Advanced internal combustion engine technologies have afforded an increase in the number of controllable variables and the ability to optimize engine operation. Values for these variables are determined during engine calibration by means of a tabular static correlation between the controllable variables and the corresponding steady-state engine operating points to achieve desirable engine performance, for example, in fuel economy, pollutant emissions, and engine acceleration. In engine use, table values are interpolated to match actual operating points. State-of-the-art calibration methods cannot guarantee continuously the optimal engine operation for the entire operating domain, especially in transient cases encountered in the driving styles of different drivers. This article presents brief theory and algorithmic implementation that make the engine an autonomous intelligent system capable of learning the required values of controllable variables in real time while operating a vehicle. The engine controller progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance criteria. A gasoline engine model, which learns to optimize fuel economy with respect to spark ignition timing, demonstrates the approach.

1.
Malikopoulos
,
A. A.
,
Papalambros
,
P. Y.
, and
Assanis
,
D. N.
, 2007, “
A Learning Algorithm for Optimal Internal Combustion Engine Calibration in Real Time
,”
Proceedings of the ASME 2007 International Design Engineering Technical Conferences Computers and Information in Engineering Conference
,
Las Vegas, NV
, Sept. 4–7.
2.
Rask
,
E.
, and
Sellnau
,
M.
, 2004, “
Simulation-Based Engine Calibration: Tools, Techniques, and Applications
,”
SAE Trans.
0096-736X,
113
, pp.
821
832
.
3.
Guerrier
,
M.
, and
Cawsey
,
P.
, 2004, “
The Development of Model-Based Methodologies for Gasoline IC Engine Calibration
,”
SAE Trans.
0096-736X,
113
, pp.
981
1002
.
4.
Stuhler
,
H.
,
Kruse
,
T.
,
Stuber
,
A.
,
Gschweitl
,
K.
,
Piock
,
W.
,
Pfluegl
,
H.
, and
Lick
,
P.
, 2002, “
Automated Model-Based GDI Engine Calibration Adaptive Online DoE Approach
,”
SAE 2002 World Congress
,
Detroit, MI
, Mar. 3–7, SAE Paper No. 2002-01-0708.
5.
Burk
,
R.
,
Jacquelin
,
F.
, and
Wakeman
,
R.
, 2003, “
A Contribution to Predictive Engine Calibration Based on Vehicle Drive Cycle Performance
,”
SAE 2003 World Congress
,
Detroit, MI
, Mar. 3–6, SAE Paper No. 2003-01-0225.
6.
Jacquelin
,
F.
,
Burk
,
R.
, and
Wakeman
,
R. J.
, 2003, “
Cam Phaser Actuation Rate Performance Impact on Fuel Consumption and NOx Emissions Over the FTP-75 Drive Cycle
,”
SAE 2003 World Congress
,
Detroit, MI
, SAE Paper No. 2003-01-0023.
7.
Atkinson
,
C.
, and
Mott
,
G.
, 2005, “
Dynamic Model-Based Calibration Optimization: An Introduction and Application to Diesel Engines
,”
SAE World Congress
,
Detroit, MI
, Apr. 11–14, SAE Paper No. 2005-01-0026.
8.
Ayeb
,
M.
,
Theuerkauf
,
H. J.
, and
Winsel
,
T.
, 2005, “
SI Engine Emissions Model Based on Dynamic Neural Networks and D-Optimality
,”
SAE World Congress
,
Detroit, MI
, Apr. 11–14, SAE Paper No. SAE 2005-01-0019.
9.
Brahma
,
I.
,
He
,
Y.
, and
Rutland
,
C. J.
, 2003, “
Improvement of Neural Network Accuracy for Engine Simulations
,”
Powertrain and Fluid Systems Conference and Exhibition
,
Pittsburgh, PA
, Sept. 27–30, SAE Paper No. 2003-01-3227.
10.
Lowe
,
D.
, and
Zapart
,
K.
, 1997, “
Validation of Neural Networks in Automotive Engine Calibration
,”
Proceedings of the Fifth International Conference on Artificial Neural Networks
,
Cambridge, UK
, pp.
221
226
.
11.
Meyer
,
S.
, and
Greff
,
A.
, 2002, “
New Calibration Methods and Control Systems With Artificial Neural Networks
,”
SAE 2002 World Congress
,
Detroit, MI
, March 4–7, SAE Paper No. 2002-01-1147.
12.
Wu
,
B.
