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

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Bibliography 134 [32] P.J. Fleming and R.C. Purshouse. Evolutionary Algorithms in Control Systems Engineering: a Survey. Control engineering practice, 10(11):1223–1241, 2002. ISSN 0967-0661. [33] Q. Wang, P. Spronck, and R. Tracht. An Overview of Genetic Algorithms Applied to Control Engineering Problems. In Machine Learning and Cybernetics, 2003 Inter- national Conference on, volume 3, pages 1651–1656. IEEE, 2003. ISBN 0780381319. [34] Harald Langer. Extended Evolutionary Algorithms for Multiobjective and Discrete Design Optimization of Structures. PhD thesis, Technische Universität München, 2005. [35] Helen G. Cobb and John J. Grefenstette. Genetic Algorithms for Tracking Chang- ing Environments. In Stephanie Forrest, editor, ICGA, pages 523–530. Kaufmann, Morgan, 1993. ISBN 1-55860-299-2. [36] Steven Daly. Automotive Air Conditioning and Climate Control Systems. Butterworth-Heinemann, 1 edition, October 2006. ISBN 978-0750669559. [37] G. A. Spinas and U. Heitz. Ernährung- und Energiestoffwechsel. Projekt MEGRU - MEdizinische GRUndlagen, 2004. [38] Lehrstuhl für Fahrzeugtechnik, TU München. TUM Electric Mobility Homepage. http://www.mute-automobile.de/, 2011. [Online; as May 18th 2011]. [39] Webasto AG. Webasto Caravan - Heating - AirTop 2000 ST product information. http://motorhome.webasto.co.uk/, 2011. [Online; as May 18th 2011]. [40] Meteorologisches Institut der Universität München. Meteowiki Messwerte München. http://www.meteo.physik.uni-muenchen.de/dokuwiki/doku.php?id=wetter:stadt, 2011. [Online; as May 18th 2011].
Appendices
Page 1:
ISSN 0280-5316 ISRN LUTFD2/TFRT--58
Page 5:
Diploma thesis subject Problem stat
Page 8 and 9:
Acknowledgements ii Acknowledgement
Page 10 and 11:
Acknowledgements iv 3.1.4 Windscree
Page 12 and 13:
Acknowledgements vi 8.5 System simu
Page 14 and 15:
Nomenclature viii Nomenclature Symb
Page 16 and 17:
Nomenclature x Other symbols Symbol
Page 18 and 19:
Nomenclature xii Subscript Descript
Page 21 and 22:
1 Introduction Energy consumption t
Page 23 and 24:
1 Introduction 3 adjustment of the
Page 25 and 26:
2 Control objectives 5 2.1 Windscre
Page 27 and 28:
2 Control objectives 7 2.2 Comfort
Page 29 and 30:
2 Control objectives 9 � Metaboli
Page 31 and 32:
2 Control objectives 11 ��
Page 33 and 34:
2 Control objectives 13 Figure 2.6:
Page 35 and 36:
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 71 and 72:
Page 73 and 74:
Page 75 and 76:
Page 77 and 78:
Page 79 and 80:
Page 81 and 82:
Page 83 and 84:
Page 85 and 86:
Page 87 and 88:
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 95 and 96:
Page 97 and 98:
Page 99 and 100:
Page 101 and 102:
Page 103 and 104: 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: Bibliography 133 [22] M. Wang, T.M.
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 and 168: Simulation parameters 147 Panic han
Page 169 and 170: Simulation parameters 149 C.5 Test
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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