Pressure Sensor Signal Correction Using Combustion Peak Timing
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Solution Overview
Problem
Existing signal processing methods for pressure measuring devices in internal combustion engines are complex, memory-intensive, difficult to calibrate, and result in signal deformation, especially at low engine speeds, leading to underestimation of cylinder pressure.
Innovation Solution
A simplified signal processing method that calculates correction times based on durations between combustion pressure peaks and uses single constants to correct the signal offset, avoiding deformation and reducing memory requirements, by identifying plateau phases for accurate offset correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Kalman filters are used for signal processing, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The signal processing is divided into two distinct segments: a fast Kalman filter for processing pressure peak points and a slow Kalman filter for determining signal drift during plateau phases. This segmentation allows each filter to be optimized for its specific function, improving overall measurement precision while managing complexity through functional separation.
Solution Approach 2:
The system dynamically switches between different processing modes based on the signal characteristics. During pressure peaks, the fast Kalman filter is applied with high-value gains for rapid response. During plateau phases, the slow Kalman filter is used with low-value gains to accurately track drift. This dynamic adaptation resolves the contradiction by adjusting processing complexity according to the operational phase.
2Measurement precision
If Kalman filters with multiple gains are used, then measurement precision is improved, but ease of operation worsens
Solution Approach 1:
The calibration process is segmented into two independent parts: calibration of the fast Kalman filter for pressure peak detection and calibration of the slow Kalman filter for drift determination. Each segment can be calibrated separately using its own characteristic data (pressure peaks for the fast filter, plateau phases for the slow filter), making the overall calibration process more manageable and easier to operate despite using multiple gain parameters.
3Measurement precision
If signal processing is performed at every point, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The processing intensity is dynamically adjusted based on the signal phase. During pressure peaks, the fast Kalman filter processes every point with high gains for precise capture. During plateau phases, the slow Kalman filter processes points with low gains primarily for drift tracking. This dynamic approach maintains measurement precision while reducing overall processing time compared to uniform high-intensity processing.
Solution Approach 2:
During plateau phases, the slow Kalman filter uses low-value gains, applying partial processing action rather than full processing intensity. This is sufficient for tracking drift during these phases without the need for exhaustive processing, thereby reducing total processing time while maintaining adequate precision for the specific phase requirements.
4Measurement precision
If complex signal processing is used, then measurement precision is improved, but quantity of substance (memory usage) increases
Solution Approach 1:
The memory requirements are segmented between two Kalman filters with different memory demands. The fast Kalman filter stores data for pressure peak processing, while the slow Kalman filter stores data for drift determination during plateau phases. This segmentation allows optimization of memory allocation for each function, reducing total memory usage compared to a single comprehensive filter that would need to handle all cases simultaneously.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method provides an accurate and stable processed signal representing cylinder pressure without underestimation, requiring less memory and being easier to calibrate, maintaining signal integrity across varying engine speeds.
Implementation Method 1
Pressure measuring sensors of this type may be piezoelectric sensors which, through variations in the electrical charges of the sensitive piezoelectric element subjected to a pressure, provide, in a relative manner, an indication of the pressure prevailing in the cylinder.
Implementation Method 2
this voltage signal is subjected to noise and drift due, inter alia, to the phenomena of pyroelectricity and/or vibrations to which said pressure measuring sensor is subjected.
Data Source
AI summary
A method for processing a signal of a pressure measuring device inside an internal combustion engine, the device including: a pressure measuring sensor supplying an output voltage signal (SB), and a processing unit connected to the pressure measuring sensor, the method including the following steps:I. calculating a duration (Dt1) between a first time (t0) corresponding to a first combustion pressure peak (P0) and a second time (t1) corresponding to a second combustion pressure peak (P1), consecutive to the first combustion pressure peak (P0),II. calculating a correction start time (tc1) defined according to the previously calculated duration (Dt1),III. measuring a value of the signal (VOFF1) at the correction start time (tc1),IV. correcting the signal, on the basis of the correction start time (tc1) in relation to a reference value (VREF) and obtaining a first processed signal (S) such that:S=SB−VOFF1+VREF.