Skip to:Content
|
Bottom
Cover image for Dynamic modeling and predictive control in solid oxide fuel cells : first principle and data-based approaches
Title:
Dynamic modeling and predictive control in solid oxide fuel cells : first principle and data-based approaches
Personal Author:
Publication Information:
Hoboken, N.J. : John Wiley & Sons Ltd., c2013
Physical Description:
xxi, 323 p. : ill. ; 26 cm.
ISBN:
9780470973912

Available:*

Library
Item Barcode
Call Number
Material Type
Item Category 1
Status
Searching...
30000010306255 TK2931 H83 2013 Open Access Book Book
Searching...

On Order

Summary

Summary

The high temperature solid oxide fuel cell (SOFC) is identified as one of the leading fuel cell technology contenders to capture the energy market in years to come. However, in order to operate as an efficient energy generating system, the SOFC requires an appropriate control system which in turn requires a detailed modelling of process dynamics.

Introducting state-of-the-art dynamic modelling, estimation, and control of SOFC systems, this book presents original modelling methods and brand new results as developed by the authors. With comprehensive coverage and bringing together many aspects of SOFC technology, it considers dynamic modelling through first-principles and data-based approaches, and considers all aspects of control, including modelling, system identification, state estimation, conventional and advanced control.

Key features:

Discusses both planar and tubular SOFC, and detailed and simplified dynamic modelling for SOFC Systematically describes single model and distributed models from cell level to system level Provides parameters for all models developed for easy reference and reproducing of the results All theories are illustrated through vivid fuel cell application examples, such as state-of-the-art unscented Kalman filter, model predictive control, and system identification techniques to SOFC systems

The tutorial approach makes it perfect for learning the fundamentals of chemical engineering, system identification, state estimation and process control. It is suitable for graduate students in chemical, mechanical, power, and electrical engineering, especially those in process control, process systems engineering, control systems, or fuel cells. It will also aid researchers who need a reminder of the basics as well as an overview of current techniques in the dynamic modelling and control of SOFC.


