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

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7 The HVAC-system controller 70 7.2 Heat pump control For a heat pump the scheme described before can be used as well. The cooling and the heating power have to be translated into hardware settings. The heat source or sink can be taken directly from the optimiser output if a design variable for this has been introduced (cf. Section 4.5). The de-icing should be implemented by using a predefined program and by sus- pending the inlet temperature control. Therby, no interference with other control signals can happen during the heating and melting of the ice on the front heat exchanger. This avoids too short de-icing which would lead to fast ice re-accumulation. 7.3 Fan control Normally no sensor is used for the air flow, so open-loop control is the only option. It is based on the fan characteristics as well as the system impedance of the HVAC-system. This gives one operating point (= air mass flow) for a given fan voltage, as shown in Figure 3.5 in Section 3.2.1. If these points are known, a fan voltage for a desired air flow can be given. In this way the air flow is controlled with the help of a look-up table. However, there are a lot of disturbances making this approach inaccurate. As described in the system description in Section 3.2.1 countermeasures have to be realised depending on the geometry and HVAC-system. Even if some relations can be given, for example for the pressure rise at the air inlet, measurements (or simulations) are required to quantify the resulting influence on the air flow. 7.4 Flap control The air mass flow percentage to the outlets (or passing the flap) has to be translated into a flap opening angle. This is done with look-up tables relating these two parameters. To obtain these, experiments for each flap have to be performed. For a mass-production vehicle this could also be implemented in hardware by a gear or a cam disc making the control “see” a (nearly) linear relation.
7 The HVAC-system controller 71 7.5 Cold air prevention system The heating core is not able to deliver heat instantaneously. By contrast, the fan reacts much quicker to changes, so there has to be a mechanism preventing the passengers to be blown with cold air, which is not comfortable. The cold air prevention system avoids this by reducing the fan control output when the outlet air is cold. The air speed-comfort relationship found in Section 2.2.2 can be used as a base. Air flow into the cabin colder than zero degree Celsius should be reduced to a minimum.
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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
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 89: 7 The HVAC-system controller 69 �
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 ��
Page 139 and 140: 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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