Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and                      Applications (IJE...
Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and                   Applications (IJERA)...
Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and                        Applications (I...
Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and                         Applications (...
Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and                      Applications (IJE...
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  1. 1. Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.640-644 Controlling Thermal Comfort Of Passenger Vehicle Using Fuzzy Controller Prof. Alok Singh*, Sandeep Kumar** *(Faculty, Department of Mechanical Engineering, M.A.N.I.T. Bhopal India** (M.Tech, Maintenance Engineering &Management, Department of Mechanical Engineering, M.A.N.I.T. Bhopal IndiaABSTRACT The automobile car cabin is complex man which results in improved performance, simpler–machine interface. Two main goals of Heating implementation and reduced design costs [1, 2]. MostVentilation and Air conditioning System is control applications have multiple inputs and requireproviding thermal comfort and save energy. modeling and tuning of a large number of parameters,Comfort is a subjective feeling and hard to model which makes implementation very tedious and timemathematically. This is because thermal comfort consuming. Fuzzy rules can simplify theis influenced by many variables such as implementation by combining multiple inputs intotemperature, relative humidity, air velocity and single if-then statements while still handling non-radiation. Aim of this paper is to design the linearity [7].mathematical model for car cabin and to showfeasibility with fuzzy logics. Fuzzy controller is NOMENCLATUREdesigned for cabin to control the cabintemperature. bp Blower Power (kW) Cp Specific Heat (kJ/kg _C)Keywords - Thermal comfort, Fuzzy controller E Rate of Change of Energy (kW) e Temperature ErrorI. INTRODUCTION Δe Rate of Change of Temperature Error Car is a medium of fulfilling vital part of Δz Blower Inputsociety need of transport. Many people in modern m. Mass Flow Rate (kg/s)society utilize a car in one way or other. Temperature pf Percent of Fresh Airis an important factor in occurrence of traffic T Temperature (_C)accident. Better climate control system in car cabin V Velocity (m/s)improves thermal comfort which results in increased W Power (kW)driver as well as passenger caution and thus improves ε Heat Exchanger Effectivenessdriving performance and safety in different drivingconditions. Since an automobile is operated in SUBSCRIPTSvarious weather Conditions, such as scorching heatand downpours, passenger thermal comfort is a Airconstantly affected by environmental changes [7]. An amb Ambientair conditioning system must maintain an acceptable b Blowerthermal comfort inside the cabin despite these cabin Automobile Cabinchanges. However, an air conditioning system cooler Cooler Compartmentinevitably uses energy, which increases automobile e Evaporatorfuel consumption. This energy must be minimized. eR Evaporator refrigerantTherefore, an effective control procedure is needed to fa Fresh Airresolve the contradictions of low energy consumption gen Generationand a pleasant driving climate for passenger as well i Inas driver [2]. min Minimum o OutFuzzy logic provides an alternative control because it R Refrigerantis closer to real world. Fuzzy logic is handled by ra Re-circulation Airrules, membership functions and inference process, 640 | P a g e
  2. 2. Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.640-644II. THERMAL MODELING OFAUTOMOBILE CABIN Initially mathematical model of thermalenvironment for an automobile cabin is derived.Specific parameters for Maruti alto automobile areused. This model includes blower, evaporator, as wellas the impact of important thermal loads such as sunradiation, outside air temperature and passengers onclimate control of cabin. Mathematical modelingshould be performed in a manner to clearly show theeffect of control parameters on occupant thermalcomfort [5]. The main control point is blower power,which regulates the blower speed for cold air. III. ASSUMPTIONS1.1 The following assumptions were made toderive the mathematical model:  Dry air. Figure 1.Automobile cabin air conditioning system  Ideal gas behavior.  Perfect air mixing. Mathematical model of system is obtained as [1]  Neglect potential energy in all parts.  Neglect thermal losses between components. Egen = 0.118 (T -Tcabin) + 0.0022 (T -Tcabin) amb amb  Negligible infiltration and exfiltration +0.2618 (1) effects.  