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

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Simulation parameters 148 C.4 Optimiser settings Symbol Parameter Value �� Sample time for the optimiser �� Population size �� Number of children �� Number of elite individuals � �� Number of generations � �� Maximum exploring factor �� Minimum exploring factor �� Exploring factor adaption increment �� Maximum power weight bias �� Prediction horizon �� System prediction sample time �� Diversity threshold �� Base population � �� Comment �� Little fresh air ��+22 �� Fast warm up ��+22 �� Defrost/defogg 22 �� Steady state mode �� Maximum cooling 22 ℃ is the maximum possible temperature increase possible with the available heating power at full air mass flow.
Simulation parameters 149 C.5 Test case parameters Heat-up Cool-down Rainy autumn Transition period �� -10 ℃ 30 ℃ 10 ℃ 15 ℃ ��, �� (��) 80% (�� ) 70% (�� ) 100% (�� ) 80% (�� ) �� -10 ℃ 50 ℃ 20 ℃ 15 ℃ �� -10 ℃ 50 ℃ 20 ℃ 15 ℃ �� (South) � �� (South) � �� (South) � �� (South) �� Driving direction � �� Slope � �� 2/0 2/0 2 2 � �� Precondition Off/On Off/On Off Off Automatic mode was enabled in all cases. The defrost or recirculation mode was not active. The temperature control was set to 50%. No limits regarding the battery cooling air (cf. section 10.5) or energy consumption were made in these tests. The energy management influence was set to 50 %. The values for the sun radiation were taken from measurements [40] made by the Meteorologisches Institut der Universität München on the following dates: 30/01/2011, 03/07/2010,03/06/2010, 14/09/2010. The simulations were run with a fixed sample time of �� seconds. As solver the “ode3 (Bogacki-Shampine)” algorithm was used. For all test cases 2700 seconds (45 minutes) were simulated.
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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
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3 System description 35 of performa
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4 Control strategy The main goal of
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4 Control strategy 39 limits are di
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4 Control strategy 41 ��
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4 Control strategy 43 cabin. This s
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4 Control strategy 45 controlled. T
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5 The disturbance estimator 47 in e
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6 The optimiser: an evolutionary al
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7 The HVAC-system controller 69 �
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7 The HVAC-system controller 71 7.5
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8 System and functionality integrat
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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
Page 157 and 158: B In- and outputs The interface bet
Page 159 and 160: Signal Unit Range Type Comment Stat
Page 161 and 162: In- and outputs 141 B.3 Error codes
Page 163 and 164: and system parameters 2 concentrati
Page 165 and 166: Simulation parameters 145 C.2 Contr
Page 167: Simulation parameters 147 Panic han
Page 171 and 172: Additional simulation results 151 D
Page 173 and 174: Optimiser code 153 real_T ∗panic_
Page 175 and 176: Optimiser code 155 52 return 10; 54
Page 177 and 178: Optimiser code 157 138 /∗ return
Page 179 and 180: Optimiser code 159 230 ∗/ ∗ ===
Page 181 and 182: Optimiser code 161 ∗ IMEA_breedin
Page 183 and 184: Optimiser code 163 414 } 416 } } st
Page 185 and 186: Optimiser code 165 if (sortedPop [
Page 187 and 188: Optimiser code 167 596 ∗ Printout
Page 189: Optimiser code 169 690 ∗ Latest R
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