Efficient Mode Transition Control for Parallel Hybrid Electric Vehicle With Adaptive Dual-Loop Control Framework
Bibliographic record
Abstract
Mode transition control problem in a hybrid powertrain has always been a central concern, because many complicated transient dynamics are involved in this process, such as engine-start, clutch engagement, and actuator control, etc. Especially for a parallel hybrid electric vehicle (HEV), the drivability problem during mode transition process is significant yet challenging to solve. In this paper, a new efficient mode transition control method with adaptive dual-loop control framework is proposed for the clutch engagement in a parallel HEV. Firstly, the expected clutch engaging speed can be calculated by two approaches, optimization method via particle swarm optimization algorithm, and practical method by compromising the transient vehicle jerk and the clutch slipping power, respectively. Utilizing the integral transformation, the demand clutch position trajectory for the inner loop can be obtained. Considering the uncertainties and the backlash in the clutch actuator system, an adaptive state feedback controller is designed in the inner loop. Simulation and experimental results show that the proposed control method can effectively improve the HEV drivability while taking clutch actuator uncertainties into consideration. Furthermore, compared with the control method commonly used in practice, the time of mode transition process can be shortened and vehicle jerk can be controlled within an acceptable range using the proposed method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".