Design of an Automatic Control Algorithm for Energy-Efficient ...

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2 Control objectives 8 �� Internal heat production Heat loss due to water vapour diffusion through the skin Heat loss due to sweating Latent heat loss due to respiration Dry respiration heat loss Heat loss by radiation from the surface of the clothed body Heat loss by convection from the surface of the clothed body By combining this equation with experimental data a comfort index reaching from -3 (too cold) to +3 (too hot) has been found. The resulting figure is called Predicted Mean Vote (PMV). �� ¡ � �� (2.5) The expressions used in equation 2.5 can be stated as follows [8]: �� ¡ �� ¡ �� ¡ �� for �� for �� for � �� for � �� (2.6) (2.7)
2 Control objectives 9 � Metabolism [��] (�� , sitting calm) � External work [��] � � � �� Water vapour pressure [��] Air temperature [℃] Ratio of the surface area of the clothed body to the surface area of the nude body [-] Surface temperature of the clothing [℃] Mean radiant temperature [℃] Convective heat transfer coefficient [ � �� ¡� ] Thermal resistance of clothing [��] (�� ¡� � ) Relative air velocity [ � � ] 2.2.2 Inhomogeneous climate The model by Fanger was developed for stationary environmental conditions. It gives no means of including the influence of different temperatures and air flows on the body. A more complex approach is used in the ISO standard 14505-2 [9] by measuring temperatures of body parts. A comfort function chart found in these experiments is given for a winter and a summer case. It is found that a cooler temperature in the head area is desired, while warm feet lead to maximum comfort, especially with cooler ambient temperatures. This is opposed to the natural air layering where the warmer and lighter air is on top. Controlling this online would require a lot of sensors and actors. Therefore, this is usually implemented in hardware in a car HVAC-system. The air flow is optimised in such way that warm air is output through the foot and defrost outlets, while cooler air is going directly to the passenger. Manual adaption of the direct air flow temperature is sometimes implemented. Since this is related to the hardware, the temperature layering will not be regarded further in this work. The air flow itself can also cause discomfort. Higher air speed on the body leads to higher heat transfer coefficients and consequently to an intensified temperature sensation. The effect of moving air with ambient temperature is included in the comfort calculation in equation 2.6. Local air jets with a different temperature, however, as they occur in a
Page 1: ISSN 0280-5316 ISRN LUTFD2/TFRT--58
Page 5: Diploma thesis subject Problem stat
Page 8 and 9: Acknowledgements ii Acknowledgement
Page 10 and 11: Acknowledgements iv 3.1.4 Windscree
Page 12 and 13: Acknowledgements vi 8.5 System simu
Page 14 and 15: Nomenclature viii Nomenclature Symb
Page 16 and 17: Nomenclature x Other symbols Symbol
Page 18 and 19: Nomenclature xii Subscript Descript
Page 21 and 22: 1 Introduction Energy consumption t
Page 23 and 24: 1 Introduction 3 adjustment of the
Page 25 and 26: 2 Control objectives 5 2.1 Windscre
Page 27: 2 Control objectives 7 2.2 Comfort
Page 31 and 32: 2 Control objectives 11 ��
Page 33 and 34: 2 Control objectives 13 Figure 2.6:
Page 35 and 36: 2 Control objectives 15 2.4 Influen
Page 37 and 38: 3 System description 17 or heat com
Page 39 and 40: 3 System description 19 calculated
Page 41 and 42: 3 System description 21 ��
Page 43 and 44: 3 System description 23 � � �
Page 45 and 46: 3 System description 25 defrost tem
Page 47 and 48: 3 System description 27 Figure 3.4:
Page 49 and 50: 3 System description 29 width modul
Page 51 and 52: 3 System description 31 ��
Page 53 and 54: 3 System description 33 shown in Fi
Page 55 and 56: 3 System description 35 of performa
Page 57 and 58: 4 Control strategy The main goal of
Page 59 and 60: 4 Control strategy 39 limits are di
Page 61 and 62: 4 Control strategy 41 ��
Page 63 and 64: 4 Control strategy 43 cabin. This s
Page 65 and 66: 4 Control strategy 45 controlled. T
Page 67 and 68: 5 The disturbance estimator 47 in e
Page 69 and 70: 6 The optimiser: an evolutionary al
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6 The optimiser: an evolutionary al
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7 The HVAC-system controller 69 �
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7 The HVAC-system controller 71 7.5
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8 System and functionality integrat
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9 The MUTE project 95 order to fit
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10 Hardware and characteristics of
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11 Matlab implementation The climat
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11 Matlab implementation 111 Only r
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11 Matlab implementation 113 left i
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11 Matlab implementation 115 tion a
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12 Controller evaluation 117 ��
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12 Controller evaluation 119 be not
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12 Controller evaluation 121 Number
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12 Controller evaluation 123 Table
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12 Controller evaluation 125 In the
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12 Controller evaluation 127 ��
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13 Conclusion 129 13.2 Possible imp
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Bibliography [1] Holger Großmann.
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Bibliography 133 [22] M. Wang, T.M.
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Appendices
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B In- and outputs The interface bet
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Signal Unit Range Type Comment Stat
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In- and outputs 141 B.3 Error codes
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and system parameters 2 concentrati
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Simulation parameters 145 C.2 Contr
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Simulation parameters 147 Panic han
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Simulation parameters 149 C.5 Test
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Additional simulation results 151 D
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Optimiser code 153 real_T ∗panic_
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Optimiser code 155 52 return 10; 54
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Optimiser code 157 138 /∗ return
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Optimiser code 159 230 ∗/ ∗ ===
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Optimiser code 161 ∗ IMEA_breedin
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Optimiser code 163 414 } 416 } } st
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Optimiser code 165 if (sortedPop [
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Optimiser code 167 596 ∗ Printout
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Optimiser code 169 690 ∗ Latest R
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