Identification for Automotive Systems

Cover......Page 1
Front matter......Page 2
Introduction......Page 16
Methods for System Identification of Automotive Systems......Page 20
Applications......Page 22
Conclusions and Outlook......Page 23
References......Page 24
Introduction......Page 26
Proposed Modeling Environment......Page 29
Physical Modeling Environment......Page 30
Empirical Modeling Environment......Page 35
Integration of Physical and Empirical Models......Page 36
Approach A......Page 37
Approach B......Page 41
Summary......Page 45
References......Page 46
Introduction......Page 48
Example 1: Grey-Box Identification for Yaw Control of Four-Wheeled Vehicles ([8])......Page 49
Example 2: Black-Box Identification of Engine-to-Slip Dynamics for Motorcycle Traction vControl ([9])......Page 52
Example 3: Estimation-Oriented Identification for Sensor Reduction in Semi-active Suspension Systems for Cars ([10])......Page 55
Example 4: Direct Braking Control Design for Two-Wheeled Vehicles ([11])......Page 56
Conclusions......Page 60
References......Page 61
Introduction......Page 63
LPV System Identification Overview......Page 64
LPV Subspace Identification......Page 65
Analytical LPV Modeling......Page 70
Simulation Results......Page 71
Conclusions......Page 73
References......Page 74
A Tutorial on Numerical Methods for State and Parameter Estimation in Nonlinear Dynamic Systems......Page 76
How to Calibrate Models? The Modeling Cycle......Page 77
Approaches for the Optimization of Dynamic Systems......Page 78
Maximum Likelihood Estimation for Differential Equation Models......Page 80
Smoothing Heuristics......Page 81
The Importance of Convexity......Page 82
Least Squares Terms versus l1-Norms......Page 83
Generalized Gauss-Newton Methods......Page 84
Schl¨oder’s Trick or the Lifted Newton Type Method for Parameter Estimation......Page 86
Modular Forward Lifting Techniques......Page 87
Automatic Backward Lifting Techniques......Page 89
State and Parameter Estimation with ACADO Toolkit......Page 91
Conclusions......Page 93
References......Page 94
Evolutionary Computation and Genetic Algorithms......Page 98
Basics of Genetic Programming......Page 100
Evolutionary Structure Identification Using Genetic Programming......Page 101
Designing Virtual Sensors for Emissions (NOx, Soot) of Motor Engines......Page 103
Quality Pre-assessment in Steel Industry Using Data Based Estimators......Page 104
Enhanced Selection Concepts......Page 105
On-Line and Sliding Window Genetic Programming......Page 107
Cooperative Evolutionary Data Mining Agents......Page 108
Algorithm Analysis: Population Diversity Dynamics......Page 110
References......Page 112
Introduction......Page 119
Generalization of Conventional Markov Chain through Interval and Fuzzy Granulation......Page 121
Markov Chains with Interval Encoding......Page 122
Markov Chains with Fuzzy Encoding......Page 123
General Algorithm for On-Line Learning of the Transition Probability Matrix......Page 127
Markov Chain Models of Vector-Valued Signals......Page 131
Conclusions......Page 134
References......Page 135
Introduction......Page 137
Problem Statement......Page 139
Linear Parameters......Page 142
Noisy Experimental Data......Page 145
Reduced-Order Modelling......Page 146
References......Page 152
Introduction......Page 154
A Stochastic Framework......Page 156
Stochastic Applications-Oriented Experiment Design......Page 157
Alternative Formulations......Page 158
Choice of Decision Variables......Page 159
Using the Set of Acceptable Models......Page 160
How to Handle System Dependency of the Optimal Solution......Page 162
Robust Designs......Page 163
Adaptive and Sequential Designs......Page 164
Cost of Complexity......Page 165
Consistent Estimation with Unmodelled Dynamics......Page 166
References......Page 167
Introduction to Engine Calibration......Page 170
Nonlinear Statistical Dynamic Modeling......Page 172
Test Design......Page 174
Modeling......Page 177
Virtual Engine Calibration......Page 181
Conclusion......Page 186
References......Page 187
Introduction......Page 188
Main Limitations of the Mean Value Representation......Page 192
In-Cycle Variation......Page 194
Distributed Pressure Losses: Flow through Porous Media......Page 197
Three-Dimensional Flow......Page 201
EGR Flow Repartition......Page 202
Compressor Surge......Page 204
Summary......Page 206
References......Page 208
Introduction......Page 210
The Fuel Path as System Example......Page 212
Efficient High Pressure Pump Model......Page 213
Injector Remanence Compensation......Page 219
References......Page 224
Introduction......Page 225
Experimental Equipment......Page 227
Experiment Design......Page 228
Identification of Local Linear Models......Page 229
Identification of Models for Extended Output Set......Page 231
Reducing the Number of Models......Page 232
Identification Results......Page 237
Discussion......Page 238
Conclusions......Page 240
References......Page 241
Introduction......Page 242
Method Outline......Page 243
Observability......Page 246
Unobservable Modes and Covariance Growth......Page 247
Method for Bias Compensation......Page 248
Method Evaluation......Page 249
Study 1: Simulation......Page 250
Study 2: Experimental Data......Page 252
Conclusions......Page 256
References......Page 257
Introduction......Page 258
Background......Page 259
Model Type and Requirements......Page 262
Internal Combustion Engine Modeling Challenges......Page 265
Constrained Domains......Page 266
Identification of Internal Combustion Engine Models......Page 267
Identification of Static Nonlinear Engine Components......Page 268
Identification of Overall Model Steady State Input-Output Response......Page 272
Identification of Overall Model Dynamic Input-Output Response......Page 274
A Comment on Component versus Overall Identification......Page 275
Modeling Results......Page 278
Conclusion......Page 281
References......Page 282
Introduction......Page 284
Modeling......Page 285
Cylinder Wall Temperature Dynamics......Page 286
Temperature Trace......Page 287
Model Inputs and Outputs......Page 290
Optical Engine......Page 291
Conventional Engine......Page 292
Calibration of Heat Transfer Parameters......Page 293
Calibration of Integration Times......Page 294
Predictive Control of Combustion Phasing......Page 297
Experimental Results......Page 298
Robustness Towards Disturbances......Page 299
Discussion......Page 300
References......Page 301
Comparison of Sensor Configurations for Mass Flow Estimation of Turbocharged Diesel Engines......Page 304
Introduction......Page 305
Turbochared Engine System Description and Mean-Value Engine Model......Page 306
Open Loop Observer......Page 311
Extended Kalman Filter......Page 315
Estimation Results......Page 317
Two-Sensor Observer: pi, ntc......Page 318
Two-Sensor Observer: pi, px......Page 321
References......Page 325
Optimal Finite and Receding Horizon Control for Identification in Automotive Systems......Page 328
Introduction......Page 329
Optimization of Identification Trajectory......Page 330
General Methodology......Page 331
Optimal Trajectories for Transient Fuel Identification......Page 332
General Methodology......Page 334
Identifying Transient Fuel Parameters......Page 336
General Methodology......Page 340
Estimating Transient Fuel Model Parameters......Page 343
Steady-State Engine Mapping......Page 344
Concluding Remarks......Page 347
References......Page 348
Back matter......Page 350