Description

Book Synopsis

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

    Table of Contents

    Preface xi

    Acknowledgments xiii

    List of Figures xv

    List of Tables xxi

    1 Introduction 1

    1.1 Overview of Fuel Cell Technology 1

    1.1.1 Types of Fuel Cells 2

    1.1.2 Planar and Tubular Designs 3

    1.1.3 Fuel Cell Systems 4

    1.1.4 Pros and Cons of Fuel Cells 5

    1.2 Modelling, State Estimation and Control 5

    1.3 Book Coverage 6

    1.4 Book Outline 6

    Part I Fundamentals

    2 First Principle Modelling for Chemical Processes 11

    2.1 Thermodynamics 11

    2.1.1 Forms of Energy 11

    2.1.2 First Law 12

    2.1.3 Second Law 13

    2.2 Heat Transfer 13

    2.2.1 Conduction 14

    2.2.2 Convection 15

    2.2.3 Radiation 17

    2.3 Mass Transfer 18

    2.4 Fluid Mechanics 20

    2.4.1 Viscous Flow 21

    2.4.2 Velocity Distribution 21

    2.4.3 Bernoulli Equation 21

    2.5 Equations of Change 22

    2.5.1 The Equation of Continuity 23

    2.5.2 The Equation of Motion 23

    2.5.3 The Equation of Energy 24

    2.5.4 The Equations of Continuity of Species 26

    2.6 Chemical Reaction 26

    2.6.1 Reaction Rate 26

    2.6.2 Reversible Reaction 28

    2.6.3 Heat of Reaction 29

    2.7 Notes and References 29

    3 System Identification I 31

    3.1 Discrete-time Systems 31

    3.2 Signals 36

    3.2.1 Input Signals 36

    3.2.2 Spectral Characteristics of Signals 41

    3.2.3 Persistent Excitation in Input Signals 44

    3.2.4 Input Design 49

    3.3 Models 50

    3.3.1 Linear Models 50

    3.3.2 Nonlinear Models 54

    3.4 Notes and References 56

    4 System Identification II 57

    4.1 Regression Analysis 57

    4.1.1 Autoregressive Moving Average with Exogenous Input Models 57

    4.1.2 Linear Regression 59

    4.1.3 Analysis of Linear Regression 60

    4.1.4 Weighted Least Squares Method 61

    4.2 Prediction Error Method 64

    4.2.1 Optimal Prediction 65

    4.2.2 Prediction Error Method 70

    4.2.3 Prediction Error Method with Independent Parameterisation 74

    4.2.4 Asymptotic Variance Property of PEM 75

    4.2.5 Nonlinear Identification 76

    4.3 Model Validation 79

    4.3.1 Model Structure Selection 79

    4.3.2 The Parsimony Principle 80

    4.3.3 Comparison of Model Structures 81

    4.4 Practical Consideration 82

    4.4.1 Treating Non-zero Means 82

    4.4.2 Treating Drifts in Disturbances 83

    4.4.3 Robustness 83

    4.4.4 Additional Model Validation 83

    4.5 Closed-loop Identification 84

    4.5.1 Direct Closed-loop Identification 85

    4.5.2 Indirect Closed-loop Identification 87

    4.6 Subspace Identification 92

    4.6.1 Notations 92

    4.6.2 Subspace Identification via Regression Analysis Approach 97

    4.6.3 Example 100

    4.7 Notes and References 102

    5 State Estimation 103

    5.1 Recent Developments in Filtering Techniques for Stochastic Dynamic Systems 103

