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

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7 The HVAC-system controller The HVAC-controller receives its set-point from the optimiser. In the inner loop the task is to realise those inlet air conditions as fast and as good as possible with the available hardware. Here conventional automatic control objectives are of importance: speed, precision and accuracy. As already mentioned in Section 4.1, this control has to be adapted to the hardware. Therefore, only general control structures will be shown in this chapter. If not stated otherwise a “standard” car HVAC-system is assumed as it is presented in Section 3.2. 7.1 Temperature and humidity control In car HVAC-system heat and humidity control are usually coupled since the air is first cooled (and dehumidified) and then reheated. The humidity of the air after the evaporator is not measured, while its temperature has to be captured to avoid icing. This measurement will be used as indicator for humidity. The here presented control design is based on feed-forward signals to allow fast reaction to set-point changes. To give accuracy and to compensate for model errors a closed-loop PI-controller is added. A scheme for doing this in a standard HVAC-system is shown in Figure 7.1. Equa- tions for the calculations are found in Section 3.2 describing the HVAC-hardware func- tioning. The required temperature of the saturated air coming from the evaporator is calculated on the basis of the given ��. If it is higher than or equal to than the ambient temperature, the evaporator can be switched off. This is due to the fact that no humidi- fication is possible. A lower limit for the temperature should be around 4 degrees Celsius
7 The HVAC-system controller 69 �� Figure 7.1: Scheme of the HVAC temperature and humidity control. above zero in order to avoid icing on the evaporator [36]. Based on equation 3.32, a feed- forward value for the power needed to cool the air from its inlet value � �� to the desired � �� can be determined. The difference between the calculated cool air temperature and the actual value is fed into a PI-controller. Its output is then added to the feed-forward control signal. This cooling power has to be translated to actor values, for example a valve setting and the compressor speed. The principle for the heating power control is the same. The given set-point for the inlet temperature � �� and the temperature after the evaporator are used to calculate a feed-forward heater power. To this value the correction of the PI-controller, which is working on the error between the set and the actual � �� is added. Again, depending on the hardware, this power is translated, for example into an air mix flap angle. If no cooling is available, the cooling part is simply removed and the � �� temperature is replaced by the inlet value � ��. � � � � ��
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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
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
Page 87: 6 The optimiser: an evolutionary al
Page 91 and 92: 7 The HVAC-system controller 71 7.5
Page 93 and 94: 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 ��
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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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