DISSERTATION By Ahmed Abad Al-Durra, BSECE, MSECE Graduate Program in Electrical and ...

174 Pages·2010·2.63 MB·English

MODEL-BASED ESTIMATION FOR IN-CYLINDER PRESSURE OF
ADVANCED COMBUSTION ENGINES
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree
Doctor of Philosophy in the Graduate School of The Ohio State University
By
Ahmed Abad Al-Durra, B.S.E.C.E., M.S.E.C.E.
Graduate Program in Electrical and Computer Engineering
The Ohio State University
2010
Dissertation Committee:
Prof. Steve Yurkovich, Adviser
Prof. Giorgio Rizzoni
Prof. Yann Guezennec
⃝c Copyright by
Ahmed Abad Al-Durra
2010
ABSTRACT
Cylinder pressure is one of the most important parameters that characterize the
combustion process in an internal combustion engine. Recent developments in en-
gine control technologies suggest the use of cylinder pressure as a feedback signal for
closed-loop combustion control. However, the sensors measuring in-cylinder pressure
are typically subject to noise and offset issues, requiring signal processing methods
to be applied to obtain a sufficiently accurate pressure trace. The signal condition-
ing implies a considerable computational burden, which ultimately limits the use of
cylinder pressure sensing to laboratory testing, where the signal can be processed
off-line.
In order to enable closed-loop combustion control through cylinder pressure feed-
back, a real-time algorithm that extracts the pressure signal from the in-cylinder
production grade sensor is proposed in this study. The algorithm is based on a
crank-angle based engine combustion that predicts the in-cylinder pressure from the
definition of a burn rate function. The model is then adapted to model-based esti-
mation by applying an extended Kalman filter in conjunction with a recursive least
squares estimation scheme. The estimator is tested at certain operating points on
a high-fidelity Diesel engine simulator, as well as on experimental data obtained at
various operating conditions. The results obtained show the effectiveness of the esti-
mator in reconstructing the cylinder pressure on a crank-angle basis and in rejecting
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measurement noise and modeling errors. Furthermore, a comparative study with a
conventional signal processing method shows the advantage of using the derived es-
timator, especially in the presence of high signal noise (as frequently happens with
low-cost sensors).
As an extension and further application, this methodology is built upon to cover
a wider range of operations as well as transient data. Linear parameter varying
techniques using genetic algorithms are utilized to identify the gains of linear spline
functions of the LPV-corrector estimator. The LPV-corrector performs well with a
relatively small computation burden. The two estimators are examined under both
steadystatedataandtransientdata, wherethecomparisoncriteriaincludeestimation
of combustion metrics.
Finally, a model-based estimation methodology that facilitates real-time recon-
struction of individual in-cylinder pressure utilizing a minimum sensor set is demon-
strated. Based on a derived crankshaft speed model incorporated with the pres-
sure model, a sliding mode observer is implemented, wherein chattering is mitigated
and the estimation design is validated. Adding disturbances to the model parame-
ter degrades the performance of the SMO, which motivates the development of an
adaptive-SMO based on the certainty equivalence principle, utilizing the cylinder
pressure signal from one cylinder. The estimator was derived analytically and a proof
of stability is provided.
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This is dedicated to the UAE
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ACKNOWLEDGMENTS
I wish to thank my adviser, Prof. Steve Yurkovich, for his intellectual support,
encouragement, and enthusiasm which motivated me to do my best, and for his
patience in correcting both stylistic and scientific errors. It is only through his sound
advice and guidance that I was able to make the transition from an undergraduate
to a graduate level mindset and work ethic.
I would like to thank Doctor Marcello Canova for discussing with me various
aspects of this thesis and for explaining all thermodynamical and mechanical aspects
that I needed for this work. Without him all this would not have been possible.
I would also like to thank Prof. Giorgio Rizzoni and Prof. Yann Guezennec who
were also instrumental in the help they provided. Indeed their vast experience helped
us surpass many hurdles seamlessly. I would also like to thank all the people at CAR
that helped me in this project and helped me feel part of the group.
Lastbutnotleast, Iwouldliketothankmyfamily, myfriends, andmyscholarship
sponsors, for providing the best support and encouragement any person could ever
hope for. Finally, I am deeply grateful to my wife and our two little daughters for
their constant love and support.