,
Prucka
,
R. G.
,
Filipi
,
Z. S.
,
Kramer
,
D. M.
, and
Ohl
,
G. L.
, 2006, “
Cam-Phasing Optimization Using Artificial Neural Networks as Surrogate Models—Fuel Consumption and NOx Emissions
,”
SAE 2006 World Congress
,
Detroit, MI
, Apr. 3–6, SAE Paper No. 2006-01-1512.
13.
Wu
,
B.
,
Prucka
,
R. G.
,
Filipi
,
Z. S.
,
Kramer
,
D. M.
, and
Ohl
,
G. L.
, 2005, “
Cam-Phasing Optimization Using Artificial Neural Networks as Surrogate Models~Maximizing Torque Output
,”
SAE Trans.
0096-736X,
114
, pp.
1586
1599
.
14.
Rakopoulos
,
C. D.
,
Giakoumis
,
E. G.
,
Hountalas
,
D. T.
, and
Rakopoulos
,
D. C.
, 2004, “
The Effect of Various Dynamic, Thermodynamic and Design Parameters on the Performance of a Turbocharged Diesel Engine Operating under Transient Load Conditions
,”
SAE 2004 World Congress and Exhibition
,
Detroit, MI
, Apr. 8–11, SAE Paper No. 2004-0-0926.
15.
Wijetunge
,
R. S.
,
Brace
,
C. J.
,
Hawley
,
J. G.
,
Vaughan
,
N. D.
,
Horroocks
,
R. W.
, and
Bird
,
G. L.
, 1999, “
Dynamic Behavior of a High-Speed, Direct-Injection Diesel Engine
,”
SAE Trans.
0096-736X,
108
, pp.
1120
1129
.
16.
Clark
,
N. N.
,
Gautam
,
M.
,
Rapp
,
B. L.
,
Lyons
,
D. W.
,
Graboski
,
M. S.
,
McCormick
,
R. L.
,
Alleman
,
T. L.
, and
National
,
P. N.
, 1999, “
Diesel and CNG Transit Bus Emissions Characterization by Two Chassis Dynamometer Laboratories: Results and Issues
,”
SAE Trans.
0096-736X,
108
, pp.
801
812
.
17.
Samulski
,
M. J.
, and
Jackson
,
C. C.
, 1998, “
Effects of Steady-State and Transient Operation on Exhaust Emissions From Nonroad and Highway Diesel Engines
,”
SAE Trans.
0096-736X,
107
, pp.
2068
2080
.
18.
Green
,
R. M.
, 2000, “
Measuring the Cylinder-to-Cylinder EGR Distribution in the Intake of a Diesel Engine During Transient Operation
,”
SAE Trans.
0096-736X,
109
, pp.
2036
2045
.
19.
Hagena
,
J. R.
,
Filipi
,
Z. S.
, and
Assanis
,
D. N.
, 2006, “
Transient Diesel Emissions: Analysis of Engine Operation During a Tip-In
,”
SAE 2006 World Congress
,
Detroit, MI
, Apr. 3–6, SAE Paper No. 2006-01-1151.
20.
Sennott
,
L. I.
, 1998,
Stochastic Dynamic Programming and the Control of Queuing Systems
,
1st ed.
,
Wiley-Interscience
,
New York
.
21.
Bellman
,
R.
, 1957,
Dynamic Programming
,
Princeton University Press
,
Princeton, NJ
.
22.
Malikopoulos
,
A. A.
,
Papalambros
,
P. Y.
, and
Assanis
,
D. N.
, 2009, “
A Real-Time Computational Learning Model for Sequential Decision-Making Problems Under Uncertainty
,”
ASME J. Dyn. Syst., Meas., Control
0022-0434,
131
(
4
), p.
041010
.
23.
Malikopoulos
,
A. A.
, 2009, “
Convergence Properties of a Computational Learning Model for Unknown Markov Chains
,”
ASME J. Dyn. Syst., Meas., Control
0022-0434,
131
(
4
), p.
041011
.
25.
Malikopoulos
,
A. A.
,
Assanis
,
D. N.
, and
Papalambros
,
P. Y.
, 2009, “
Real-Time Self-Learning Optimization of Diesel Engine Calibration
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
131
(
2
), p.
022803
.
26.
Malikopoulos
,
A. A.
, 2008, “
Real-Time, Self-Learning Identification and Stochastic Optimal Control of Advanced Powertrain Systems
,” Ph.D. thesis, Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI.
27.
Malikopoulos
,
A. A.
,
Assanis
,
D. N.
, and
Papalambros
,
P. Y.
, 2008, “
Optimal Engine Calibration for Individual Driving Styles
,”
Proceedings of the SAE 2008 World Congress and Exhibition
,
Detroit, MI
, Apr. 14–17, SAE Paper No. 2008-01-1367.
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