    5.2 Problem Formulation 105

    5.3 Sequential Bayesian Inference for State Estimation 107

    5.3.1 Kalman Filter and Extended Kalman Filter 110

    5.3.2 Unscented Kalman Filter 112

    5.4 Examples 116

    5.5 Notes and References 120

    6 Model Predictive Control 121

    6.1 Model Predictive Control: State-of-the-Art 121

    6.2 General Principle 122

    6.2.1 Models for MPC 122

    6.2.2 Free and Forced Response 125

    6.2.3 Objective Function 125

    6.2.4 Constraints 126

    6.2.5 MPC Law 126

    6.3 Dynamic Matrix Control 127

    6.3.1 Prediction 127

    6.3.2 DMC without Penalising Control Moves 129

    6.3.3 DMC with Penalising Control Moves 130

    6.3.4 Feedback in DMC 130

    6.4 Nonlinear MPC 134

    6.5 General Tuning Guideline of Nonlinear MPC 136

    6.6 Discretisation of Models: Orthogonal Collocation Method 137

    6.6.1 Orthogonal Collocation Method with Prediction Horizon 1 137

    6.6.2 Orthogonal Collocation Method with Prediction Horizon N 140

    6.7 Pros and Cons of MPC 142

    6.8 Optimisation 142

    6.9 Example: Chaotic System 144

    6.10 Notes and References 145

    Part II Tubular SOFC

    7 Dynamic Modelling of Tubular SOFC: First-Principle Approach 149

    7.1 SOFC Stack Design 149

    7.2 Conversion Process 150

    7.2.1 Electrochemical Reactions 150

    7.2.2 Electrical Dynamics 153

    7.3 Diffusion Dynamics 155

    7.3.1 Transfer Function of Diffusion 156

    7.3.2 Simplified Transfer Function of Diffusion 157

    7.3.3 Dynamic Model of Diffusion 158

    7.3.4 Diffusion Coefficient 159

    7.4 Fuel Feeding Process 160

    7.4.1 Reforming/Shift Reaction 160

    7.4.2 Mass Transport 162

    7.4.3 Momentum Transfer 164

    7.4.4 Energy Transfer and Heat Exchange 165

    7.5 Air Feeding Process 166

    7.5.1 Mass Transport in the Cathode Channel 166

    7.5.2 Cathode Channel Momentum Transfer 167

    7.5.3 Energy Transfer in the Cathode Channel 168

    7.5.4 Air in Injection Channel 168

    7.6 SOFC Temperature 169

    7.6.1 Dynamic Energy Exchange Process 169

    7.6.2 Conduction 170

    7.6.3 Convection 171

    7.6.4 Radiation 172

    7.6.5 Cell Temperature Model 174

    7.6.6 Injection Tube Temperature Model 174

    7.7 Final Dynamic Model 175

    7.7.1 I/O Variables 175

    7.7.2 State Space Model 176

    7.7.3 Model Validation 180

    7.8 Investigation of Dynamic Properties through Simulations 181

    7.8.1 Dynamics of Diffusion 182

    7.8.2 Dynamics of Fuel Feeding Process 184

    7.8.3 Dynamics of Air Feeding Process 186

    7.8.4 Dynamics due to External Load 188

    7.9 Notes and References 190

    8 Dynamic Modelling of Tubular SOFC: Simplified First-Principle Approach 193

    8.1 Preliminary 193

    8.1.1 Relation of Process Variables 194

    8.1.2 Limits to Power Output 194

    8.2 Low-order State Space Modelling of SOFC Stack 195

    8.2.1 Physical Processes 195

