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

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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
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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
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
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8 System and functionality integrat
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8 System and functionality integrat
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9 The MUTE project 95 order to fit
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10 Hardware and characteristics of
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11 Matlab implementation The climat
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11 Matlab implementation 111 Only r
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11 Matlab implementation 113 left i
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11 Matlab implementation 115 tion a
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12 Controller evaluation 117 ��
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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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