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

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13 Conclusion The hardware control is working in the HIL-test rig. The control program showed its suitability for an online application by very short execution times. The evolutionary optimiser algorithm proofed its efficiency and its ability to adapt to changes in a test on a static problem with one switched parameter. In the simulations the control design has proven that it is able to meet the demands. Energy savings are achieved through (partial) recirculation in a normal usage. Fogging can be prevented, and in extreme conditions the control - limited by the hardware - reacts to the situation. The oscillations that occur in certain situations are always limited. been created. It can therefore be stated that a viable automatic control for a prototype car has 13.1 Extension for full car air conditioning In order to add full car air conditioning capability to the implemented controller the air humidity design variable � �� has to be integrated like in the general description in Chapter 4. All related settings, like the boundaries or the panic handlers, need to be extended. In addition, objectives may be required for certain system like the heat pump (see Sections 8.1.7 and 8.2). A power consumption model for the cooling system needs to be integrated into the controller-internal simulation. Finally, a control mechanism adapted the cooling system has to be included in the HVAC-system control. The result is better control over the cabin air humidity and an increased comfort in hot weather compared to the results in Figure 12.6.
13 Conclusion 129 13.2 Possible improvements At large, more or better information allows better control. Better system models can increase the control accuracy. Inclusion of the interior thermal masses temperature as a separate state could improve the disturbance estimation, but would require an additional sensor. Air leakage could be integrated - if known - into the model, too. In general, a validation of the models will be required to some extent to assure good control in the real car. Not only the system, but also the objective models can be improved, most impor- tantly the thermal comfort model. For example, the effect of air jets with temperatures different from the cabin air temperature could be included. On the hardware side a better temperature and comfort estimation could be ob- tained with a solar sensor, especially when it is coupled with an adapted comfort model. For better carbon dioxide concentration values a sensor could be added. With more ac- curate values, more use of recirculation could be made, resulting in savings of heating energy. In particular this applies if water in the cabin (from shoes or clothes) causes the CO2 simulation to give too high values. For the HVAC-system, better control over the air flow is required. Influences like the car speed, flaps or recirculation can be taken into account by different means. One way is to measure the effects and to implement a model to compensate for them. Another way are additional sensors capturing the pressure drop and allowing closed-loop control. Finally, the hardware can be adapted to reduce some effects. This is possible for the recirculation with the help of a so-called ram door (cf. Section 3.2.1). The HVAC-unit in the MUTE project is taken from a “normal” car which will probably cause minor problems in the way it is used. The openings and the flap are not designed for partial recirculation. This can lead to direct air inflow through the recirculation path at higher car speeds. An adapted design would also reduce the pressure drop, the acoustics, the size, and it could improve the controllability. 13.3 Outlook The designed and implemented controller includes a means of using recirculated air to reduce energy consumption. Simultaneous it takes into account comfort and windscreen
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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
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Nomenclature x Other symbols Symbol
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Nomenclature xii Subscript Descript
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1 Introduction Energy consumption t
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1 Introduction 3 adjustment of the
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2 Control objectives 5 2.1 Windscre
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2 Control objectives 7 2.2 Comfort
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2 Control objectives 9 � Metaboli
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2 Control objectives 11 ��
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2 Control objectives 13 Figure 2.6:
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2 Control objectives 15 2.4 Influen
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3 System description 17 or heat com
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3 System description 19 calculated
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3 System description 21 ��
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3 System description 23 � � �
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3 System description 25 defrost tem
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3 System description 27 Figure 3.4:
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3 System description 29 width modul
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3 System description 31 ��
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3 System description 33 shown in Fi
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3 System description 35 of performa
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4 Control strategy The main goal of
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4 Control strategy 39 limits are di
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4 Control strategy 41 ��
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4 Control strategy 43 cabin. This s
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4 Control strategy 45 controlled. T
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5 The disturbance estimator 47 in e
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6 The optimiser: an evolutionary al
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7 The HVAC-system controller 69 �
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7 The HVAC-system controller 71 7.5
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8 System and functionality integrat
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8 System and functionality integrat
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Page 115 and 116: 9 The MUTE project 95 order to fit
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Page 135 and 136: 11 Matlab implementation 115 tion a
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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
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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
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