A performance optimisation-based e-CVT design method and analysis of representative HEV/PHEV powertrains
Bibliographic record
Abstract
An electronically controlled continuous variable transmission (eCVT) which consists of planetary gears and electric machines plays a key role in advanced hybrid powertrains of HEV/PHEV/EREVs. Design of these eCVTs for best performance is a challenging task due to the complexity of these eCVTs and hybrid powertrain system derived from them. In this work, a performance modelling and optimisationbased design method is introduced to automatically identify the best performance of an eCVT or other complex hybrid vehicle powertrain system. The complex design optimisation problem for peak powertrain performance is solved using a unique twostage search method to significantly reduce needed computation and assure the accuracy of the optimisation. Four representative eCVT powertrain designs of representative HEV/PHEV/EREVs were used as case studies. Test results are calibrated using verified data to reveal the performance characteristics and potential of each powertrain designs. Application of this method to produce new eCVT and geartrain designs with enhanced performance is demonstrated.
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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 | 0.001 |
| 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 | 0.001 | 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".