Automotive Air Conditioning (eBook)

Dr. Quansheng Zhang holds a Ph.D in Mechanical Engineering from The Ohio State University. He is currently a research engineer in the Ford Motor Company in Dearborn, MI, USA. He specializes in the development of energy management strategy for electric vehicles and the optimization and control of automotive HVAC systems. Dr. Shengbo Eben Li was a postdoctoral research fellow in Mechanical Engineering from University of Michigan-Ann Arbor. He is currently an associate professor in Department of Automotive Engineering at Tsinghua University, China. He specializes in the optimization and control of autonomous vehicles, driver behavior and assistance, optimal control and estimation. Dr. Kun Deng holds a Ph.D. in Mechanical Engineering from University of Illinois at Urbana-Champaign. He is currently a research engineer at Ford Motor Company in Dearborn, MI, USA. He specializes in modeling and control of stochastic systems, optimization and control of building HVAC systems, and control system development of automated driving vehicles.

Preface 6
Contents 8
Part I Model Development 10
1 CFD-Based Modeling of Heat Transfer in a PassengerCompartment 11
1.1 Introduction 11
1.2 Governing Equations 12
1.2.1 The Mass Conservation Equation 13
1.2.2 The Momentum Equation 13
1.2.3 The Energy Equation 14
1.3 Turbulence Models 14
1.3.1 K-Epsilon Turbulence Model 14
1.3.2 SST Turbulence Model 15
1.4 Numerical Methods 16
1.4.1 Mesh Terminology and Types 16
1.4.2 Discretization Methods 18
1.5 Accuracy and Convergence 19
1.6 Summary 19
References 20
2 Model Development for Air Conditioning System in Heavy Duty Trucks 21
2.1 Introduction 21
2.2 System Overview 23
2.3 Compressor Model 25
2.3.1 Calculation of Refrigerant Flow 25
2.3.2 Calculation of Compressor Power 25
2.4 Thermal AC Model 26
2.4.1 Thermal Model Structure 27
2.4.2 Air Humidity and Latent Heat 29
2.5 Model Validation 30
2.5.1 Validation of Compressor Model 31
2.5.2 Validation of Thermal AC Model 32
2.6 Concluding Remarks 35
References 35
3 Aggregation-Based Thermal Model Reduction 37
3.1 Introduction 37
3.2 Full-Order Building Thermal Model 39
3.3 Markov Chain Analogy and Aggregation 40
3.3.1 Analogy to a Markov Chain 41
3.3.2 Discretization of the Continuous-Time Markov Chain 42
3.3.3 Aggregation of Markov Chain 43
3.3.4 Analogy to Thermal Dynamics 44
3.4 Aggregated Building Thermal Model 45
3.4.1 Aggregated Linear Thermal Dynamics 45
3.4.2 Aggregated Building Thermal Model 48
3.5 Simulation and Discussion 50
3.5.1 Simulation Setup 50
3.5.2 Recursive Bi-partition of Building Graph 51
3.5.3 Simulation of Full- and Reduced-Order Models 53
3.5.4 Simulation of Super-Zone Models 54
3.6 Conclusions and Future Directions 56
References 57
Part II Control 58
4 Robust H? Switching Control of Polytopic Parameter-Varying Systems via Dynamic Output Feedback 59
4.1 Introduction 59
4.2 Problem Statement 61
4.3 Robust Analysis via Min-Switching 64
4.4 RSOF Controller Synthesis 66
4.5 Numerical Examples 71
4.6 Conclusions 75
References 76
5 Output Feedback Control of Automotive Air Conditioning System Using H? Technique 79
5.1 Introduction 79
5.2 Automotive A/C System Description 81
5.3 Model Calibration and Validation 83
5.4 Control Design Overview 87
5.4.1 Control Objective 87
5.4.2 H? Synthesis Background 89
5.5 Design Process 90
5.5.1 Full-Order H? Controller Design 90
5.5.2 Model and Controller Order Reduction 92
5.5.3 Simulation Results 93
Appendix 98
LTI A/C Model 98
Full-Order and Reduced-Order Controller 98
References 100
6 Improving Tracking Performance of Automotive Air Conditioning System via Synthesis 102
Nomenclature 102
6.1 Introduction 103
6.2 A/C Model and H? Control 104
