Recursive Engine In-Cylinder Pressure Reconstruction Using Sensor-Fused Engine Speed.

Area of Science:

• Mechanical Engineering
• Automotive Engineering
• Signal Processing

Background:

• Engine in-cylinder pressure is critical for internal combustion engine optimization.
• Accurate cylinder pressure measurement is essential for performance tuning and diagnostics.
• Traditional methods may lack precision or rely on intrusive sensors.

Purpose of the Study:

• To propose an alternative recursive Kalman filter-based approach for engine cylinder pressure reconstruction.
• To utilize sensor-fused engine speed for enhanced reconstruction accuracy.
• To validate the proposed method using combustion indicators like pressure peak (Pmax) and peak location (Ploc).

Main Methods:

• Centralized sensor fusion technique to synthesize data from engine vibration and flywheel angular speed sensors.
• Obtaining fused engine speed as a primary input for reconstruction.
• Reconstructing engine cylinder pressure via inverse filtering of the engine structural vibration signal using the fused speed.

Main Results:

• The fused engine speed demonstrates improved smoothness, particularly when vibration signals are weighted higher.
• The proposed method shows a direct correlation between sensor-fused speed accuracy and cylinder pressure reconstruction quality.
• Increased reliance on vibration signal data leads to demonstrably better reconstructed cylinder pressure results.

Conclusions:

• The recursive Kalman filter approach with sensor-fused speed offers a viable alternative for engine cylinder pressure reconstruction.
• Sensor fusion, especially prioritizing vibration data, significantly enhances the accuracy of reconstructed cylinder pressure.
• This method provides a pathway for more precise engine optimization and performance analysis.