Contents:
Part I Idle Speed Control, Adaptive Flow Estimation and Spline Interpolation
1 Idle Speed Control with Estimation of Unmeasurable Disturbances
1.1 Introduction
1.2 Engine Model
1.3 Problem Statement
1.4 Control Design
1.4.1 Observer Design for Disturbance Torque Td(t)
1.4.2 Control Law Design for a Spark Advance
1.4.3 Control Law Design for Throttle
1.5 Stability Analysis of the System
1.6 Simulation Results
1.7 Conclusion
2 Cylinder Flow Estimation
2.1 Introduction
2.2 Air Charge Determination Using Input Observer
2.2.1 Input Estimation Algorithms: General Case
2.2.2 Input Observer Application to Charge Estimation
2.2.3 Experimental Evaluation
2.3 Composite Adaptive Engine Air Charge Estimation
2.3.1 Composite Adaptive Algorithms: General Case
2.3.2 Derivation of Composite Adaptive Air Charge Estimator
2.3.3 Improving Feedforward via Learning
2.3.4 Evaluation
2.4 Air Charge Prediction
2.5 Concluding Remarks
3 Recursive Spline Interpolation Method
3.1 Introduction
3.2 The Estimation of the Derivatives of Signal via Spline Interpolation
3.3 Second Order Example
3.3.1 Spline Interpolation Method
3.3.2 Combination of High Gain Observer and Spline Interpolation Method and Their Comparative Analysis
3.4 Implementation Results: Crankshaft Acceleration Estimation
3.5 Conclusion
Part II Engine Torque and Friction Estimation
4 Engine Torque Estimation
4.1 Introduction
4.2 Problem Statement
4.3 Recursive Trigonometric Interpolation Method
4.3.1 General Description
4.3.2 Computationally Efficient Filtering Algorithms
4.4 Filtering Technique Based on the Kaczmarz Projection Method
4.5 Estimation of the Engine Torque via Crankshaft Speed Fluctuations
4.6 Conclusion
5 Engine Friction Estimation at Start
5.1 Introduction
5.2 Impact on Drivability
5.3 Problem Statement
5.4 Estimation of the Friction Torque at Start
5.5 Adaptation Algorithms for Look-up Tables
5.5.1 Problem Statement
5.5.2 General Adaptation Algorithms for Look-Up Tables
5.5.3 Adaptation Algorithms of the Engine Friction Torque Look-up Table
5.6 Conclusions
6 Data-Driven Algorithms for Engine Friction Estimation
6.1 Introduction
6.2 Estimation of Engine Losses During Fuel Cut Off State
6.2.1 Problem Statement
6.2.2 Filtering Technique
6.3 Adaptation of the Friction Torque Look-up Table
6.3.1 Description of Adaptation Algorithm for Look-up Tables
6.3.2 Adaptation Algorithm for Engine Friction Look-up Table
6.4 Conclusion
Part III Engine Misfire and Cam Profile Switching State Detection
7 Statistical Engine Misfire Detection
7.1 Introduction
7.2 Recursive Trigonometric Interpolation Algorithms
7.2.1 Problem Statement
7.2.2 Recursive Algorithms for Trigonometric Interpolation
7.2.3 Correction of the Recursive Algorithms for Round-Off Errors
7.3 Filtering Technique Based on Trigonometric Interpolation Method
7.4 Statistical Misfire Detection Technique
7.4.1 Misfire Detection at the Combustion Frequency
7.4.2 Misfire Detection at the Half-Order Frequency
7.5 Conclusion
8 The Cam Profile Switching State Detection Method
8.1 Introduction
8.2 The CPS State Detection Algorithm
8.3 Conclusion
Part IV Engine Knock
9 Statistical Engine Knock Detection
9.1 Introduction
9.2 Recursive Trigonometric Interpolation Algorithms
9.2.1 Problem Statement
9.3 Knock Detection Algorithms
9.3.1 Step1: Knock Detection by Using the Cylinder Pressure Signal
9.3.2 Step2: Knock Detection by Using the Knock Sensor Signal
9.4 Conclusion
10 Statistical Engine Knock Control
10.1 Introduction
10.2 Statistical Models of the Knock Sensor and Microphone Signals
10.2.1 Generation of the Amplitude Signals
10.2.2 Threshold Value Determination
10.2.3 Knock Sound Model
10.3 Engine Knock Control with Desired α-risk
10.3.1 Introduction
10.3.2 Control Aims
10.3.3 Trade-off Between the α-risk and Fuel Consumption
10.3.4 Control Algorithm
10.4 Simulation of the Closed Loop Knock Control System
10.5 Adaptation of the Threshold Value
10.6 Conclusion
11 Appendix A
12 Appendix B
13 Appendix C
14 Appendix D
14.1 Hypotheses Tests
14.1.1 One Sample t-Test
14.1.2 Two Sample t-Tests
14.1.3 One Sample χ2-Test (Test on the Variance)
14.1.4 Test for Equal Variances (F-test)
14.1.5 A Statistical Transient Detection
14.1.6 Outlier Detection and Confidence Intervals as Thresholds
14.1.7 Change in Mean Test Based on Neyman-Pearson Lemma
Notes:
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