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Research Papers: Internal Combustion Engines

Real-Time Self-Learning Optimization of Diesel Engine Calibration

[+] Author and Article Information
Andreas A. Malikopoulos

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

Dennis N. Assanis

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

Panos Y. Papalambros

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

J. Eng. Gas Turbines Power 131(2), 022803 (Dec 19, 2008) (7 pages) doi:10.1115/1.3019331 History: Received March 18, 2008; Revised October 05, 2008; Published December 19, 2008

Compression ignition engine technologies have been advanced in the past decade to provide superior fuel economy and high performance. These technologies offer increased opportunities for optimizing engine calibration. Current engine calibration methods rely on deriving static tabular relationships between a set of steady-state operating points and the corresponding values of the controllable variables. While the engine is running, these values are being interpolated for each engine operating point to coordinate optimal performance criteria, e.g., fuel economy, emissions, and acceleration. These methods, however, are not efficient in capturing transient engine operation designated by common driving habits, e.g., stop-and-go driving, rapid acceleration, and braking. An alternative approach was developed recently, which makes the engine an autonomous intelligent system, namely, one capable of learning its optimal calibration for both steady-state and transient operating points in real time. Through this approach, while the engine is running the vehicle, it progressively perceives the driver’s driving style and eventually learns to operate in a manner that optimizes specified performance criteria. The major challenge to this approach is problem dimensionality when more than one controllable variable is considered. In this paper, we address this problem by proposing a decentralized learning control scheme. The scheme is evaluated through simulation of a diesel engine model, which learns the values of injection timing and variable geometry turbocharging vane position that optimize fuel economy and pollutant emissions over a segment of the FTP-75 driving cycle.

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Copyright © 2009 by American Society of Mechanical Engineers
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Figures

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Figure 1

Learning process during the interaction between the engine and the driver

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Figure 2

Segment of the FTP-75 driving cycle

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Figure 4

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

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Figure 5

Gas-pedal position rate representing a driver’s driving style (zoom-in)

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Figure 6

Injection timing

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Figure 7

Injection timing (zoom-in)

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Figure 8

Fuel mass injection duration (zoom-in)

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Figure 9

Fuel mass injected per cylinder (zoom-in)

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Figure 10

VGT vane position

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Figure 11

VGT vane position (zoom-in)

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Figure 12

Fuel consumption for the driving cycle

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Figure 13

Emission temperature in the exhaust manifold

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Figure 14

NOx concentration of emissions (zoom-in)

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