Research Papers: Internal Combustion Engines

Spark Advance Real-Time Optimization Based on Combustion Analysis

[+] Author and Article Information
Enrico Corti, Claudio Forte

Department of Mechanical, Aerospace Nuclear Engineering and Metallurgy (DIEM), University of Bologna, Viale Risorgimento 2, 40136 Bologna, Italy

J. Eng. Gas Turbines Power 133(9), 092804 (Apr 15, 2011) (8 pages) doi:10.1115/1.4002919 History: Received October 11, 2010; Revised October 11, 2010; Published April 15, 2011; Online April 15, 2011

One of the most effective factors influencing performance, efficiency, and pollutant emissions of internal combustion engines is the combustion phasing: In gasoline engines, electronic control units (ECUs) manage the spark advance (SA) in order to set the optimal combustion phase. SA is usually optimized on the test bench by changing the ignition angle while monitoring brake mean effective pressure (BMEP) and indicated mean effective pressure (IMEP) and brake specific fuel consumption (BSFC). The optimization process relates BMEP, IMEP, and BSFC mean values with the control setting (SA). However, the effect of SA on combustion is not deterministic due to the cycle-to-cycle variation: The analysis of mean values requires many engine cycles to be significant in the performance obtained with the given control setting. This paper presents a novel approach to SA optimization, with the objective of improving the performance analysis robustness while reducing the test time. For a given running condition, IMEP can be considered a function of the combustion phase, represented by the 50% mass fraction burned (50% MFB). Due to cycle-to-cycle variation, different MFB50 and IMEP values are obtained during a steady state test carried out with constant SA, but these values are related by means of a unique relationship. The distribution on the plane IMEP-MFB50 forms a parabola; therefore, the optimization could be carried out by choosing SA values maintaining the scatter around the vertex. Unfortunately, the distribution shape is slightly influenced by heat losses: This effect must be taken into account in order to avoid overadvanced calibrations. SA is then controlled by means of a proportional-integer-derivative controller, fed by an error that is defined based on previous considerations: A contribution is related to the MFB50-IMEP distribution, and a second contribution is related to the net cumulative heat release-IMEP distribution. The latter is able to take into account for heat losses. First, the methodology has been tested on in-cylinder pressure data, collected from different SI engines; then, it has been implemented in real-time by means of a programmable combustion analyzer: The system performs a cycle-to-cycle combustion analysis, evaluating the combustion parameters necessary to calculate the target SA, which is then actuated by the ECU. The approach proved to be efficient, reducing the number of engine cycles necessary for the calibration to less than 1000 per operating condition.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

Relationship between IMEP and MFB50 distributions

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Figure 2

Wiebe function parameters versus MFB50

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Figure 3

IMEP values versus MFB50 class

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Figure 4

Race engine part load-high speed behavior

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Figure 5

IMEP-CHRnet and CHRnet-MFB50 trends

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Figure 6

IMEP-CHRnet and IMEP-MFB50 trends

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Figure 7

PID error input for different SA and N values

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Figure 8

IMEP and error trend for different SA values

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Figure 9

Controller performance on the test bench

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Figure 10

Effects of controller action on IMEP and MFB50

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Figure 11

Controller behavior in different AFR conditions




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