6.2.1 A/C System Modeling 105
6.2.2 H? Synthesis 108
6.3 Robust Analysis of H? Controller 110
6.3.1 Uncertainty Implementation 111
6.3.2 Uncertainty Analysis 115
6.3.3 Robust Stability and Robust Performance 116
6.3.4 Reference Tracking and Disturbance Rejection 119
6.4 Synthesis 122
6.4.1 Robust Stability and Robust Performance 123
6.4.2 Reference Tracking and Disturbance Rejection 125
References 127
7 Mean-Field Control for Improving Energy Efficiency 129
7.1 Introduction 129
7.2 Building Thermal Model 131
7.2.1 Configuration of HVAC System 131
7.2.2 Baseline Building Thermal Model 131
7.2.3 Reduced Building Thermal Model 133
7.3 Mean-Field Control 136
7.3.1 Local Optimal Control of a Single Zone 138
7.3.2 Coupled Model 139
7.3.3 Approximate Local Optimal Control 140
7.4 Mean-Field Control of Linearized System 142
7.5 Simulation and Discussion 143
7.5.1 Basic Setup 143
7.5.2 Simulation Results 144
7.6 Conclusions and Future Directions 146
References 146
8 Pseudospectral Optimal Control of ConstrainedNonlinear Systems 148
8.1 Introduction 148
8.2 Computational Framework of Legendre Pseudospectral Method 150
8.2.1 General Bolza-Type OCP 151
8.2.1.1 Calculation Steps by LPM 151
8.3 Implementation of Pseudospectral Method 154
8.3.1 Costate Estimation 154
8.3.2 Numerical Calculation of Collocation Points 158
8.3.3 Multi-Phase Problems 159
8.3.4 Pseudospectral Optimal Control Problem Solver 160
8.4 Application to Autonomous Vehicles 161
8.4.1 Model for Control 161
8.4.2 The Formulation of OCP 163
8.4.3 Optimization Results 163
8.4.4 Comparison with Other Methods 164
8.5 Conclusions and Remarks 165
References 166
Part III Optimization 168
9 Multi-Objective Supervisory Controller for Hybrid Electric Vehicles 169
9.1 Introduction 169
9.2 Battery Aging Model and Capacity Loss Reference Trajectory 172
9.2.1 Capacity Loss Reference for Cycle-Life 173
9.3 Vehicle Simulator 175
9.3.1 Battery Cell Model 177
9.3.1.1 Electrical Model 178
9.3.1.2 Thermal Model 179
9.3.1.3 Aging Model 179
9.3.2 Battery Pack Model 180
9.4 Well-Posedness of Multi-Objective Control Problem 180
9.5 Aging-Limiting Energy Management Problem Formulation 182
9.6 Aging-Limiting Pontryagin's Minimum Principle Problem Solution 185
9.6.1 Comparison with Standard PMP Solution 191
9.7 Remarks on Multi-Objective Optimal Control Formulation 193
9.7.1 Multi-Objective PMP Problem 193
9.7.2 ECMS with Aging 195
9.8 Penalty Function on Capacity Loss 196
9.9 AL-PMP Solution via Map-Based Tuning 199
9.9.1 Tuning Algorithm Flowchart 200
9.10 Simulation Results 202
9.10.1 Results for Different Ambient Temperatures 206
9.10.2 Results with Penalty Function 209
9.11 Conclusions 214
References 215
10 Energy-Optimal Control of an Automotive Air Conditioning System for Ancillary Load Reduction 218
10.1 Introduction 218
10.2 Description of the A/C System Model 220
10.2.1 Compressor Model 221
10.2.2 Heat Exchangers Models 222
10.2.3 Final Form of the A/C System Model 226
10.2.4 Model Calibration and Validation 227
10.3 Formulation of the Energy Optimization Problem 230
10.3.1 Solution and Analysis 231
10.4 Control Design for A/C System Energy Management 234
10.4.1 Solution of the Embedded Optimal Control Problem 238
10.4.2 Projection Results and Comparison with Dynamic Programming 240
References 244
11 Modeling Air Conditioning System with Storage Evaporator for Vehicle Energy Management 247
Nomenclature 247
11.1 Introduction 248
11.2 Modeling A/C System with Storage Evaporator 249
11.2.1 Lumped-Parameter Modeling Approach 250
11.2.2 Refrigerant Dynamics 252
11.2.3 PCM Mode Switching 253
11.2.4 Descriptor Form 255
11.3 On/Off Cycle Evaluation of Storage Evaporator 256
11.4 Energy Management Strategy 259
