Estimation of Individual Cylinder Fuel Air Ratios from a Switching or Wide Range Oxygen Sensor for Engine Control and On-Board Diagnosis
Bibliographic record
Abstract
The fuel air ratio imbalance between individual cylinders can result in poor fuel economy and severe exhaust emissions. Individual cylinder fuel air ratio control is one of the important techniques used to improve fuel economy and reduce exhaust emission. California Air Resources Board (CARB) also has required automotive manufacturers to equip with on-board diagnosis system for cylinder fuel air ratio imbalance detection starting in 2011. However, one of the most challenging tasks for the individual cylinder fuel air ratio control and cylinder imbalance diagnosis is how to retrieve the cylinder fuel air ratio information effectively at low cost. This paper presents a novel and practical signal processing based fuel air ratio estimation method for individual cylinder fuel air ratio balance control and on-board fuel air ratio imbalance diagnosis. Based on temporal array signal processing techniques, an array of data samples from an oxygen sensor located in a confluence point of runners is fed into each cylinder's linear, non-linear, or neural network estimator to estimate its fuel air ratio. This method works with both wide range oxygen sensors and switching oxygen sensors. This paper presents in more detail the linear estimation method and the vehicle test results due to its advantages of good performance, low computational load, and easily automated calibration for production.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".