Gasoline Particulate Filter Leak Detection via Engine Speed Segmentation
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Solution Overview
Problem
Existing methods for diagnosing leakage in gasoline particulate filters (GPFs) face challenges at low exhaust flow rates due to overlap in pressure-flow relationships between functional and faulted GPFs, and are confounded by ash loading and resonances in the exhaust system, making it difficult to accurately detect leaks or missing filters.
Innovation Solution
The method involves comparing pressure-flow relationships across a GPF at different engine speed ranges, using existing exhaust gas sensors to generate plots that correlate pressure drop with exhaust flow at high and low engine speeds, thereby improving the separation of curve fits and reducing the confounding effects of ash loading, allowing for more reliable diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pressure sensors are used to measure pressure drop across the particulate filter, then leakage detection is enabled, but measurement precision deteriorates at low exhaust flow rates due to overlap in pressure-flow relationships between functional and faulted filters
Solution Approach 1:
The patent introduces engine speed as an additional dimension to the pressure-flow measurement space. By comparing pressure-flow relationships across different engine speed ranges (low vs. high), the system creates a multi-dimensional diagnostic approach that separates overlapping cases. A functional filter and a leaking filter exhibit different pressure-flow behaviors at different engine speeds, allowing reliable differentiation even when their pressure-flow curves overlap at a single operating point.
Solution Approach 2:
The patent changes the operating parameters by evaluating pressure-flow relationships across multiple engine speed conditions rather than a single condition. By capturing and comparing data from different engine speeds, the system transforms a single-parameter measurement problem into a multi-parameter analysis, enabling distinction between functional and faulted states that would be indistinguishable under fixed conditions.
2Reliability
If pressure drop measurement is used for GPF diagnostics, then leakage detection is possible, but diagnostic accuracy worsens due to confounding effects of ash loading
Solution Approach 1:
The patent adds engine speed as a distinguishing dimension to separate the effects of ash loading from actual leakage. While ash loading affects pressure drop, its impact varies differently with engine speed compared to the impact of filter leakage. By analyzing pressure-flow relationships across multiple engine speed ranges, the system can identify patterns specific to leakage versus patterns caused by ash accumulation, improving diagnostic accuracy.
Solution Approach 2:
The patent segments the diagnostic evaluation into distinct engine speed ranges (low engine speed range versus high engine speed range). This segmentation allows independent analysis of pressure-flow behavior under different operating conditions, enabling the system to identify which segments exhibit anomalies consistent with leakage versus those consistent with normal ash loading effects.
3Reliability
If pressure sensors are used to detect missing GPF, then filter integrity monitoring is enabled, but detection capability deteriorates due to resonances in exhaust pipe cavities and sharp bends
Solution Approach 1:
The patent uses engine speed as an additional dimension to distinguish between pressure variations caused by resonances and those caused by a missing filter. Resonances in exhaust pipes occur at specific frequencies and engine speeds, creating periodic pressure fluctuations. By analyzing pressure-flow relationships across a range of engine speeds and comparing low-speed versus high-speed behavior, the system can identify the characteristic signature of a missing filter that persists across conditions versus transient resonance effects.
Solution Approach 2:
The patent applies a dynamic diagnostic approach by continuously monitoring pressure-flow relationships across varying engine speeds rather than relying on static threshold measurements. This dynamic evaluation allows the system to adapt to changing exhaust system conditions, including resonance frequencies that vary with engine speed, and identify consistent anomalies indicative of a missing filter regardless of transient acoustic effects.
Data Source
AI summary
Methods and systems are provided for diagnosing a gasoline particulate filter in an engine exhaust passage. A pressure-flow relationship of the filter is learned in a low engine speed and high engine speed range. Degradation of the filter is identified based on a substantial separation between the curve fits at the high and low speed range.