Author Notes

Biao Huang University of Alberta, Canada

Yutong Qi Corporate Electronics, Canada

AKM Monjur Murshed Shell Canada, Canada


Table of Contents

Prefacep. xi
Acknowledgmentsp. xiii
List of Figuresp. xv
List of Tablesp. xxi
1 Introductionp. 1
1.1 Overview of Fuel Cell Technologyp. 1
1.1.1 Types of Fuel Cellsp. 2
1.1.2 Planar and Tubular Designsp. 3
1.1.3 Fuel Cell Systemsp. 4
1.1.4 Pros and Cons of Fuel Cellsp. 5
1.2 Modelling, State Estimation and Controlp. 5
1.3 Book Coveragep. 6
1.4 Book Outlinep. 6
Part I Fundamentals
2 First Principle Modelling for Chemical Processesp. 11
2.1 Thermodynamicsp. 11
2.1.1 Forms of Energyp. 11
2.1.2 First Lawp. 12
2.1.3 Second Lawp. 13
2.2 Heat Transferp. 13
2.2.1 Conductionp. 14
2.2.2 Convectionp. 15
2.2.3 Radiationp. 17
2.3 Mass Transferp. 18
2.4 Fluid Mechanicsp. 20
2.4.1 Viscous Flowp. 21
2.4.2 Velocity Distributionp. 21
2.4.3 Bernoulli Equationp. 21
2.5 Equations of Changep. 22
2.5.1 The Equation of Continuityp. 23
2.5.2 The Equation of Motionp. 23
2.5.3 The Equation of Energyp. 24
2.5.4 The Equations of Continuity of Speciesp. 26
2.6 Chemical Reactionp. 26
2.6.1 Reaction Ratep. 26
2.6.2 Reversible Reactionp. 28
2.6.3 Heat of Reactionp. 29
2.7 Notes and Referencesp. 29
3 System Identification Ip. 31
3.1 Discrete-time Systemsp. 31
3.2 Signalsp. 36
3.2.1 Input Signalsp. 36
3.2.2 Spectral Characteristics of Signalsp. 41
3.2.3 Persistent Excitation in Input Signalsp. 44
3.2.4 Input Designp. 49
3.3 Modelsp. 50
3.3.1 Linear Modelsp. 50
3.3.2 Nonlinear Modelsp. 54
3.4 Notes and Referencesp. 56
4 System Identification IIp. 57
4.1 Regression Analysisp. 57
4.1.1 Autoregressive Moving Average with Exogenous Input Modelsp. 57
4.1.2 Linear Regressionp. 59
4.1.3 Analysis of Linear Regressionp. 60
4.1.4 Weighted Least Squares Methodp. 61
4.2 Prediction Error Methodp. 64
4.2.1 Optimal Predictionp. 65
4.2.2 Prediction Error Methodp. 70
4.2.3 Prediction Error Method with Independent Parameterisationp. 74
4.2.4 Asymptotic Variance Property of PEMp. 75
4.2.5 Nonlinear Identificationp. 76
4.3 Model Validationp. 79
4.3.1 Model Structure Selectionp. 79
4.3.2 The Parsimony Principlep. 80
4.3.3 Comparison of Model Structuresp. 81
4.4 Practical Considerationp. 82
4.4.1 Treating Non-zero Meansp. 82
4.4.2 Treating Drifts in Disturbancesp. 83
4.4.3 Robustnessp. 83
4.4.4 Additional Model Validationp. 83
4.5 Closed-loop Identificationp. 84
4.5.1 Direct Closed-loop Identificationp. 85
4.5.2 Indirect Closed-loop Identificationp. 87
4.6 Subspace Identificationp. 92
4.6.1 Notationsp. 92
4.6.2 Subspace Identification via Regression Analysis Approachp. 97
4.6.3 Examplep. 100
4.7 Notes and Referencesp. 102
5 State Estimationp. 103
5.1 Recent Developments in Filtering Techniques for Stochastic Dynamic Systemsp. 103
5.2 Problem Formulationp. 105
5.3 Sequential Bayesian Inference for State Estimationp. 107
5.3.1 Kalman Filter and Extended Kalman Filterp. 110
5.3.2 Unscented Kalman Filterp. 112
5.4 Examplesp. 116
5.5 Notes and Referencesp. 120
6 Model Predictive Controlp. 121
6.1 Model Predictive Control: State-of-the-Artp. 121
6.2 General Principlep. 122
6.2.1 Models for MPCp. 122
6.2.2 Free and Forced Responsep. 125
6.2.3 Objective Functionp. 125
6.2.4 Constraintsp. 126
6.2.5 MPC Lawp. 126
6.3 Dynamic Matrix Controlp. 127
6.3.1 Predictionp. 127
6.3.2 DMC without Penalising Control Movesp. 129
6.3.3 DMC with Penalising Control Movesp. 130
6.3.4 Feedback in DMCp. 130
6.4 Nonlinear MPCp. 134
6.5 General Tuning Guideline of Nonlinear MPCp. 136
6.6 Discretisation of Models: Orthogonal Collocation Methodp. 137
6.6.1 Orthogonal Collocation Method with Prediction Horizon 1p. 137
6.6.2 Orthogonal Collocation Method with Prediction Horizon Np. 140
6.7 Pros and Cons of MPCp. 142
6.8 Optimisationp. 142
6.9 Example: Chaotic Systemp. 144
6.10 Notes and Referencesp. 145
Part II Tubular SOFC