    8.2.2 Modelling Assumptions 197

    8.2.3 I/O Variables 197

    8.2.4 Voltage 198

    8.2.5 Partial Pressures 199

    8.2.6 Flow Rates 200

    8.2.7 Temperatures 203

    8.3 Nonlinear State Space Model 204

    8.4 Simulation 205

    8.4.1 Validation 205

    8.4.2 Step Response to the Inputs 207

    8.4.3 Step Responses to the Disturbances 209

    8.5 Notes and References 211

    9 Dynamic Modelling and Control of Tubular SOFC: System Identification Approach 213

    9.1 Introduction 213

    9.2 System Identification 213

    9.2.1 Selection of Variables 213

    9.2.2 Step Response Test 214

    9.2.3 Non-typical Step Response 217

    9.2.4 Input Design 218

    9.2.5 Linear System Identification 220

    9.2.6 Nonlinear System Identification 234

    9.3 PID Control 241

    9.3.1 Set Point Tracking 243

    9.3.2 Disturbance Rejection 243

    9.3.3 Internal Model Control for Discrete-time Processes 243

    9.3.4 Application of Discrete-time IMC to Multi-loop Control of SOFC 254

    9.4 Closed-loop Identification 257

    9.5 Notes and References 263

    Part III Planar SOFC

    10 Dynamic Modelling of Planar SOFC: First-Principle Approach 267

    10.1 Introduction 267

    10.2 Geometry 268

    10.3 Stack Voltage 268

    10.4 Mass Balance 270

    10.5 Energy Balance 271

    10.5.1 Lumped Model 272

    10.5.2 Detail Model 273

    10.6 Simulation 277

    10.6.1 Steady-state Response 277

    10.6.2 Dynamic Response 278

    10.7 Notes and References 280

    11 Dynamic Modelling of Planar SOFC System 283

    11.1 Introduction 283

    11.2 Fuel Cell System 283

    11.2.1 Fuel and Air Heat Exchangers 284

    11.2.2 Reformer 286

    11.2.3 Burner 287

    11.3 SOFC along with a Capacitor 287

    11.4 Simulation Result 289

    11.4.1 Fuel Cell System Simulation 290

    11.4.2 SOFC Stack with Ultra-capacitor 292

    11.5 Notes and References 292

    12 Model Predictive Control of Planar SOFC System 295

    12.1 Introduction 295

    12.2 Control Objective 296

    12.3 State Estimation: UKF 297

    12.4 Steady-state Economic Optimisation 298

    12.5 Control and Simulation 301

    12.5.1 Linear MPC 301

    12.5.2 Nonlinear MPC 303

    12.5.3 Optimisation 305

    12.6 Results and Discussions 306

    12.7 Notes and References 307

    Appendix A Properties and Parameters 309

    A.1 Parameters 309

    A.2 Gas Properties 309

    References 315

    Index 321

Dynamic Modeling and Predictive Control in Solid

    Product form

    £135.74

    Includes FREE delivery

    Order before 4pm today for delivery by Tue 30 Jun 2026.

    A Hardback by Biao Huang, Yutong Qi, A. K. M. Monjur Murshed

    Out of stock


      View other formats and editions of Dynamic Modeling and Predictive Control in Solid by Biao Huang

      Publisher: John Wiley & Sons Inc
      Publication Date: 18/01/2013
      ISBN13: 9780470973912, 978-0470973912
      ISBN10: 0470973919

