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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".