Spark Ignition Engine Modeling and Control System Design
A Guide to Model-in-the-Loop Hierarchical Control Methodology
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This book presents a step-by-step guide to engine control system design, providing case studies and a thorough analysis of the modelling process, using model predictive control (MPC). Covering advanced processes alongside the theoretical foundation, it enables engineers to improve performance in both hybrid and non-hybrid vehicles. Control system improvement is one of the major priorities for engineers seeking to enhance an engine. Often possible on a low budget, substantial improvements can be made through applying cutting-edge methods, such as artificial intelligence when modelling engine control system designs and using MPC. The book presents approaches to control system improvement at mid, low and high levels of control. Beginning with model-in-the-loop hierarchical control design of ported fuel injection SI engines, it focuses on optimal control of both transient and steady state, and also discusses hardware-in-the-loop. Chapters on low level control discuss adaptive model predictive control and adaptive variable functioning, as well as designing fuel injection feedback controllers. At mid-level control, engine calibration maps are discussed, with consideration of constraints such as limits on pollutant emissions. Finally, high-level control methodology is detailed in relation to transient torque control of SI engines. This comprehensive yet clear guide to control system improvement is an essential read for any engineer working in automotive engineering and engine control system design.
Table of Contents
Contents Preface Nomenclature: Chapter 1 Abstract 1.1 Engine control system developments 1.2 Design and simulation of control systems 1.3 Modeling 1.4 Hierarchical control 1.4.1 Engine hierarchical torque-based control structure 1.4.2 Mid-level Layer 1.4.3 Low-level Layer 1.4.4 High-level Layer: Torque Control 1.5 Engine Properties for Case Study and Data Acquisition 1.6 Overview, Organization and Structure Guideline 2. Abstract 2.1 Introduction 2.2 Modeling Practice: Neuro-MVM 2.3 Data Acquisition and Design of Experiments 2.4 Modeling Subsystems 2.4.1 Throttle subsystem 2.4.2 Gas Exchange Subsystem 2.4.3 Receiver Subsystem (Intake Manifold Dynamic) 2.4.4 Rotational Dynamics 2.4.5 Combustion subsystem 2.4.6 Exhaust Manifold 2.4.7 Turbocharger 2.4.8 Engine Thermal Model 2.4.9 Catalytic converter Subsystem 2.5 Model Validation and Discussion 2.6 Summary 3. Abstract 3.1 Introduction 3.2 A review on merits of the control-oriented modeling approach 3.3 Model-Based Optimization 3.3.1 Optimization Methodology 3.3.2 A review of Optimization Algorithm Selection 3.3.3 First Step: Preliminary Optimization 3.3.4 Second Step: Main Calibration Determination of the Weighted Objective Function 3.4 Final Results 3.4.1 Calibration Results: Normal Mode 3.4.2 Calibration Results: Full-Load Mode 3.5 Further discussion regarding the final structure 3.6 Summary 4. Abstract 4.1 Introduction 4.2 Air Mass Flow Observer 4.3 Modeling 4.4 Control 4.4.1 Conventional Feedback Control 4.4.2 Alternative Feedback Control 4.5 Further discussion regarding the system unstructured uncertainties 4.6 Results 4.6.1 Parametric Uncertainties 4.6.2 Parametric and Unstructured Uncertainties 4.6.3 Adaptive variable functioning 4.7 Outer Loop Control 4.8 Summary 5 Abstract 5.1 Introduction 5.2 Methodology 5.3 Modeling 5.4 Force-Load Computations 5.5 Control 5.6 Summary 6 Appendix A: 6.1 Introduction 6.2 Table of Patterns 6.3 Performance surfaces of Neural Networks and Initial Weight Values 6.4 Number of Hidden Neurons 6.5 The Number of Iterations in Training Process 6.6 Training Methods 6.7 Parsimony 6.8 Employing single-output Neural Networks 6.9 Minimizing the number of Network Inputs 6.10 Ensemble Averaging (from Committee Methods) 6.11 Improved Partitioning Method 6.12 Dynamic Neural Networks 6.13 Diagonal Recurrent Neural Networks 6.14 Summary Appendix B Index References
Amir-Mohammad Shamekhi was born in 1986, in Kashan, Iran. He received his PhD in Automobile Control from K N Toosi University of Technology in 2021, and was chosen as the superior researcher of the Department of Mechanical Engineering. His works concern Control and Machine Learning, and their applications particularly in vehicles, about which he has published several journal papers.
Amir H. Shamekhi was born in Kashan, Iran, in 1970. He received his B.Sc. in Mechanical Engineering from Tehran University in 1993. Carrying on his studies, he obtained M.Sc. from K. N. Toosi University of Technology in 1997. Receiving his Ph.D. in 2004, Dr. Shamekhi was the first Ph.D. alumni of Mechanical Engineering in K. N. Toosi University of Technology. Currently, Dr. Shamekhi is Associate Professor in the faculty of Mechanical Engineering, K. N. Toosi University of Technology. His fields of study include Internal Combustion Engines, Mechatronics, and Automotive Transmission.