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Technical Briefs

Online Identification and Stochastic Control for Autonomous Internal Combustion Engines

[+] Author and Article Information
Andreas A. Malikopoulos

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109amaliko@umich.edu

Panos Y. Papalambros

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109pyp@umich.edu

Dennis N. Assanis

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109assanis@umich.edu

J. Dyn. Sys., Meas., Control 132(2), 024504 (Feb 09, 2010) (6 pages) doi:10.1115/1.4000819 History: Received January 25, 2009; Revised November 15, 2009; Published February 09, 2010; Online February 09, 2010

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.

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Copyright © 2010 by American Society of Mechanical Engineers
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References

Figures

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Figure 1

Two trajectories, A and B, of engine operating points ending at the same operating point

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Figure 2

BSFC value of the terminal engine operating point as reached from trajectories A and B

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Figure 3

The learning process during the interaction between the engine and the driver

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Figure 4

Effect of spark ignition timing on the engine brake torque at constant engine speed

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Figure 5

Gas-pedal position rate representing a driver’s driving style

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Figure 6

Spark ignition timing over the driving style

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Figure 7

BSFC comparison between the baseline and self-learning calibration

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Figure 8

Three different acceleration profiles

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Figure 9

BSFC comparison between the baseline and self-learning calibration (acceleration profile A)

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Figure 10

BSFC comparison between the baseline and self-learning calibration (acceleration profile B)

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Figure 11

BSFC comparison between the baseline and self-learning calibration (acceleration profile C)

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