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

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8 System and functionality integration In this chapter the application of the optimiser algorithm for the car climate control will be shown. The equations for the objectives found in Chapter 2 and the system (Chapter 3) have to be brought into a suitable form for the algorithm. User control and different operating modes have to be integrated. 8.1 Objective implementation The objectives are directly used as far as they can be expressed mathematically. The results are used in the algorithm, as seen in Section 6.3.5, in two ways. On the one hand, it is important if they exceed a limit. On the other hand, the weighted sum of all is used if the limit comparison gives no winner. In order to make the weight designation and the objective comparison easier it is advantageous to have a common maximum and minimum objective result. This avoids that one objective becomes too dominant. The minimum is set to zero, while for the maximum a parameter �� is used. 8.1.1 Windscreen fogging affinity The basic equations to estimate the fogging affinity are given in Section 2.1. The finding of the minimum, however, is not suited for a control application. The distance calculated in equation 2.3 will be replaced by an estimation. The idea is to approximate the closest point on the vapour saturation curve. This is done via a local linearisation of this curve as seen in Figure 8.1. Starting from a measured point � given through the water vapour pressure �� and a temperature � the points �
8 System and functionality integration 73 �� Figure 8.1: A way to estimate the fogging affinity given in equation 2.3. and � are found by the vertical and horizontal crossings with the saturation function. They can be calculated with the help of equation 2.1, here referred to as � �� . � �� (8.1) There are small errors calculating � � with the inverse function if � � � � ℃ because of the switch in the parameters (see Table 2.1). They are not important to the estimation, though and do not justify a recalculation with changed parameters. Having found these points, the temperature of the closest point to � on the segment �� is of interest. It is given by the following equation: �� ¡ �� (8.2) The values of the points �, �, and� are inserted and the vapour pressure is scaled with the factor � �� from equation 2.2 in order to obtain a correct angle �� in the
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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
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
Page 89 and 90: 7 The HVAC-system controller 69 �
Page 91: 7 The HVAC-system controller 71 7.5
Page 95 and 96: 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
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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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