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

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8 System and functionality integration 86 The lower bound is given by the amount of water in the saturated air at the evaporator. This is dependent on the temperature of the evaporator and therefore on the cooling power. It is also a limiting factor if the heating power needed to re-heat the air is available. Another limit to respect is that the evaporator temperature should not be below zero degree to avoid icing. �� (8.25) The constraints then look similar to the ones on the temperature �� (8.26) The lower limit of the air mass flow is 0 �� , but since a low air flow is always desired, the air flow when the fan starts spinning is taken instead. The physical upper limit depends on the fan and the HVAC-unit, the duct and the outlet pressure drop. Another limitation is given by the cold air prevention system (see Section 7.5). Here the lower of both limits has to be chosen as ��. �� (8.27) Since the flap passage percentage is defined as mass flow to a certain output divided by mass flow before the flap, the � cannot be negative and is 100% maximum. �� (8.28) In an electric vehicle power management is important. Therefore, it is good if sys- tems which are not safety-critical, like heating and ventilation, can be restricted in power consumption. This results in a constraint that the actual drawn power � �� has to be less then the given (and changing!) limit ��. �� (8.29) Additional constraints may be necessary depending on the HVAC-system. For ex-
8 System and functionality integration 87 ample a constraint for the icing if a heat pump is employed. The icing can be integrated into the energy consumption objective function by a modified efficiency of the heat pump. However, to force de-icing above a certain limit in order to avoid damage to the com- ponents, a separate constraint is advisable. Its limit can then be set without interfering with the objectives while it is not exceeded. 8.2.1 Integration as boundaries Some constraints vary, while others are fixed. These - as the flap passage and fan limits - can be given as boundaries for the design space. Upper and lower limits for the other design variables have to be set in order to limit the search area. A smaller design space leads to faster results as unusable results are not considered. These boundaries may be adapted by external signals or user settings in order to force a different minimum or limit one variable. However, they may not depend on other design variables or predicted states. In Table 8.1 an overview of the standard values is given. Table 8.1: Overview of the objectives and their corresponding parameters. Design variable lower bound upper bound �� 0 �� 0 1 �� 0 1 �� 0 1 Boundaries for the de-icing switch and source selection are not required. Due to their discrete nature only available values can be chosen. 8.2.2 Integration as objectives All the constraints depending on design variables or predicted states have to be evaluated for each individual. Therefore, they have to be implemented as special objective, as described in Section 6.3.5. The functions, as shown above, return a value between -1 and
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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 � � �
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3 System description 25 defrost tem
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3 System description 27 Figure 3.4:
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3 System description 29 width modul
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3 System description 31 ��
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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 105: 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
Page 147 and 148: 12 Controller evaluation 127 ��
Page 149 and 150: 13 Conclusion 129 13.2 Possible imp
Page 151 and 152: Bibliography [1] Holger Großmann.
Page 153 and 154: Bibliography 133 [22] M. Wang, T.M.
Page 155 and 156: 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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