This book presents a step-by-step guide to the engine control system design, providing case studies and a thorough analysis of the modeling process using machine learning, and model predictive control (MPC). Covering advanced processes alongside the theoretical foundation, MPC 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 by applying cutting-edge methods, such as artificial intelligence when modeling engine control system designs and using MPC. This book presents approaches to control system improvement at mid, low, and high levels of control. Beginning with the model-in-the-loop hierarchical control design of ported fuel injection SI engines, this book focuses on optimal control of both transient and steady state and also discusses hardware-in-the-loop. The chapter on low-level control discusses adaptive MPC and adaptive variable functioning, as well as designing a fuel injection feed-forward controller. At mid-level control, engine calibration maps are discussed, with consideration of constraints such as limits on pollutant emissions. Finally, the high-level control methodology is discussed in detail 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.
Author(s): Amir-Mohammad Shamekhi, Amir Hossein Shamekhi
Publisher: CRC Press
Year: 2023
Language: English
Pages: 191
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
List of Abbreviations
Preface
Author
Chapter 1: Introduction
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
1.4.2.1. Calibration Maps
1.4.2.2. Air Mass Flow Observer
1.4.3. Low-Level Layer
1.4.3.1. Fuel Injection Feed-Back Control
1.4.3.2. Spark Advance Feed-Back Control
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
Notes
References
Chapter 2: Control-Oriented Modeling
2.1. Introduction
2.2. Modeling Practice: Neuro-MVM
2.2.1. Modeling in terms of optimization perspective
2.2.2. Assumptions
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
2.4.5.1. Torque Generation Subsystem
2.4.5.2. Knock Detection Subsystem
2.4.5.3. NOx Emission Subsystem
2.4.5.4. CO Emission Subsystem
2.4.5.5. HC Emission Subsystem
2.4.5.6. Exhaust Gas Temperature Subsystem
2.4.6. Exhaust Manifold
2.4.7. Turbocharger
2.4.7.1. Compressor
2.4.7.2. Turbine
2.4.8. Engine Thermal Model
2.4.9. Catalytic Converter Subsystem
2.4.9.1. Catalyst NOx Emission Subsystem
2.4.9.2. Catalyst CO Emission Subsystem
2.4.9.3. Catalyst HC Emission Subsystem
2.5. Model Validation and Discussion
2.6. Summary
Notes
References
Chapter 3: Mid-Level Controller Design: Calibration
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
3.3.4.1. 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
Notes
References
Chapter 4: Low-Level Controller Design: Fuel Injection Control
4.1. Introduction
4.2. Air Mass Flow Observer
4.3. Modeling
4.4. Control
4.4.1. Conventional Feed-Back Control
4.4.2. Alternative Feed-Back 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
Notes
References
Chapter 5: High-Level Controller Design: Torque Control
5.1. Introduction
5.2. Methodology
5.3. Modeling
5.4. Force-Load Computations
5.5. Control
5.6. Summary
Notes
References
Appendix A: A Short Review of Neural Networks Design
Appendix B: A Short Review of Some Optimization Algorithms
Index