11.4.1 Difficulties Faced by DP Algorithm 259
11.4.2 Hybrid Minimum Principle 260
11.4.3 Preliminary Application to A/C System 261
References 264
12 Cruising Control of Hybridized Powertrain for Minimized Fuel Consumption 267
12.1 Introduction 267
12.2 HEV Model and Problem Statement 268
12.2.1 HEV Model for Control 268
12.2.1.1 Vehicle Longitudinal Dynamics 269
12.2.1.2 Battery and Motor Model 270
12.2.2 Performance Index for Fuel Economy 271
12.2.3 Constraints for Inputs and States 272
12.2.4 Optimal Control Problem 273
12.3 Legendre Pseudospectral Method and Knotting Technique 273
12.3.1 Legendre Pseudospectral Method 274
12.3.2 Knotting Technique 274
12.3.2.1 Step1: Conversion of Time Interval 275
12.3.2.2 Step2: Collocation Points and Approximation 275
12.3.2.3 Step3: Conversion of the State Space Equations 276
12.3.2.4 Step 4: Conversion of the Cost Function 276
12.3.2.5 Step 5: Connection Constraints 276
12.4 Optimization Results 277
12.4.1 Speed-PnG Cruising Operation 277
12.4.1.1 Setting Conditions 277
12.4.1.2 Solving Results 278
12.4.1.3 Explication the Fuel Economy of Speed-PnG 279
12.4.2 SOC-PnG Cruising Operation 280
12.4.2.1 Setting Conditions 280
12.4.2.2 Solving Results 280
12.4.2.3 Explication the Fuel Economy of SOC-PnG 281
12.4.3 Comparison Between Speed-PnG and SOC-PnG 281
12.5 Fuel-Saving Mechanisms 282
12.6 Compromised Rules and Performance 286
12.7 Conclusions and Remarks 287
References 288
Part IV Fault Diagnosis 290
13 Fault Detection and Isolation with Applications to Vehicle Systems 291
13.1 Introduction to Fault Detection and Isolation 291
13.2 Observer Design Methods for FDI 293
13.2.1 Dedicated Observer Scheme 294
13.2.2 Generalized Observer Scheme 295
13.2.3 Example of FDI for Brake-by-Wire System 297
13.2.3.1 Caliper Force Observer Design 298
13.2.3.2 Motor Position Observer Design 299
13.2.3.3 Fault Detection and Isolation 300
13.3 NPERG Method 302
13.3.1 Conditions and Capabilities of a Diagnostic System 303
13.3.1.1 Diagnosis of a Single Fault 303
13.3.1.2 Diagnosis of Multiple Faults 304
13.3.2 FDI Algorithm Design for Nonlinear Dynamic Systems 305
13.3.2.1 The Nonlinear Parity Equation Residual Generation Scheme 306
13.4 Inverse Models Using Sliding Modes 307
13.4.1 Design of State Estimator for Linear Systems 307
13.4.2 Input Fault Estimation 308
13.5 Fault Diagnosis of Li-Ion Batteries 309
13.5.1 Battery Model 309
13.5.2 Diagnostic Problem 311
13.5.3 Fault Diagnosis Scheme 312
13.5.3.1 Core Temperature Estimation 312
13.5.3.2 Capacitor Voltage Forward Model 313
13.5.3.3 Temperature Observer 314
13.5.3.4 Simulation Results 315
References 317
14 Fault Detection and Isolation of Automotive Air Conditioning Systems using First Principle Models 320
Nomenclature 320
14.1 Introduction 321
14.2 Recent Development of VCC Fault Diagnosis 322
14.3 Fault Modeling Using MBM A/C Model 324
14.3.1 Overview of A/C System Faults 324
14.3.2 Actuator Fault 325
14.3.3 Sensor Fault 326
14.3.4 Parameter Fault 327
14.4 Experiment System 327
14.5 Results and Analysis 329
14.5.1 Actuator and Sensor FDI 329
14.5.1.1 Performance Evaluation 330
14.5.1.2 Limitations 332
14.5.2 Parametric FDI 334
References 337
15 Evaluating the Performance of Automated Fault Detection and Diagnosis Tools 339
15.1 Introduction 339
15.2 AFDD Method Categorization 341
15.3 AFDD Performance Evaluation Terminology 342
15.4 AFDD Performance Evaluation Method 343
15.4.1 Faulted and Unfaulted Operation 344
15.4.2 Test Case Outcomes 344
15.4.3 Test Case Outcome Rate Calculations 345
15.4.4 Fault Types 346
15.4.5 Input Data 347
15.5 Case Study 349
15.6 Results 350
15.6.1 Discussion of Case Study Results 352
15.7 Conclusions 352
References 353
Index 354