Neglect transient effect in components and The principle of energy and mass conservation are channels. used to derived the equation for mathematical model  Negligible energy storage in air conditioning [6]. components.  Zero mechanical work in the cabin ðW=0 dE cabin = E gen –W cabin +∑ E cabin,i - ∑ Ecabin,o (2) dt  Air parameters at standard conditions of 20°C, 50% rh, sea level. dm cabin = ∑ mmin -∑ m out = 0 = mcabin = constant (3)  Air parameter exiting the cabin has the same dt properties as inside the cabin. on simplification equation are as followsAbove assumptions help to simplify the equations d(m cabin h dE cabin cabin )while produce negligible error in modeling. = dt dt = d[ (  air V cabin )(CpT cabin )] dt =  air V cabin Cp dT cabin (4) dt ∑ E cabin,i = E cooler,o = m air CpTcooler,o (5) ∑ E cabin,o = E cooler,i = m air CpTcabin,i (6) Putting the values on eq. (2) which leads to 641 | P a g e
  3. 3. Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.640-644K1.T cabin +K2.T cabin = (K3 .Tamb + K4.T eRi + K5 The major components of fuzzy logic+k6.Twi ) (7) controller (FLC) are the input and output variables, fuzzification, inference mechanism, fuzzy rule baseK1 =  air V cabin .m air Cpa2 and deffuzification. FLC involves receiving input signal and converting the signal into fuzzy variableK2 = m air Cpa [ m air Cpa –(1- ε e ) { m air Cpa } + (fuzzifier). In most air conditioning controllers,0.1202] temperature is used as feedback and a fix temperature is the controller goal. Block diagram of this controllerK3 = 0.1202 mair CPa for automobile cabin is given in figure 3. Inputs to fuzzy controller are the error and the changes ofK4 = m2air C2Pa εe error. Output is blower power for regulating the necessary blend of cold air. Error is defined by theK5 = 0.2618 m air Cpa following equation [6]:K6 = 0 Error = T d - T cabinV air = 0.0245 exp (4.0329*bp) Desired temperature is adjusted in the automobile, which in warm months is about 22°C. The governing equation (7) is first order Formation of suitable fuzzy sets is important indifferential equation with time dependent designing of fuzzy controller. Triangular fuzzy setscoefficients. So it has a complex behavior. are chosen for this controller and are equally divided. The min–max limits are adjusted based on theIV. FUZZY CONTROLLER dynamics of the system and the control goal. For this Fuzzy logic was born in 1965 by Zadeh [1]. system, the min–max limits are manually selected.Nowadays, it is widely used in industrial Maximum number of rules is equal to multiplicationapplications. Fuzzy logic can model the nonlinear of the number of input membership functions. It isrelationship between inputs and outputs. It can clear that a large number of rules are more difficult tosimulate the operator’s behavior without use of define [3, 4]. Therefore, to simplify the problem threemathematical model. It is a method that transfers membership functions are selected for each input andhuman knowledge into mathematics, with the aid of output variables. This results into nine rules for eachif–then rules [3]. Each rule explains a nonlinear output. The state evaluation fuzzy control rule isrelationship between inputs and outputs. All rules applied for controlling this system [2].together define a linguistic model. For moreinformation about fuzzy logic see Zimmerman.Fuzzy control is the most practical branch of fuzzylogic. Fuzzy control is inherently vague andnonlinear, thus it is suitable for systems with thisbehavior. Automobile air conditioning system is alsononlinear and complex therefore, it is difficult forconventional methods to for controlling this system. Figure 3. Temperature feedback system The fuzzy control rules relate the input fuzzy variables to an output fuzzy variable which is called fuzzy associative memory (FAM), andFigure2. Climate control system using fuzzy logic defuzzifying to obtain crisp values to operate the system (defuzzifier).V. DEVELOPMENT OF FUZZY LOGIC A linguistic variable in the antecedent of aCONTROLLER fuzzy control rule forms a fuzzy input space with respect to a certain universe of discourse, while that 642 | P a g e
