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

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3 System description 24 given based on Figure 3.2. �� (3.14) �� Thermal energy change of the windscreen glass [�] Heat transfer rate between the cabin and the windscreen [�] Heat transfer rate between the ambient air and the windscreen [�] �� Heat transfer caused by other influences like rain or sun [�] �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� (3.15) � (3.16) The temperature of the air reaching the middle part of the windscreen is lower than at the outlet. This is due to the heat transfer to the (here assumed) colder glass. This will be taken into account with a simple estimation using the already calculated mean convection heat transfer coefficient at the inner windscreen (equations 3.10 and 3.13) for half the windscreen. �� ¡ �� ¡ �� ¡ �� ¡ �� (3.17) There are two errors that are made in this equation. �� is higher in the lower parts of the windscreen due to higher air speeds. On the other hand, the air temperature difference �� driving the heat transfer diminishes towards the middle. Therefore, this estimation should not be used for precise calculations but can be integrated into a controller due to its simplicity. Now, the windscreen temperature can be written as a function of the ambient and
3 System description 25 defrost temperatures and the air speeds on the windscreen. The windscreen constants are assumed to be known. �� (3.18) 3.1.5 Time constant considerations The systems described are of first order. A main characteristic of such systems is the time constant giving an indication of how fast the system reacts to external changes. Although the found systems are not time invariant, boundaries can be given. For a linear time invariant (LTI) first order system the following can be written with � being the time constant and � being the gain. results: � ¡ �� ¡ �� Finding the time constants for the four systems presented before gives the following �� ¡ �� ¡ �� ¡ �� ¡ �� (3.19) The varying parameters here are the air flow and the fraction of recirculation. The minimum time constant is found by using no recirculation and the maximum air flow. With the parameters found for the MUTE car described in Chapter 10 (see also Appendix C.1), a minimum of �� and a maximum of �� can be estimated. This is due to the high amount of thermal energy stored in the car material. The air itself will react faster. Without the interior thermal mass these times reduce to �� and �� seconds. For the humidity and carbon dioxide concentration the influence of the same parameters can be seen. �� ¡ �� ¡ �� (3.20) (3.21)
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 and 28: 2 Control objectives 7 2.2 Comfort
Page 29 and 30: 2 Control objectives 9 � Metaboli
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: 3 System description 23 � � �
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
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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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