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VITA
April 17, 1982 ..............................Born - Al Ain, The United Arab Emi-
rates (UAE)
2000 ........................................Distinguished Students Scholarship
Scholarship Coordination Office (UAE-
President Office)
2005 ........................................B.S. Electrical & Computer Engineer-
ing,
The Ohio State University
2006 ........................................Petroleum Institute Study Leaver Pro-
gram
Abu Dhabi National Oil Company
(ADNOC)
2007 ........................................M.S. Electrical & Computer Engineer-
ing,
The Ohio State University
2007-present ................................PhD Student in Electrical & Computer
Engineering,
The Ohio State University
PUBLICATIONS
Al-Durra, A., Yurkovich, S., and Guezennec, Y., “Gain-Scheduled Control for an
AutomotiveTractionPEMFuelCellSystem”. Proceedings of the ASME International
Mechanical Engineering Congress and Exposition, 42660, Nov. 2007.
Al-Durra, A., Canova, M., and Yurkovich, S., “Application of Extended Kalman
Filter to On-Line Diesel Engine Cylinder Pressure Estimation”. Proceedings of the
Dynamic System and Control Conference, 2523, Oct. 2009.
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FIELDS OF STUDY
Major Field: Electrical and Computer Engineering
Studies in:
Linear Control Prof. Steve Yurkovich
Nonlinear Control Prof. Andrea Serrani
Sliding Mode Control Prof. Vadim Utkin
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TABLE OF CONTENTS
Page
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xii
Chapters:
1. Introduction and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 CI and SI Background . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Current Practice in IC Engine Control . . . . . . . . . . . . . . . . 5
1.4 Issues and Motivations . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Thesis Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2. State of the Art in Cylinder Pressure Modeling and Estimation . . . . . 12
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Applications of Cylinder Pressure . . . . . . . . . . . . . . . . . . . 13
2.2.1 Closed Loop Control . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Combustion Modes and Flex Fuel . . . . . . . . . . . . . . . 15
2.2.3 Combustion Diagnostics and Misfire Detection. . . . . . . . 18
2.3 Control-Oriented Cylinder Pressure Models . . . . . . . . . . . . . 18
2.4 Pressure Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . 22
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2.4.1 Using the Cylinder Pressure Signal . . . . . . . . . . . . . . 22
2.4.2 Using Crankshaft Speed . . . . . . . . . . . . . . . . . . . . 25
2.4.3 Other Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3. Mathematical Tools for Estimation . . . . . . . . . . . . . . . . . . . . . 27
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Extended Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . 27
3.3 Linear Parameter Varying Systems . . . . . . . . . . . . . . . . . . 31
3.4 Sliding Mode Observer . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.5 Adaptive Observer . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4. EKF Estimation of Cylinder Pressure . . . . . . . . . . . . . . . . . . . . 39
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.2 Cylinder Pressure Model for Estimator Design . . . . . . . . . . . . 40
4.2.1 Description of the Model Structure . . . . . . . . . . . . . . 40
4.2.2 Model Calibration and Validation . . . . . . . . . . . . . . . 44
4.3 Design of In-Cylinder Pressure Estimator . . . . . . . . . . . . . . 51
4.3.1 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.3.2 Preliminary Validation of the Estimator . . . . . . . . . . . 55
4.3.3 Extension for Offset Compensation . . . . . . . . . . . . . . 59
4.4 Application to On-Line Cylinder Pressure Estimation . . . . . . . . 61
4.4.1 Application to Lab-Grade Sensor Data . . . . . . . . . . . . 68
4.4.2 Application to Production-Grade Sensor Data . . . . . . . . 70
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5. LPV-EKF Estimation for Expanded Operating Regions . . . . . . . . . . 76
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.2 Model Structure and Validation . . . . . . . . . . . . . . . . . . . . 77
5.2.1 Description of the Model . . . . . . . . . . . . . . . . . . . . 77
5.2.2 Model Calibration and Validation . . . . . . . . . . . . . . . 78
5.3 Estimation Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.4 Results from Steady State Data . . . . . . . . . . . . . . . . . . . . 85
5.5 Results from Transient Data . . . . . . . . . . . . . . . . . . . . . . 88
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
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