7 Dynamic Modelling of Tubular SOFC: First-Principle Approachp. 149
7.1 SOFC Stack Designp. 149
7.2 Conversion Processp. 150
7.2.1 Electrochemical Reactionsp. 150
7.2.2 Electrical Dynamicsp. 153
7.3 Diffusion Dynamicsp. 155
7.3.1 Transfer Function of Diffusionp. 156
7.3.2 Simplified Transfer Function of Diffusionp. 157
7.3.3 Dynamic Model of Diffusionp. 158
7.3.4 Diffusion Coefficientp. 159
7.4 Fuel Feeding Processp. 160
7.4.1 Reforming/Shift Reactionp. 160
7.4.2 Mass Transportp. 162
7.4.3 Momentum Transferp. 164
7.4.4 Energy Transfer and Heat Exchangep. 165
7.5 Air Feeding Processp. 166
7.5.1 Mass Transport in the Cathode Channelp. 166
7.5.2 Cathode Channel Momentum Transferp. 167
7.5.3 Energy Transfer in the Cathode Channelp. 168
7.5.4 Air in Injection Channelp. 168
7.6 SOFC Temperaturep. 169
7.6.1 Dynamic Energy Exchange Processp. 169
7.6.2 Conductionp. 170
7.6.3 Convectionp. 171
7.6.4 Radiationp. 172
7.6.5 Cell Temperature Modelp. 174
7.6.6 Injection Tube Temperature Modelp. 174
7.7 Final Dynamic Modelp. 175
7.7.1 I/O Variablesp. 175
7.7.2 State Space Modelp. 176
7.7.3 Model Validationp. 180
7.8 Investigation of Dynamic Properties through Simulationsp. 181
7.8.1 Dynamics of Diffusionp. 182
7.8.2 Dynamics of Fuel Feeding Processp. 184
7.8.3 Dynamics of Air Feeding Processp. 186
7.8.4 Dynamics due to External Loadp. 188
7.9 Notes and Referencesp. 190
8 Dynamic Modelling of Tubular SOFC: Simplified First-Principle Approachp. 193
8.1 Preliminaryp. 193
8.1.1 Relation of Process Variablesp. 194
8.1.2 Limits to Power Outputp. 194
8.2 Low-order State Space Modelling of SOFC Stackp. 195
8.2.1 Physical Processesp. 195
8.2.2 Modelling Assumptionsp. 197
8.2.3 I/O Variablesp. 197
8.2.4 Voltagep. 198
8.2.5 Partial Pressuresp. 199
8.2.6 Flow Ratesp. 200
8.2.7 Temperaturesp. 203
8.3 Nonlinear State Space Modelp. 204
8.4 Simulationp. 205
8.4.1 Validationp. 205
8.4.2 Step Response to the Inputsp. 207
8.4.3 Step Responses to the Disturbancesp. 209
8.5 Notes and Referencesp. 211
9 Dynamic Modelling and Control of Tubular SOFC: System Identification Approachp. 213
9.1 Introductionp. 213
9.2 System Identificationp. 213
9.2.1 Selection of Variablesp. 213
9.2.2 Step Response Testp. 214
9.2.3 Non-typical Step Responsep. 217
9.2.4 Input Designp. 218
9.2.5 Linear System Identificationp. 220
9.2.6 Nonlinear System Identificationp. 234
9.3 PID Controlp. 241
9.3.1 Set Point Trackingp. 243
9.3.2 Disturbance Rejectionp. 243
9.3.3 Internal Model Control for Discrete-time Processesp. 243
9.3.4 Application of Discrete-time IMC to Multi-loop Control of SOFCp. 254
9.4 Closed-loop Identificationp. 257
9.5 Notes and Referencesp. 263
Part III Planar SOFC
10 Dynamic Modelling of Planar SOFC: First-Principle Approachp. 267
10.1 Introductionp. 267
10.2 Geometryp. 268
10.3 Stack Voltagep. 268
10.4 Mass Balancep. 270
10.5 Energy Balancep. 271
10.5.1 Lumped Modelp. 272
10.5.2 Detail Modelp. 273
10.6 Simulationp. 277
10.6.1 Steady-state Responsep. 277
10.6.2 Dynamic Responsep. 278
10.7 Notes and Referencesp. 280
11 Dynamic Modelling of Planar SOFC Systemp. 283
11.1 Introductionp. 283
11.2 Fuel Cell Systemp. 283
11.2.1 Fuel and Air Heat Exchangersp. 284
11.2.2 Reformerp. 286
11.2.3 Burnerp. 287
11.3 SOFC along with a Capacitorp. 287
11.4 Simulation Resultp. 289
11.4.1 Fuel Cell System Simulationp. 290
11.4.2 SOFC Stack with Ultra-capacitorp. 292
11.5 Notes and Referencesp. 292
12 Model Predictive Control of Planar SOFC Systemp. 295
12.1 Introductionp. 295
12.2 Control Objectivep. 296
12.3 State Estimation: UKFp. 297
12.4 Steady-state Economic Optimisationp. 298
12.5 Control and Simulationp. 301
12.5.1 Linear MPCp. 301
12.5.2 Nonlinear MPCp. 303
12.5.3 Optimisationp. 305
12.6 Results and Discussionsp. 306
12.7 Notes and Referencesp. 307
Appendix A Properties and Parametersp. 309
A.1 Parametersp. 309
A.2 Gas Propertiesp. 309
Referencesp. 315
Indexp. 321
Go to:Top of Page