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Research Papers

A Model-Based Methodology for Real-Time Estimation of Diesel Engine Cylinder Pressure

[+] Author and Article Information
Ahmed Al-Durra

Department of Electrical Engineering, The Petroleum Institute, P.O. Box 2533, Abu Dhabi, United Arab Emirates

Marcello Canova, Stephen Yurkovich

Center for Automotive Research, The Ohio State University, Columbus, OH 43212

J. Dyn. Sys., Meas., Control 133(3), 031005 (Mar 24, 2011) (9 pages) doi:10.1115/1.4003370 History: Received July 29, 2009; Revised November 04, 2010; Published March 24, 2011; Online March 24, 2011

Cylinder pressure is one of the most important parameters characterizing the combustion process in an internal combustion engine. The recent developments in engine 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 conditioning 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 feedback, a real-time algorithm that extracts the pressure signal from the in-cylinder sensor is proposed in this study. The algorithm is based on a crank-angle based engine combustion of that predicts the in-cylinder pressure from the definition of a burn rate function. The model is then adapted to model-based estimation by applying an extended Kalman filter in conjunction with a recursive least-squares estimation scheme. The estimator is tested 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 estimator in reconstructing the cylinder pressure on a crank-angle basis and in rejecting measurement noise and modeling errors. Furthermore, a comparative study with a conventional signal processing method shows the advantage of using the derived estimator, especially in the presence of high signal noise (as frequently happens with low-cost sensors).

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Comparison of model identification results with burn rate curve obtained from the experimental cylinder pressure trace (test No. 8)

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Figure 2

Validation of cylinder pressure model on experimental data: burn rate profile (test No. 2)

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Figure 3

Validation of cylinder pressure model on experimental data: cylinder pressure (test No. 2)

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Figure 4

Summary of pressure estimation results with corrected EKF for a single engine operating condition

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Figure 5

Summary of pressure estimation results with extended RLS-EKF estimator for a single engine operating condition

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Figure 6

Comparison of pressure traces measured in the same cylinder with laboratory-grade sensor and production-type sensor during one engine cycle (test No. 1, cycle 58)

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Figure 7

Noise characteristics for laboratory-grade (left) and production-type (right) pressure sensors

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Figure 8

Error signature in crank-angle domain: error between estimator outputs and cylinder pressure trace obtained from an average of 100 cycles (laboratory-grade sensor)

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Figure 9

Comparative results for on-line cylinder pressure estimation from laboratory-grade sensor data (test No. 3)

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Figure 10

Error signature in crank-angle domain: error between estimator outputs and cylinder pressure trace obtained from an average of 100 cycles (production-type sensor)

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Figure 11

Comparative results for on-line cylinder pressure estimation from production-type sensor data (test No. 3)

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