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

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5 The disturbance estimator The structure of the systems seen in Chapter 3 is nonlinear with many time dependent values. An observer requires a certain structure or linearisation of the model. The structure is not given. Linearisation would have to be repeated whenever a bigger change is detected and is for this reason supposed to be too calculation expensive. Therefore, a simpler approach is taken here. 5.1 Estimation of disturbances All effects not taken into account by the system model and errors in the parameters are regarded as disturbances. For the thermal cabin system from equation 3.5 this would be �� which represents heat sources like the passengers, electronics or heat loss through air gaps. Here the sun is also included in this term, which has to be done if no solar radiation sensor is available. �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� (5.1) �� The disturbance in the car cabin air humidity represents the humidity sources, called �� in equation 3.8. It also covers humidity loss through air gaps or open windows. �� ¡ �� ¡ �� ¡ �� (5.2) External heat fluxes disturbing the windscreen system that were called � ��
5 The disturbance estimator 47 in equation 3.16, are estimated by this equation: �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� ¡ �� (5.3) When all the temperatures and humidity values as well as the material and car constants are known, the only the derivatives are left to calculate. Since sensor values are usually noisy, a robust way has to be found in order not to amplify these disturbances and introduce oscillations into the controller. The way taken here is to employ the slope of a linear regression as derivative. This permits to use a (configurable) number of samples and thus reduce noise. Having � measurement values taken with a sample time ¡� at times �� ¡ ¡� the slope is calculated by: �� ¡ �� ¡ �� ¡ � �� (5.4) � � �� Approximations of higher order can be used as well, but are not regarded any further in this context. The increased accuracy does not justify the more complicated calculation and may even counteract the low-pass filtering. For the other inputs to the equations, � values are averaged as well to keep the influence of noise or input jumps low. With the choice of � the amount of this low-pass filtering can be adapted. 5.2 Simulation as replacement for sensors Some of the information required by the objectives might not be available in a normal car. One sensor which is only found in very few vehicles, but quite important when trying to safe energy and using recirculated air, is the carbon dioxide sensor. Simulating this state entirely can give an idea whether the CO2 concentration is high or not. However, one has to keep in mind that this will represent only a rough estimation. Generally, the real concentration will be lower due to air leakage - which is not taken into account here.
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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
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: 4 Control strategy 45 controlled. T
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 115 and 116: 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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