Compound Lever Based Optimal Configuration Selection of Compound-Split Hybrid Vehicles
Bibliographic record
Abstract
Finding optimal split hybrid configurations through exhaustive search is almost intractable, mainly due to the huge design space, e.g. 252 compound split configurations using two planetary gear sets (PG). Thus, a systematic exhaustive design methodology is required to find optimal configurations. While most of the prior studies proposed methodologies that assess the performance within the physical design space, i.e. based on the powertrain configurations, this paper proposes a compound lever-based comprehensive design methodology. The (virtual) compound lever is an attractive design tool defined by two design variables, i.e. α and β, that omits the redundancy existing within the physical design space, thus, reduces the computational load. The proposed method explores the entire (virtual) compound lever design space to find optimal compound split configurations with outstanding fuel economy and acceleration performance. First, two performance metrics, i.e. fuel economy and acceleration performance, are assessed within the entire compound lever design space, and optimal compound lever designs, i.e. optimal α and β, are selected. These selected compound lever designs, however, lack the physical parameters (e.g. PGs connections and gear ratios) required for further design steps. Therefore, a conversion model and conversion equations are developed in order to convert the selected optimal compound lever designs into feasible powertrain configurations. The developed compound-lever design methodology was applied to a few optimal designs on the Pareto front, and multiple novel compound split configurations with outstanding fuel economy and acceleration performance were found.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".