shown in table 3.1 below.
The quantization level will be converted into the FIS Editor as shown in figure 3.5 and translate to the membership function as shown in figure 3.6 (a), (b),and (c).
Table 3.1: Quantization for FLCBLOWER
Figure 3.6: FIS Editor: FLCBLOWER
Figure 3.7: Membership function for FLCBLOWER (a) Input: Heat (n) (b) Input: Temperature difference (c) Output: Blower speed
Fuzzy rule is important to fuzzy logic system to make inference. By using if-then statements, fuzzy rules create based on the controller requirement. These are some examples of the fuzzy rules based;
If temperature differences is Negative Large and the heat is Very Small the output is Positive Small,
If temperature differences is Negative Medium and the heat is Very Small the output is Positive Small,
The fuzzy rules can be simplified by declared all the rules in the tables as shown in Table 3.2 below.
Table 3.2: Rule base for FLCBOWER
184.108.40.206 FLCSWITCH (Fuzzy Logic Controller for Switch)
FLCSWITCH consist of one input and two output, for the input which is the range temperature different in the car contain of seven quantization level that are negative large (NL), negative medium (NM), negative small (NS), zero (ZR), positive small (PS), positive medium (PM), and positive large (PL). The first output, the switch of air conditioner contain of two quantization level which are on (1) and off (0). Same as the second output which is the heater switch also contain of two quantization levels, on (1) and off (0). All the inputs and outputs quantization level is written in a table as shown in table 3.3 below.
The quantization level will be converted into the FIS Editor as shown in figure 3.7 and translate to the membership function as shown in figure 3.8 (a), (b),and (c).
Table 3.3: Quantization for FLCSWTCH
Figure 3.8: FIS Editor: FLCSWITCH
Figure 3.9: Membership function for FLCSWITCH (a) Input: Temperature difference, (b) Output: Air conditioner switch (c) Output: Heater switch
Fuzzy rule is important to fuzzy logic system to make inference. By using if-then statements, fuzzy rules create based on the controller requirement. These are some examples of the fuzzy rules based;
If a temperature difference is negative large, the output is air conditioner is on and heater is off.
If temperature difference is positive large the output is air conditioner is on and heater is off.
The fuzzy rules can be simplified by declared all the rules in the tables as shown in Table 3.4 below.
Table 3.4: Rule base for FLCSWITCH
Switch Air Conditioner
This chapter will explain how the both controller react to the climate control system of the car. The results of the controller are plot based on the fuzzy rules that had been determined in chapter 3. In this chapter also the system will be test for the real condition of temperature in the car using MATLAB Simulink to stimulate the climate control system to by changing the variables data that identified on the previous chapter. The result for the climate control system will be plot in the graph and show for this chapter.
4.1 FLCBLOWER Result
Figure4.1: Surface view of FLCBLOWER
There are two ways in displaying the result which is the COG (center of gravity) view and Surface view (figure 4.1). The figure 4.1 is the result for FLCBLOWER which display in surface view. The surface view show the possibility of FLCBLOWER reaction according to the fuzzy rules set. FLCBLOWER is influenced by two inputs which are temperature difference between external temperature and the user setpoint temperature and the number passenger in the car that is determined by heat produce 100w per person. The output for FLCBLOWER is the speed of blower fan. If the temperature different is negative large (-3 degree celsius), the speed of blower fan will spinning faster than -2 and -1 degree Celsius. From the surface view also state that the number of passenger is manipulated the speed of blower fan, although the temperature difference is zero the speed of blower fan increase if the number of passenger increase.
4.2 FLCSWITCH Result
Figure 4.2(a): Surface view: air conditioner sate, (b) Surface view: heater state
Figure 4.2(a) and figure 4.2(b) above show how the FLCSWITCH work depending on the fuzzy rules. The air conditioner turned on if the temperature different inside the car is negative and the air conditioner turned off if the temperature different change to positive values. In the other hand, the heater will turned on if the value of temperature different in the car is positive and turned off if the temperature is negative. The both state will turned off if the temperature different is zero.
4.3 The Climate Control System Results
Figure 4.3(a): Graph for increasing different temperature with constant number of passenger
Figure 4.3(b): Graph for decreasing different temperature with constant number of passenger
Figure 4.4(a): Graph for increasing temperature with different number of passenger (1-4)
Figure 4.4(b): Graph for decreasing temperature with different number of passenger (1-4)
Those four different figures show different results based on different variables. The importance of using different variables and factors is we can determine the relationship between variables and factors. Later, the relationship that achieved based on data in the graph can be used to determine the best results. For the first two figures, which is Figure 4.3(a) and Figure 4.3(b), the variables used for those graphs are a number of different temperature and time. A factor for those graphs is a constant value of one passenger. As we can see, Figure 4.3(a) shows a graph for increasing different temperature with constant number of one passenger while Figure 4.3(b) shows a graph for decreasing different temperature with constant number of one passenger. As for the second two figures, which is Figure 4.4(a) and Figure 4.4(b), the variables used for those graphs are a number of different temperature and time but the factor for those graphs is different.
