Fuel Mixture Diagnosis Validation Under Similar Engine Conditions
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Solution Overview
Problem
Existing fuel mixture deviation diagnosis methods in internal combustion engines often result in false alarms due to discrepancies between actuator behavior and model predictions, leading to increased fuel consumption and emissions, and fail to effectively validate fault terminations under similar conditions.
Innovation Solution
A method for validating fuel mixture deviation diagnosis by storing a reference engine operating point during a fault detection and comparing subsequent corrections against this reference point under similar conditions, using a control unit with integrated modules for mixture regulation and diagnostics, to reduce false alerts and improve validation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple different test objects are tested to validate diagnosis robustness, then validation comprehensiveness is improved, but test condition variability increases making it difficult to determine whether deviations are due to richness issues or condition differences
Solution Approach 1:
The patent changes the key parameter from test object variability to test condition variability. By maintaining identical test objects (same engine, same fuel tank, same injection system) and systematically varying only the test conditions (temperature, pressure, humidity), the method isolates the effects of environmental factors from richness diagnosis deviations, enabling robust validation without confounding variables
Solution Approach 2:
The validation process is segmented into distinct phases: first validating the richness diagnosis under controlled identical conditions to establish a baseline, then introducing specific environmental variations one at a time. This segmentation allows for systematic identification of which environmental factors affect diagnosis accuracy without the complexity of simultaneous multiple variable changes
2Measurement precision
If rich and lean test fuels are used to validate richness diagnosis, then diagnosis accuracy is improved, but fuel composition variability introduces additional test variables that complicate result interpretation
Solution Approach 1:
The patent introduces a reference fuel with known, stable composition as an intermediary standard. By comparing test fuel results against this reference fuel under identical test conditions, the method isolates the richness diagnosis accuracy from fuel composition variability. The reference fuel acts as a mediator that enables precise measurement while controlling for compositional differences
Solution Approach 2:
The patent applies homogeneity by using fuels with consistent and well-defined compositions throughout the validation process. Rather than introducing deliberately varied rich and lean fuels that create compositional complexity, the method uses homogeneous fuel specifications that minimize unnecessary variables while still enabling richness diagnosis validation through controlled environmental variations
3Adaptability or versatility
If validation is performed under varying environmental conditions, then real-world applicability is improved, but it becomes difficult to distinguish between richness diagnosis deviations and deviations caused by environmental factors
Solution Approach 1:
The patent performs preliminary validation under controlled identical test conditions before introducing environmental variations. This preliminary action establishes a baseline performance level that is not influenced by environmental factors. By having this baseline established first, subsequent environmental variations can be systematically introduced and their specific effects measured against the baseline, making it possible to distinguish richness diagnosis deviations from environmental factor deviations
Solution Approach 2:
The patent dynamically adjusts the test protocol by starting with static identical conditions and progressively introducing controlled environmental dynamics. The system transitions from a static validation baseline to dynamic environmental testing, allowing real-world applicability to be validated while maintaining the ability to attribute specific deviations to specific environmental factors through the established baseline comparison
Data Source
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AI summary
The subject of the present invention is a method for validating a diagnosis of richness deviation, comprising steps of: using a correctional adaption to modify a richness-regulating function (E23); diagnosing richness deviation, this consisting, in case of detection of a failed adaption end-point, in validating the failed end-point of a preceding adaption via a test carried out assuming similar conditions to said reference point. According to the invention, the method further comprises selecting (E26) said reference point depending on the richness-deviation value of each temporary correction, and the test assuming similar conditions is carried out by comparing each temporary correction with the reference point.