      Description

      Book Synopsis

      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

        Table of Contents

        Preface xi

        Acknowledgments xiii

        List of Figures xv

        List of Tables xxi

        1 Introduction 1

        1.1 Overview of Fuel Cell Technology 1

        1.1.1 Types of Fuel Cells 2

        1.1.2 Planar and Tubular Designs 3

        1.1.3 Fuel Cell Systems 4

        1.1.4 Pros and Cons of Fuel Cells 5

        1.2 Modelling, State Estimation and Control 5

        1.3 Book Coverage 6

        1.4 Book Outline 6

        Part I Fundamentals

        2 First Principle Modelling for Chemical Processes 11

        2.1 Thermodynamics 11

        2.1.1 Forms of Energy 11

        2.1.2 First Law 12

        2.1.3 Second Law 13

        2.2 Heat Transfer 13

        2.2.1 Conduction 14

        2.2.2 Convection 15

        2.2.3 Radiation 17

        2.3 Mass Transfer 18

        2.4 Fluid Mechanics 20

        2.4.1 Viscous Flow 21

        2.4.2 Velocity Distribution 21

        2.4.3 Bernoulli Equation 21

        2.5 Equations of Change 22

        2.5.1 The Equation of Continuity 23

        2.5.2 The Equation of Motion 23

        2.5.3 The Equation of Energy 24

        2.5.4 The Equations of Continuity of Species 26

        2.6 Chemical Reaction 26

        2.6.1 Reaction Rate 26

        2.6.2 Reversible Reaction 28

        2.6.3 Heat of Reaction 29

        2.7 Notes and References 29

        3 System Identification I 31

        3.1 Discrete-time Systems 31

        3.2 Signals 36

        3.2.1 Input Signals 36

        3.2.2 Spectral Characteristics of Signals 41

        3.2.3 Persistent Excitation in Input Signals 44

        3.2.4 Input Design 49

        3.3 Models 50

        3.3.1 Linear Models 50

        3.3.2 Nonlinear Models 54

        3.4 Notes and References 56

        4 System Identification II 57

        4.1 Regression Analysis 57

        4.1.1 Autoregressive Moving Average with Exogenous Input Models 57

        4.1.2 Linear Regression 59

        4.1.3 Analysis of Linear Regression 60

        4.1.4 Weighted Least Squares Method 61

        4.2 Prediction Error Method 64

        4.2.1 Optimal Prediction 65

        4.2.2 Prediction Error Method 70

        4.2.3 Prediction Error Method with Independent Parameterisation 74

        4.2.4 Asymptotic Variance Property of PEM 75

        4.2.5 Nonlinear Identification 76

        4.3 Model Validation 79

        4.3.1 Model Structure Selection 79

        4.3.2 The Parsimony Principle 80

        4.3.3 Comparison of Model Structures 81

        4.4 Practical Consideration 82

        4.4.1 Treating Non-zero Means 82

        4.4.2 Treating Drifts in Disturbances 83

        4.4.3 Robustness 83

        4.4.4 Additional Model Validation 83

        4.5 Closed-loop Identification 84

        4.5.1 Direct Closed-loop Identification 85

        4.5.2 Indirect Closed-loop Identification 87

        4.6 Subspace Identification 92

        4.6.1 Notations 92

        4.6.2 Subspace Identification via Regression Analysis Approach 97

        4.6.3 Example 100

        4.7 Notes and References 102

        5 State Estimation 103

        5.1 Recent Developments in Filtering Techniques for Stochastic Dynamic Systems 103

        5.2 Problem Formulation 105

        5.3 Sequential Bayesian Inference for State Estimation 107

        5.3.1 Kalman Filter and Extended Kalman Filter 110

        5.3.2 Unscented Kalman Filter 112

        5.4 Examples 116

        5.5 Notes and References 120

        6 Model Predictive Control 121

        6.1 Model Predictive Control: State-of-the-Art 121

        6.2 General Principle 122

        6.2.1 Models for MPC 122

        6.2.2 Free and Forced Response 125

        6.2.3 Objective Function 125

        6.2.4 Constraints 126

        6.2.5 MPC Law 126

        6.3 Dynamic Matrix Control 127

        6.3.1 Prediction 127

        6.3.2 DMC without Penalising Control Moves 129

        6.3.3 DMC with Penalising Control Moves 130

        6.3.4 Feedback in DMC 130

        6.4 Nonlinear MPC 134

        6.5 General Tuning Guideline of Nonlinear MPC 136

        6.6 Discretisation of Models: Orthogonal Collocation Method 137

        6.6.1 Orthogonal Collocation Method with Prediction Horizon 1 137

        6.6.2 Orthogonal Collocation Method with Prediction Horizon N 140

        6.7 Pros and Cons of MPC 142

        6.8 Optimisation 142

        6.9 Example: Chaotic System 144

        6.10 Notes and References 145

        Part II Tubular SOFC

        7 Dynamic Modelling of Tubular SOFC: First-Principle Approach 149

        7.1 SOFC Stack Design 149

        7.2 Conversion Process 150

        7.2.1 Electrochemical Reactions 150

        7.2.2 Electrical Dynamics 153

