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

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12 Controller evaluation Even though the MUTE car prototype will not be ready before autumn 2011, some testing was done with the help of simulations. In this way, the functioning of the controller in combination with the available hardware is shown. 12.1 Hardware-in-the-loop test The leading institute for the MUTE development, the Lehrstuhl für Fahrzeugtechnik Mün- chen has built up a hardware-in-the-loop test rig. A special computer simulates the car and some sensors. This simulation is communicating over CAN-busses 1 with the Mi- croAutoBox car computer (described in Section 10.7). The actuator and sensor hardware is directly connected to this box. With this configuration, the interaction of electrics, hardware control software and the (simulated) car can already be tested. A schematic drawing of the test rig (showing only the relevant hardware components for the climatisation) is found in Figure 12.1. In this way, tests of the HVAC-unit and first parameterisations were done. One very important result is the execution time of the optimiser algorithm. Its turnaround time was measured to be �� seconds (in the automatic mode). This equals a processor load of 0.09% with the sample time of two seconds. The disturbance estimator also showed a very short turnaround time of 0.00030 seconds. The remaining control parts are included in a“�� ”-task because have the same sample time as other blocks in the MUTE software and can therefore not be regarded separately. This task showed a turnaround time of 0.00028 seconds. As the turnaround times are very low, they showed to be dependent on the overall system sample time. This is set to 10 milliseconds. 1 Controller-area-network, a standard bus in vehicles.
12 Controller evaluation 117 �� Figure 12.1: Overview of the hardware-in-the-loop test rig for the climatisation. All required and planned software as driving dynamics calculations are not imple- mented so far. Therefore, no information about the overall load of the computer can be given. However, he results show that the developed control algorithm is fast enough and suitable for the computational power that is available in a car. 12.2 Optimiser performance evaluation It is important for the optimiser algorithm to find good solutions fast enough to be able to compensate for changes that occur in the temperature or other measurement values. This can be tested in a simulation. A static example is created where all temperatures and other inputs are constant. Now, the optimiser results of each sample are compared to the true optimum. In this way it is seen, if the optimisation process is fast enough. To compare the solutions, their fitness is used. This is calculated by the weighted sum of the objective results. A small fitness difference does not necessarily mean that the solutions are close, but it is usually
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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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Page 85 and 86: 6 The optimiser: an evolutionary al
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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
Page 117 and 118: 10 Hardware and characteristics of
Page 129 and 130: 11 Matlab implementation The climat
Page 131 and 132: 11 Matlab implementation 111 Only r
Page 133 and 134: 11 Matlab implementation 113 left i
Page 135: 11 Matlab implementation 115 tion a
Page 139 and 140: 12 Controller evaluation 119 be not
Page 141 and 142: 12 Controller evaluation 121 Number
Page 143 and 144: 12 Controller evaluation 123 Table
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Page 147 and 148: 12 Controller evaluation 127 ��
Page 149 and 150: 13 Conclusion 129 13.2 Possible imp
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
Page 163 and 164: and system parameters 2 concentrati
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 [
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Optimiser code 167 596 ∗ Printout
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Optimiser code 169 690 ∗ Latest R
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