  4. 4. Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.640-644in the consequent of the rule forms a fuzzy output  If e is N and Δe is PO Then ΔZ is NMspace. In this work the FLC will have two inputs andone output. The two inputs are the temperature error  If e is C and Δe is PO Then ΔZ is SL(e) and temperature rate of change of error (Δe), andthe output is the blower speed change (ΔZ). The membership functions for fuzzy sets canhave many different shapes, depending on definition[4,5]. Popular fuzzy membership functions used inmany applications include triangular, trapezoidal,bell-shaped and sigmoidal membership function. Themembership function used in this study is thetriangular type. This type is simple and gives goodcontroller performance as well as easy to handle. The ouniverse of discourse of e, Δe and ΔZ is -6 C to o o o Table 2. FAM+6 C, –1 C to +1 C, and 0 to 5 V . dcTable1. Input and output fuzzy variables Fuzzy variable Linguistic Labels Hot H A fuzzy logic rule is called a fuzzy eassociation. A fuzzy associative memory (FAM) is Normal Nformed by partitioning the universe of discourse of Input Cool Ceach condition variable according to the level of Negative NEfuzzy resolution chosen for these antecedents, ∆e Normal NOthereby a grid of FAM elements. The entry at each Positive POgrid element in the FAM corresponds to fuzzy action. Slow SLThe FAM table must be written in order to write the Output ∆zfuzzy rules for the motor speed. The FAM table for Normal NMthe motor speed has two inputs (temperature error Fast FTand temperature rate-of-change-of-error) and oneoutput (the blower speed change). The output decision of a fuzzy logic controller is a As the input and the output have three fuzzy fuzzy value and is represented by a membershipvariables, the FAM will be three by three, containing function, to precise or crisp quantity.nine rules. A FAM of a fuzzy logic controller for themotor speed is shown in the FAM diagram in Table2.The rules base from Table 2 are as follows :  If e is H and Δe is NE Then ΔZ is SL  If e is N and Δe is NE Then ΔZ is SL  If e is C and Δe is NE Then ΔZ is SL  If e is H and Δe is NO Then ΔZ is SL Figure4. Fuzzy sets A defuzzification strategy is aimed at  If e is N and Δe is NO Then ΔZ is SL producing a non-fuzzy control action that best represents the possibility distribution of an inferred  If e is C and Δe is NO Then ΔZ is SL fuzzy control action. As to defuzzify the fuzzy control output into crisp values, the centroid  If e is H and Δe is PO Then ΔZ is FT defuzzification method is used. For practical 643 | P a g e
  5. 5. Prof. Alok Singh, Sandeep Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 4, June-July 2012, pp.640-644purposes, the centroid method gives stable steady Evaporator cooling capacity was selected as criterionstate result, yield superior results and less for energy consumption. These two goals result in acomputational complexity and the method should two-objective optimization problem, this minimizingwork in any situation. both the comfort error and energy consumption. The multi-objective goal is converted into a single objective problem. To improve the performance of fuzzy controller, It is observed that after optimization controller reaches thermal comfort quicker while minimizing energy consumption. REFERENCES [1] C. L. Hock Simulation of Fuzzy Logic Control Implimentation in Chemical Ne utralisation Plant Using MATLAB’s Simulink, Thesis University Teknologi Malaysia, 2002. [2] J.M. Saiz Jabardo, Modeling and experimentalFigure 5. Temperature variation in automobile cabin evalution of an automobile air conditioning system with variable capacity compressor,CONCLUSIONS Elsevior science, 2002. Through this paper, many simulations havebeen carried out to study the implementation of fuzzy [3 ] Mohd Fauzi Othman, Fuzzy logic control forlogic control in automobile cabin climate control. non linear car air conditioning, Elektrika,Poor interior condition of automobile contributes to 2006.traffic accidents as well as discomfort in longdistance drives. Thermal comfort is one of the most [4] Henry Nasution, Development of Fuzzy Logicimportant comfort factors. Automobile cabin thermal Control for vehicle Air Conditioning system,environment is complex, continually varies during 2007.time and cannot be described with temperature alone.Important parameters that affect thermal comfort are [5] M. N. Musa, Application of Fuzzy Logiccabin air temperature, relative humidity, air velocity, Control for Roof –Top Bus Multi –Circuit Airenvironment radiation, activity level of passengers Conditioning System, ImechE, 2007.and clothing insulation. In this paper thermal comfortfor Maruti Alto automobile is studied. A simplified [6] A.A. Tootoonchi, Intelligent control ofmathematical model is introduced where cabin air thermal comfort in automobile, CIS, 2008.temperature and the air velocity are the only twovariables. This simplification is made without [7] Yadollah Farzaneh, Controlling automobileintroducing significant error. The temperature is used thermal comfort using optimized fuzzyas the feedback in fuzzy controller. controller, Applied Thermal Engineering, 2008.Providing thermal comfort while minimizing energyconsumption was the goal of the controller. 644 | P a g e