Drivability Improving Control During Mode Transition Process of Through-the-Road Hybrid Electric Vehicles
Bibliographic record
Abstract
Drivability control in mode transition process of through-the-road hybrid electric vehicles (TTR HEVs) is focused in this article. There is more than one path of energy flow from the power sources to the wheels in TTR HEVs. The torque coupling is achieved through the road. The operation modes and transitions show more diversity than other HEVs. These distinguishing features imply the necessity of remodeling the TTR HEV powertrains for drivability control. In addition, how to handle the uncertainty resulting from clutch actuation, internal combustion engine (ICE) response lag, and modeling error is still a tough issue. To overcome these difficulties, first, operation modes and transition process of the concerned TTR HEV are discussed. The dynamic models of the front and rear powertrain are derived. Second, considering clutch torque change, ICE torque error, and model uncertainty, sliding mode controller (SMC) is designed to synchronize the rotate speed of the clutch driving and driven disks. Third, in order to reduce the reaching time as well as suppress the chattering near and on the sliding surface, a fuzzy sliding mode controller (FSMC) is proposed. Compared simulation results show that the proposed FSMC can improve vehicle drivability effectively.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".