Different number of passenger is used to determine whether the factors can influence the incoming result. For Figure 4.4(a), it shows a graph for increasing different temperature with different number of passenger while Figure 4.4(b) shows a graph for decreasing different temperature with different number of passenger.
ANALYSIS AND DISCUSSION
5.1 Analysis and Discussion
Figure 5.1(a): Graph for increasing different temperature with constant number of passenger
This graph [figure 5.1(a)] shows the times that take to warm the environment inside the car based on the temperature that is set up by the user. The temperature is set up at sixteen degrees. This is the default temperature for this system if the user does not enter the temperature. When user set the temperature at seventeen degrees Celsius, the time required for the temperature reaches the set temperature is 2 second. If the user set up the temperature at eighteen degrees Celsius, the time that take to reach this temperature is 5 second, 3 second late compared to time need for seventeen degrees. Next, when nineteen degrees set by the user, the time will be increased by three more seconds for the temperature reaches the set temperature of 8 seconds.
It is the same if the user set up the degree at twenty degree Celsius, it will increased 3 more second to reach the twenty degree and the time that it takes is 21 second. From the default value to reach the twenty degree Celsius it will take around 11second .From the result, the higher the temperature the longer the time taken to warm the environment inside the car. There a change at twenty one degree Celsius, the time that need to reach the set up temperature is 23 seconds. It is need 2 more second from the time that twenty degree Celsius need. For twenty two degrees Celsius, the time that need to reach the temperature is 26 second, for twenty three degrees is 19 seconds, for twenty four degrees is 20 second and last but not least twenty five degrees is 23 second. Between the twenty three degrees and twenty four degrees, the different is 1 second. This happen because, the different of heat between these two degrees is not too far, then it can take less time to reach compare to others.
Figure 5.2(a): Graph for decreasing different temperature with constant number of passenger
This graph [figure 5.1(b)] showed the result for temperature heat down. The default value for this temperature is 16 degrees. User will set up the higher temperature if they are feeling too cold and will use low temperature if they are feel hot. In his graph, the slow temperature user set, the longer time it take to heat down the temperature. For example, from this graph, when user set the temperature at 24 degree Celsius, the time that to go to the temperature is shorter and it only take 5 seconds. Compared to the 16 degree Celsius, when user try to heat down at this temperature, its take a longer time that is 24 seconds. For the range between 22 degrees until 17 degrees the time take for the temperature heat down is decreased by one seconds every time the user set up the lower temperature.
Figure 5.2(a): Graph for increasing temperature with different number of passenger (1-4)
For this graph [figure 5.2(a)], this is the result for what happen to temperature when there is passenger around. The passengers now as variables that can affect the temperature inside the car when they are around. There are 4 passengers that act as variable in this case. This graph show the result when the passengers are inside the car and also show what happen to the time and temperature. As the result shows, an increasing number of passengers, the less time it takes to heat. It is because, the more passenger inside the place, the heat from the individual passenger will heat the place and this help the actual temperature to heat more faster than normally.
From the result, the graph showed that that when there are 4 passenger inside the environment, the temperature will increase faster than other three attribute. Just like explanation on the first paragraph, the natural heat that comes from the passenger help to increase the temperature. While when they are 3 passengers the temperature heat up more slower than when it is have 4 passenger inside it. As we know, there is a big different when come to 3 or 4 persons The body heat produce is in big amount. For two and one person, the body heat produce by them is less than 4 and 3 person. So, the difference temperature between it so large. For two people, the result show that it heat faster in the beginning but it become slower after that and reach the temperature at 23 seconds. The last one, the one passenger, the heat body produce not really higher, there the heater need to heat up longer to reach the temperature that suitable for the passenger.
Figure 5.2(b): Graph for decreasing temperature with different number of passenger (1-4)
For this graph [Figure 5.2(b)], usually when the time is too hot, the passenger will be lowering the temperature in accordance with the coolness that they want. For many passengers, the time for the temperature dropped faster than when there is only one passenger. For example in this cases, when there are 4 passengers, the temperature heat down faster and only take 25 seconds but for the one passenger, 30 seconds need for temperature to heat down. The time that the two and three passenger takes for the temperature to heat down is 27 seconds and 28 seconds.
