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

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3 System description 36 ily switch to another heat source to heat and de-ice the front heat exchanger if serious icing is detected. The source could be the electric motor as shown in Figure 3.12. Ice detection can be made directly by observing the pressure drop through the evaporator and indirectly through the COP of the heat pump. To build up models for the icing in order to simulate or even predict it is subject to research at the moment.
4 Control strategy The main goal of this thesis is to design a control strategy that fulfils the control objectives described in Chapter 2 in the best possible way. 4.1 Existing control structures As pointed out in Section 1.1 most of the automatic “climate” control systems include only automatic temperature control. These controllers are often quite simple in order to require less powerful computing hardware. A simple hand-tuned relation between ambient, cabin and outlet temperature is used. Linearly to the output temperature the fan speed is set. Static rules are implemented to handle situations when for example the coolant is too cold. This approach does only partially reflect comfort (by a fixed temperature) and does not reduce energy consumption if possible. An approach based on a CO2 sensor [3] only uses full recirculation or fresh air, but not a mixture of both. It also does not include other objectives than the air qual- ity. Another automatic defog control by Wang et al. (2004) [22] simply overrides the normal control. The integration into a control system is done by Jung, Lee and Jang [23], but apart from a PID-estimator in the fog-detection no details are given. In general these developments focus on single parts of the control, but not on an all-encompassing approach. Information about the core algorithms difficult to obtain, since they are mostly kept as business secrets. One published approach by Wang et al. [24] uses an energy balance model and look-up tables to determine the required outlet air flow and its temperature to maintain a comfortable cabin. To this a transient part used to heat up (or cool down) the cabin is added. In this way a simple model based control is implemented. However,
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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: 3 System description 35 of performa
Page 59 and 60: 4 Control strategy 39 limits are di
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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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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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