Modeling Torque Converter Characteristics in Automatic Drivelines: Lock-up Clutch and Engine Braking Simulation
Bibliographic record
Abstract
A torque converter, which is a hydrodynamic clutch in automatic transmissions, transmits power from the engine shaft to the transmission shaft either by dynamically multiplying the engine torque or by rigidly coupling the engine and transmission shafts. The torque converter is a critical element in the automatic driveline, and it affects the vehicle’s fuel consumption and longitudinal dynamics. This paper presents a math-based torque converter model that is able to capture both transient and steady-state characteristics. The torque converter is connected to a mean-value engine model, transmission model, and longitudinal dynamics model in the MapleSim environment, which uses the advantages of an acausal modeling approach. A lock-up clutch is added to the torque converter model to improve the efficiency of the powertrain in higher gear ratios, and its effect on the vehicle longitudinal dynamics (forward velocity and acceleration) is studied. We show that the proposed model can capture the transition from the forward flow to the reverse flow operations during engine braking or coasting. The simulation results also show that the engine braking phenomenon (due to the flow reversal) can effectively assist the braking system to slow down the vehicle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".