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

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3 System description 26 The maximum time constant is easily identified to equal infinity. This is the case if no air is exchanged with the environment and nothing is changing. Furthermore, the minimum values are found to be (neglecting the water mass in the air) �� for the MUTE car. A much faster reaction is seen as no further “storage” exists apart from the air. For the windscreen temperature the changing variables are the convection heat transfer coefficients. �� ¡ �� ¡ �� ¡ �� ¡ �� (3.22) The highest time constant is found with a standing car giving �� . Looking for the maximum, the estimation of �� (equation 3.10) needs to be consulted with the maximum car speed (here 100 �� is chosen). In this way a �� of approximately �� seconds is obtained. These values are used for controller sample time considerations and give an indi- cation what type of control can be used. Due to the involved car parameter they will be different between car types. Moreover, some (as the infinity) will not occur in reality due to air leakages not considered here. Consequently some values might be higher than in a real car. The calculations still give a rough estimation of the timescale in which changes in the systems occur. The results show that these happen in the range of several seconds. 3.2 The HVAC-unit The HVAC-unit is the actor in the cars thermal system. It draws the air from the outside or the car cabin (recirculation), cools and/or heats it up and defines the way to blow it into the cabin. The heat sources (and sinks) are regarded in the following Section 3.3 for a heat pump system. For an HVAC-unit several types and layouts exist. The most common type, the air mix type, is shown in Figure 3.4. The air is drawn by a fan from the outside or the car - chosen by the recirculation flap. Before flowing through the fan, a filter (not drawn in the picture) prevents dust and other pollution from entering the car. The air passes
3 System description 27 Figure 3.4: A schematic drawing of an air mix type HVAC-unit. [2] the evaporator and is - if the evaporator is activated - cooled down and dehumidified. A part of it is then, depending on the air mix flap, heated by the engine’s waste heat and/or another heating source (typically a PTC). Finally the air is (partly) mixed and distributed to the different outlets. Different temperatures for the outlets can be achieved by a good duct design and not fully mixing the cold and hot air. The foot area is usually tempered by hotter air than the face outlets which blow directly to the passengers. To defrost and defog the front windscreen, the defrost outlets are used. Even if the layout in an electric car may change, some basic equations can be given. 3.2.1 The fan Each fan has a characteristic curve determining the pressure drop depending on the air flow. This curve varies according to the applied voltage. A characteristic curve relating the pressure drop and the air flow can also be found for the HVAC system including the air ducts, the filter, the heat exchanger and the vents. The intersection of both curves determines the air flow into the car. An example is seen in Figure 3.5. Several “disturbances” change the characteristic curve of the HVAC system. Some of the causes are very hard to take into account, such as blocked out- or inlets or other unpredictable events. With measurements in numbers the influence of the different ducts
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: 3 System description 25 defrost tem
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 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: 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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