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

More documents

Recommendations

Info

4 Control strategy 40 Table 4.1: Influence matrix of the controllable parameters and the control objectives. Inlet air parameters � �� Fogging affinity ¤ ¤ ¤ £ ¤ Thermal comfort ¤ ¤ ¤ £ ¤ Humidity comfort ¤ ¤ ¤ £ £ Air quality £ ¤ £ ¤ £ Air speed comfort ¤ ¤ £ £ ¤ Energy consumption ¤ ¤ ¤ ¤ £ ¤: has direct influence on the objective £: has no direct influence on the objective between them has to be found. It can be seen in Table 4.1 that it is not possible to control one independently of the others. In common climate control systems this is often solved by ignoring all objectives except the one most important at the moment (or not regarding some at all). While this leads to easier controllers it affects the overall performance since only one objective is (mainly) regarded at a time. In order to achieve the best performance an integral solution is needed. A quite important difference to usual automatic control problems is that it is not precision that is most important. Correct decisions have to be taken dependent on many inputs. Due to thermal resistances and storage, time requirements are also comparably low (cf. Section 3.1.5). The solution presented here consists of an estimator generating required values that are not directly measured to improve the quality of the model predictions. The outer loop controller is an optimiser, trying to find the best compromise between the objectives under given conditions. The third component uses the given optimised set-point to control the HVAC-unit fan, heater and cooler. A simplified scheme of this concept is shown in Figure 4.1. The downsides are that it is computationally expensive, mainly because of the optimiser. On the other hand, computational power gets cheaper very quickly. The flexibility is an advantage: Objectives can be added without much trouble because of a model-based and modular design. Compared to a separate controller for each objective, the results of which are blended, it allows smooth transitions without a lot of tuning. Advantages over a fuzzy-logic are the flexibility and the model instead of the experience-
4 Control strategy 41 �� Figure 4.1: The simplified control concept. �� based approach, giving good signals without requiring rules. The trade-off between the solution quality and the computational effort can be adapted with parameters for the optimisation algorithm. 4.4 The disturbance estimator As sensors and their integration cost money, it is preferable to have as few as possible. Furthermore, some influences onto the system do not have to be known exactly or cannot be measured (in a reasonable way). Disturbances of the ideal models like air leakage or quantities as the human water emission are not known or captured by a sensor. Therefore, they are estimated. The disturbance estimator filters the desired information out of the measured signals with the help of the system model. If the constants of the car used in equation 3.5 and the cabin temperature are known, it can be used to calculate the heat loss (or heat
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: 4 Control strategy 39 limits are di
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 111 and 112:
8 System and functionality integrat
Page 113 and 114:
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 119 and 120:
Page 121 and 122:
Page 123 and 124:
Page 125 and 126:
Page 127 and 128:
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 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
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?