        7.3 Diffusion Dynamics 155

        7.3.1 Transfer Function of Diffusion 156

        7.3.2 Simplified Transfer Function of Diffusion 157

        7.3.3 Dynamic Model of Diffusion 158

        7.3.4 Diffusion Coefficient 159

        7.4 Fuel Feeding Process 160

        7.4.1 Reforming/Shift Reaction 160

        7.4.2 Mass Transport 162

        7.4.3 Momentum Transfer 164

        7.4.4 Energy Transfer and Heat Exchange 165

        7.5 Air Feeding Process 166

        7.5.1 Mass Transport in the Cathode Channel 166

        7.5.2 Cathode Channel Momentum Transfer 167

        7.5.3 Energy Transfer in the Cathode Channel 168

        7.5.4 Air in Injection Channel 168

        7.6 SOFC Temperature 169

        7.6.1 Dynamic Energy Exchange Process 169

        7.6.2 Conduction 170

        7.6.3 Convection 171

        7.6.4 Radiation 172

        7.6.5 Cell Temperature Model 174

        7.6.6 Injection Tube Temperature Model 174

        7.7 Final Dynamic Model 175

        7.7.1 I/O Variables 175

        7.7.2 State Space Model 176

        7.7.3 Model Validation 180

        7.8 Investigation of Dynamic Properties through Simulations 181

        7.8.1 Dynamics of Diffusion 182

        7.8.2 Dynamics of Fuel Feeding Process 184

        7.8.3 Dynamics of Air Feeding Process 186

        7.8.4 Dynamics due to External Load 188

        7.9 Notes and References 190

        8 Dynamic Modelling of Tubular SOFC: Simplified First-Principle Approach 193

        8.1 Preliminary 193

        8.1.1 Relation of Process Variables 194

        8.1.2 Limits to Power Output 194

        8.2 Low-order State Space Modelling of SOFC Stack 195

        8.2.1 Physical Processes 195

        8.2.2 Modelling Assumptions 197

        8.2.3 I/O Variables 197

        8.2.4 Voltage 198

        8.2.5 Partial Pressures 199

        8.2.6 Flow Rates 200

        8.2.7 Temperatures 203

        8.3 Nonlinear State Space Model 204

        8.4 Simulation 205

        8.4.1 Validation 205

        8.4.2 Step Response to the Inputs 207

        8.4.3 Step Responses to the Disturbances 209

        8.5 Notes and References 211

        9 Dynamic Modelling and Control of Tubular SOFC: System Identification Approach 213

        9.1 Introduction 213

        9.2 System Identification 213

        9.2.1 Selection of Variables 213

        9.2.2 Step Response Test 214

        9.2.3 Non-typical Step Response 217

        9.2.4 Input Design 218

        9.2.5 Linear System Identification 220

        9.2.6 Nonlinear System Identification 234

        9.3 PID Control 241

        9.3.1 Set Point Tracking 243

        9.3.2 Disturbance Rejection 243

        9.3.3 Internal Model Control for Discrete-time Processes 243

        9.3.4 Application of Discrete-time IMC to Multi-loop Control of SOFC 254

        9.4 Closed-loop Identification 257

        9.5 Notes and References 263

        Part III Planar SOFC

        10 Dynamic Modelling of Planar SOFC: First-Principle Approach 267

        10.1 Introduction 267

        10.2 Geometry 268

        10.3 Stack Voltage 268

        10.4 Mass Balance 270

        10.5 Energy Balance 271

        10.5.1 Lumped Model 272

        10.5.2 Detail Model 273

        10.6 Simulation 277

        10.6.1 Steady-state Response 277

        10.6.2 Dynamic Response 278

        10.7 Notes and References 280

        11 Dynamic Modelling of Planar SOFC System 283

        11.1 Introduction 283

        11.2 Fuel Cell System 283

        11.2.1 Fuel and Air Heat Exchangers 284

        11.2.2 Reformer 286

        11.2.3 Burner 287

        11.3 SOFC along with a Capacitor 287

        11.4 Simulation Result 289

        11.4.1 Fuel Cell System Simulation 290

        11.4.2 SOFC Stack with Ultra-capacitor 292

        11.5 Notes and References 292

        12 Model Predictive Control of Planar SOFC System 295

        12.1 Introduction 295

        12.2 Control Objective 296

        12.3 State Estimation: UKF 297

        12.4 Steady-state Economic Optimisation 298

        12.5 Control and Simulation 301

        12.5.1 Linear MPC 301

        12.5.2 Nonlinear MPC 303

        12.5.3 Optimisation 305

        12.6 Results and Discussions 306

        12.7 Notes and References 307

        Appendix A Properties and Parameters 309

        A.1 Parameters 309

        A.2 Gas Properties 309

        References 315

        Index 321

      Recently viewed products

      © 2026 Book Curl

        • American Express
        • Apple Pay
        • Diners Club
        • Discover
        • Google Pay
        • Maestro
        • Mastercard
        • PayPal
        • Shop Pay
        • Union Pay
        • Visa

        Login

        Forgot your password?

        Don't have an account yet?
        Create account