Browse Publications Technical Papers 2022-36-0052

An efficient real-time monitoring framework of combustion and engine performance 2022-36-0052

Significant efforts have been made in internal combustion engine development to meet the most restrictive emission regulations. Analysis of in-cylinder pressure-time history is one of the most powerful tools to address combustion chamber design and engine calibration. This analysis provides information about load, knock occurrence and intensity, combustion phasing, combustion duration, shape of heat release rate curve and mass fraction burned. Aiming at an efficient real-time monitoring of combustion and engine performance, a processing framework was proposed. The proposed framework seeks to balance parameters accuracy with computational cost. For this, the number of points used on each parameter calculation is reduced by splitting the processing into two paths of data detailing and partitioning. Also, to reduce additional expenses with supplementary hardware, thermodynamics methods were applied to use only the in-cylinder pressure data. The framework ability to accurately estimate engine performance parameters was verified through a results comparison with a commercial system as baseline at the same operating condition, pressure signal data and angular reference. Real-time applicability was investigated across a comparison between the computational cost demanded by the proposed framework and the cycle duration for each given engine speed. To perform these investigations, experimental tests were made in a Ford EcoBoost 1.0 L Turbo engine with Bosch dedicated ECU. During the tests, the engine load and speed were swept, and the in- cylinder pressure was sampled with 0.1 crank angle degree resolution. After the tests, the in-cylinder pressure data were processed using the proposed framework. The results showed a slight difference of 7±1 kPa in absolute pressure values between the proposed framework and the baseline. This difference was negligible impact on combustion and performance results. These findings proved that proposed framework performs an efficient processing with low computational cost and good accuracy. Thus, being able to provide real-time engine test monitoring and handling.


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