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

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10 Hardware and characteristics of the MUTE prototype 108 and “turn counterclockwise”. These are fed to H bridges 1 , which power the motors which are running on 12 Volt. The main fan is driven by a �� PWM signal whose active cycle is given by an analogue output. The servo motor - potentiometer combination for the heater control might be replaced with a digital potentiometer integrated circuit. 10.8 Parameterisation for the MUTE car Some parameters related to the MUTE car could only be estimated since the car is not built, yet. They have to be measured and adapted once a prototype exists in order to achieve the best control. The heat transfer can be measured by testing how much heating power is required to keep the car’s interior at a constant temperature. A better estimation of the heat capacity of the interior parts (seats etc.) built into the prototype a can be done. The fan characteristics in the system with all ducts and outlets attached has to be measured and the curve (shown in Figure 10.4) has to be adapted. Moreover, the relationships between the flap opening angles and the air mass flow percentage passing it have to be quantified with the ducts attached (cf. Section 3.2.5). All other air flow related values in the objectives could be corrected as well. The air speed on the windscreen (equation 3.11 and the air speed towards the passengers (cf. Section 10.2) can be adapted to measurement data. One important relationship to find is the one between the battery cooling air flow and its PWM-set value (see Section 10.5). The heater potentiometer angle to set- temperature will also need calibration. 1 A semiconductor device used for motor control.
11 Matlab implementation The climate controller is integrated into the Matlab Simulink model for the MUTE car control. All time critical tasks like the driving functionality are done in this program running on the dSpace MicroAutoBox. The in- and output specifications for the climate control block can be seen in Appendix B. 11.1 Model structure The sensor input values will first enter a block called input validator (in Figure 11.1 yellow on the left side). The containing routines check, if the data is within the sensor limitations and if the servo motors are working. The scaling of the values into the later assumed ranges and unit conversion are done as well. The servo motor positions are transferred into percent of air flow trough them. The absolute humidity of the cabin and the ambient air is calculated. Found errors and the sensor values (or replacements) are the output. The model has three working modes: Automatic, manual and off. A logic (grey block on the left in Figure 11.1) decides which one of them is active. Disabling not used mode-blocks is done to safe computing time. The second grey block uses these activation signals to forward the desired control signal to the output. If the controller is switched off (at start-up or during shut-down) the off-settings (cyan, at the bottom) are activated. Fan and heater are turned off and the air inlet flap brought to the full recirculation position. The automatic control in the upper cyan block becomes active if the controller is switched on and the automatic or the precondition mode is chosen. Its structure will be described in Section 11.2. The activation of the manual mode permits the user to directly control the output temperature and air flow as well as the outlet (foot/defrost).
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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 77 and 78: 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
Page 117 and 118: 10 Hardware and characteristics of
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Page 131 and 132: 11 Matlab implementation 111 Only r
Page 133 and 134: 11 Matlab implementation 113 left i
Page 135 and 136: 11 Matlab implementation 115 tion a
Page 137 and 138: 12 Controller evaluation 117 ��
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
Page 145 and 146: 12 Controller evaluation 125 In the
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
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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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