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

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8 System and functionality integration 76 passenger being equally dressed the whole year while driving the car. Another option is a user dressed according to the ambient temperature. In summer one usually wears light clothes, so an � �� of �� [7] is taken for temperatures above 25 ℃. In winter, pullover, long trousers and better insulating boots are probably also worn in cars, resulting in a higher value. Therefore, below a temperature of -5 ℃ the maximum for casual clothes stated in [7] of �� is taken. Between these extreme values a linear interpolation is done. The resulting function is shown in Figure 8.3. �� Figure 8.3: Assumption for clothes insulation as a function of ambient temperature. The comfort temperature (PMV = 0) was calculated depending on the ambient temperature for both options. The metabolism was set to �� , 50% relative humidity and no air flow was applied. The mean radiation temperature was calculated using an estimation for the heat transfer of the car driving �� as described in the following section. The result in Figure 8.4 shows that the fixed clothing gives a higher comfort temperature in winter than in summer. This setting is usually applied today [2] and is close to the recommendation of DIN 1946-3 [11]. Most likely equal clothing has been used for all ambient temperatures in these comfort observations. The adapted clothing factor gives (in the relevant temperature range) the opposite - a higher comfort temperature at a higher ambient temperature. This setting might not be accepted by everyone, but - as the clothes are adapted to the season - it will require less energy for cooling and heating.
8 System and functionality integration 77 �� Figure 8.4: Comparison of the comfort temperatures for different clothing adaptions. As an option for the controller, the parameters (upper and lower clothes insulation) can be adapted to the desired behaviour. The metabolism value is used to let the user influence the temperature. This is a good choice since its magnitude is highly dependent on the person. Even if single values are given in EN ISO 7730 [7], these are only mean values. In an annotation it is already mentioned there that elderly people have lower metabolisms. Moreover, a woman’s energy consumption is calculated differently from a man’s. Other parameters in the Harris-Benedict-Formula, used to estimate the metabolism, are age, weight and body size [37]. By simulations metabolisms from �� to �� have been found as a suitable range. Changing this value represents an indirect means of controlling the cabin temperature and can be used to manually compensate possible errors made in other parameters. A lower value will automatically lead to a higher comfort temperature. An advantage of this kind of user control is that the comfort temperature is automatically adapted to the ambient temperature and the air flow. A scaling with 100 being �� and 0 equalling �� is done in order to obtain a simpler scale and the relation of a higher value leading to a higher temperature. Mean radiant temperature The mean radiant temperature � � also has to be estimated. It is approximated by the mean surface temperature of the surrounding environment. In a car a major part of it is
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ISSN 0280-5316 ISRN LUTFD2/TFRT--58
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Diploma thesis subject Problem stat
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Acknowledgements ii Acknowledgement
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Acknowledgements iv 3.1.4 Windscree
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Acknowledgements vi 8.5 System simu
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Nomenclature viii Nomenclature Symb
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Nomenclature x Other symbols Symbol
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Nomenclature xii Subscript Descript
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1 Introduction Energy consumption t
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1 Introduction 3 adjustment of the
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2 Control objectives 5 2.1 Windscre
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2 Control objectives 7 2.2 Comfort
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2 Control objectives 9 � Metaboli
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2 Control objectives 11 ��
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2 Control objectives 13 Figure 2.6:
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2 Control objectives 15 2.4 Influen
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3 System description 17 or heat com
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3 System description 19 calculated
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3 System description 21 ��
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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
Page 89 and 90: 7 The HVAC-system controller 69 �
Page 91 and 92: 7 The HVAC-system controller 71 7.5
Page 93 and 94: 8 System and functionality integrat
Page 95: 8 System and functionality integrat
Page 115 and 116: 9 The MUTE project 95 order to fit
Page 117 and 118: 10 Hardware and characteristics of
Page 129 and 130: 11 Matlab implementation The climat
Page 131 and 132: 11 Matlab implementation 111 Only r
Page 133 and 134: 11 Matlab implementation 113 left i
Page 135 and 136: 11 Matlab implementation 115 tion a
Page 137 and 138: 12 Controller evaluation 117 ��
Page 139 and 140: 12 Controller evaluation 119 be not
Page 141 and 142: 12 Controller evaluation 121 Number
Page 143 and 144: 12 Controller evaluation 123 Table
Page 145 and 146: 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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