From the graph, the result obtain can be conclude that the more number of passengers in the car, the faster time taken to reach the required temperature. This is because it has stated in the fuzzy rules if the temperature difference is negative large and the heat provided by the passenger are very lager, the speed blower proportion will operate in positive fast. Although there are some slightly different of time when the cooling operation occurred in the car with variables number of passenger s which a several of seconds, the result of the increasing change temperature in the car can also be conclude that are not slightly affected by the increasing number of passengers
CONCLUSION AND RECOMMENDATION
Throughout the project, many simulations have been carried out to study the implementation of fuzzy logic control in car climate control system. The simulations also include the study on the previous controller, which are on/off controller, PID controller, and StateflowÂ® controller. The performance of the controllers techniques are carefully studied for helping to evaluate the best controller for car climate control system. From the studies show that the fuzzy logic controller is selected to be the car climate control system because of its characteristic that can be adapt to the real world problem and the fuzzy logic control system also proved that can enhance the technologies to user-friendly.
For the model as shown in figure 3.4 in chapter 3, there are two fuzzy logic controllers used in the system. The first fuzzy logic controller is FLCBLOWER, used to control the blower fan speed proportion. The controller reacts based on the temperature difference of external temperature in the car and the setpoint temperature set by user. The larger the range negative temperature difference is in the car, the faster blower speed fan spinning and if the range temperature difference is positive the blower speed fan reacted vice versa. The blower speed fan also manipulate by the number of passengers in the car. The more no of passenger in the car the faster blower speed fan will spin although the temperature different in the car is the same. The other fuzzy logic controller is FLCSWITCH that determined either to switch the air conditioner sate or heater state influenced by the positive and negative temperature difference in the car. If the user set the setpoint temperature lower than the external temperature inside the car, the difference is negative and the FLCSWITCH will on the air conditioner state. If the user set the setpoint temperature higher than the external temperature inside the car, the difference is positive and the FLCSWITCH will on the heater state. The FLCSWITCH turned of both state if the temperature difference in the car is zero and the desired temperature by user will remain as the setpoint temperature until the user changes the value of the setpoint temperature.
Based on this research, the desired temperature will be achieved by adapting the fuzzy logic control system to the car climate control system. The result show in figure 5.1(a) and 5.1(b) approve that the final result of the temperature can be reach according to the user desired. The result also can be conclude that the higher range temperature different the more time taken to reach the desired temperature. In the other hand, for figure 5.2(a) that result the temperature increase changing based on the number of passengers show that the increasing number of passenger is influence the time taken to reach the desired temperature. This is because the heat produce by the passenger is helping the heating process in the car. While for figure 5.2(b) show that the result of times taken to achieve the desired temperature based on the number of passenger has the slightly different due to the increasing of number passenger in the car. This is proved that the fuzzy rules are working well with the fuzzy logic controller to control the output temperature for the climate control systems.
Based on this research, the temperature control system for air conditioning and heater for the automobile climate control system can been optimized by using fuzzy logic as a controller. The time required to achieve the desired temperature could be decreased by studying the characteristic of the climate control system more further and obtain the knowledge from the climate control system to set a better parameters for the controller. The better parameters will improve the output of the climate control to be smoother and enhance the comfortable for passenger in the car.
Therefore, fuzzy logic control shows encouraging results in this simulation studies. This control technique is able to regulate blower speed proportion, air conditioning control, and heater control without the need of a mathematical model of the system and yet capable to provide non-linear relationship functions, rules, and defuzzification. The system leads to the good performance and always stable make the user satisfied. By further study the system, a product can be implementing in the Malaysian's automobile like Proton and Perodua that can be selling and it will become a worldwide.
 L. Reznik, Fuzzy Controllers. Oxford: Newnest, 1997, pp. 60-108.
 V.A. Constantin, Fuzzy Logic and Neurofuzzy Applications Explained. 1976, pp. 203-206.
 Mohd Fauzi Othman & Siti Marhainis Othman, Fuzzy Logic Non Linear Car Air Conditioning, Universiti Teknologi Malaysia, 2006.
 Duane and L. Bruce, Mastering MATLAB 6. Pearson Education International, 2001, pp. 450-462.
 Hossein Mirinejad, Seyed Hossein Sadati, Maryam Ghasemian & Hamid Torab, Control Techniques in Heating, Ventilating and Air Conditioning (HVAC) Systems, K. N. Toosi University of Technology, Tehran, Iran, 2008.
 Joao M.C Sousa, Uzay Kaymak. Fuzzy Decision Making Modeling and Control. World Scientific, 2002