Modeling and Parameter Estimation of Automatic Transmission for Heavy-Duty Vehicle Using Dual Clutch Scheme
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">This paper focuses on modeling of the heavy-duty vehicle drivetrain with automatic transmission by using dual clutch scheme. The planetary gear set in the automatic transmission is complicated structure and difficult to understand. The advantage of the dual clutch scheme is that it can be used to represent the complex planetary gear set intuitively, which is a great help to understand the gear shifting process. It is also suitable for being used in the controller due to its low order. Some conditions are required to convert the planetary gear set to the dual clutch model. The heavy-duty vehicle driveline can be converted to the dual clutch model due to its heavy engine and vehicle inertia.</div><div class="htmlview paragraph">This paper also proposes system parameter estimation methods to represent the driveline model. The main parameters are lumped inertia, lumped gear efficiency, output shaft compliance and friction coefficient of clutches. First, a method for estimating lumped inertia and lumped gear efficiency is proposed using WLSE (Weighted Least Square Estimation) when gear is engaged. Second, resonance frequency of the system is obtained from the lock-up oscillation data occurring at the end of the gear shifting. The output shaft compliance is calculated by analyzing resonance frequency of the system. Third, the slip and friction coefficients of the clutch over time are calculated in the inertia phase. Using those data, the relationship between the dynamic friction coefficients and slip of the wet clutch can be obtained.</div><div class="htmlview paragraph">Finally, a simulation is constructed to verify the accuracy of the proposed dual clutch model and the estimated system parameters. Simulation result is compared with experimental data.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".