Particle Filter OBD Virtual Data Bins
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Solution Overview
Problem
Existing on-board diagnostics (OBDs) face challenges in delivering rapid and accurate results, particularly at varying exhaust gas flows, due to noise in differential pressure signals and the compromise of high-quality data when combined with low-quality data, leading to potential untreated emissions and inaccurate fault detection.
Innovation Solution
A method using a numerical scale with successive thresholds and virtual data bins to segregate and sum data of varying quality, allowing for early detection of component failures by averaging values when trigger limits are reached, while maintaining the accuracy of high-quality data for subsequent judgments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a substantial low pass filter is applied to reduce noise in differential pressure signals, then noise is reduced, but the accuracy of high-quality data recorded at high exhaust gas flows is compromised
Solution Approach 1:
The patent segments the data processing into multiple parallel computational paths: a first path applies heavy filtering to low-flow data while a second path processes high-flow data with minimal filtering. This segmentation allows each path to be optimized for its specific data quality, preventing high-quality data from being degraded by filters designed for noisier low-flow conditions.
Solution Approach 2:
The system dynamically selects which data path to use based on real-time exhaust gas flow conditions. When high flow is detected, the system uses the minimal filtering path to preserve data accuracy; when low flow is detected, it switches to the heavy filtering path to reduce noise. This dynamic adaptation resolves the contradiction by matching the filtering intensity to the actual data quality.
2Object-affected harmful factors
If data from a predetermined time period is averaged to reduce noise, then noise is reduced, but the accuracy of high-quality data is compromised by combination with low-quality data
Solution Approach 1:
The patent creates separate computational segments for different data quality levels. High-quality data from high-flow periods is processed in one segment with minimal averaging, while low-quality data from low-flow periods is processed in another segment with heavier averaging. The results are then combined only after separate processing, preventing the degradation of high-quality data by low-quality averages.
Solution Approach 2:
Different processing qualities are applied to different portions of the data based on their source conditions. High-flow data receives minimal processing to preserve its inherent accuracy, while low-flow data receives aggressive filtering appropriate for its noisier characteristics. This local quality approach ensures each data type is treated according to its specific needs rather than applying a uniform processing standard.
3Measurement precision
If severe entry conditions are applied to ensure only high flow/high accuracy data is used, then data accuracy is improved, but the OBD may not operate with sufficient frequency to achieve legislative operational requirements
Solution Approach 1:
The system dynamically adjusts its operational thresholds based on current exhaust flow conditions. During high-flow periods, the system operates with minimal entry condition restrictions to maximize detection frequency and utilize the naturally high-quality data. During low-flow periods, it applies more stringent conditions but processes data more aggressively to maintain operational frequency. This dynamic threshold adjustment resolves the contradiction between accuracy and productivity.
Solution Approach 2:
The patent changes the operational parameters (entry conditions and filtering intensity) based on the measured exhaust flow parameter. When flow exceeds a threshold, the system switches to a mode with lenient entry conditions and minimal filtering to maximize productivity. When flow drops below the threshold, it switches to a mode with stricter conditions but heavier processing. This parameter adaptation allows the system to maintain both accuracy and operational frequency across varying conditions.
Data Source
AI summary
An on-board diagnostic for a particle filter of a vehicle exhaust system records repeating data about flow and pressure around the particle filter. Data is recorded in virtual data bins having successive thresholds or filters within a numerical scale. Each data point is typically recorded in several bins to permit a rapid calculation of averaged data for use in the diagnostic. Sensitivity of less frequently recorded data is preserved, while giving quickly delivery of a result from the diagnostic.