Future emission regulations could force manufacturers to install in-cylinder pressure sensors on production engines. The availability of such a signal opens a new scenario in terms of combustion control: many settings that previously were optimized off-line, can now be monitored and calibrated in realtime. One of the most effective factors influencing performance and efficiency is the combustion phasing: in gasoline engines Electronic Control Units (ECU) manage the Spark Advance (SA) in order to set the optimal combustion phase. SA optimal values are usually determined by means of calibration procedures carried out on the test bench by changing the ignition angle while monitoring Brake and Indicated Mean Effective Pressure (BMEP, 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 of 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. The approach can be either used in the calibration phase or in on-board applications, if the in-cylinder pressure signal is available: this would allow maintaining the optimization active throughout the entire engine life. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of a single combustion parameter, represented by the 50% Mass Fraction Burned (50%MFB). Due to cycle-to-cycle variation, many 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 (i.e., by SA): this effect must be taken into account in order to avoid over-advanced calibrations. SA is then controlled by means of a PID (Proportional Integer Derivative) controller, fed by an error that is defined based on the previous considerations: a contribution is related to the MFB50-IMEP distribution, and a second contribution is related to the net Cumulative Heat Release (CHRNET)-IMEP distribution. The latter is able to take into account for heat losses. Firstly, 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.
E. Corti, C. Forte (2010). Spark Advance Real-Time Optimization Based on Combustion Analysis. s.l : The American Society of Mechanical Engineers.
Spark Advance Real-Time Optimization Based on Combustion Analysis
CORTI, ENRICO;FORTE, CLAUDIO
2010
Abstract
Future emission regulations could force manufacturers to install in-cylinder pressure sensors on production engines. The availability of such a signal opens a new scenario in terms of combustion control: many settings that previously were optimized off-line, can now be monitored and calibrated in realtime. One of the most effective factors influencing performance and efficiency is the combustion phasing: in gasoline engines Electronic Control Units (ECU) manage the Spark Advance (SA) in order to set the optimal combustion phase. SA optimal values are usually determined by means of calibration procedures carried out on the test bench by changing the ignition angle while monitoring Brake and Indicated Mean Effective Pressure (BMEP, 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 of 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. The approach can be either used in the calibration phase or in on-board applications, if the in-cylinder pressure signal is available: this would allow maintaining the optimization active throughout the entire engine life. The methodology is based on the observation that, for a given running condition, IMEP can be considered a function of a single combustion parameter, represented by the 50% Mass Fraction Burned (50%MFB). Due to cycle-to-cycle variation, many 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 (i.e., by SA): this effect must be taken into account in order to avoid over-advanced calibrations. SA is then controlled by means of a PID (Proportional Integer Derivative) controller, fed by an error that is defined based on the previous considerations: a contribution is related to the MFB50-IMEP distribution, and a second contribution is related to the net Cumulative Heat Release (CHRNET)-IMEP distribution. The latter is able to take into account for heat losses. Firstly, 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
