A New Valve Lift Control Technique in Electrohydraulic Variable Valve Actuation Systems
Bibliographic record
Abstract
Besides valve timings and opening duration control, several benefits could be achieved in engine operation if the valve actuation system could control the maximum valve displacement during a particular engine condition. Typically, in most electro-hydraulic variable valve actuation systems (VVA), the maximum valve lift along with valve opening/closing events are adjusted simultaneously by precise control of the spool travel in servo-valves. However, at high engine speeds, concurrent control of timings and peak valve lift becomes difficult and sometimes even impossible due to servo-valve response time limitations. In this paper, a new lift control technique is proposed using a control-valve located in the hydraulic supply line. Using this technique, it is possible to precisely control the valve lift even at high engine speeds. With this mechanism, the control-valve flow area could be adjusted using a low-speed actuator such as an electric motor. In contrast to conventional approaches, where maximum lift is repeatedly controlled within each cycle, valve lift in this technique can be adjusted after few engine cycles, thereby reducing control signal fluctuations and also eliminating the need for ultra-high-speed actuators. The proposed hydraulic VVA system is mathematically modeled, and a non-linear sliding mode controller is designed based on the derived equations. Finally, the performance of the proposed lift control technique is verified under different operating conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".