Rapid optimal design of a multimode power split hybrid electric vehicle transmission
Bibliographic record
Abstract
This work aims at presenting a design methodology capable of modeling, generating, and testing a large number of multimode power split hybrid electric vehicle transmission designs in a relatively short period of time. Design parameters include the planetary gear ratios, the final drive ratio, the configuration of hookups to link the hybrid powertrain components to the planetary gear sets and the locations of clutch connections between different nodes of the planetary gear sets. The system modeling approach is first presented, including formulations for each component (the vehicle and road load, the engine, the motor/generators and the battery). A rapid and automated modeling procedure is proposed for hybrid electric vehicle transmissions including multiple planetary gear sets and clutch connections. Two algorithms are subsequently presented that enable fast evaluation of fuel economy and acceleration performance of hybrid electric vehicle transmission designs, namely the enhanced Power-Weighted Efficiency Analysis for Rapid Sizing and the Rapid Efficiency-based Launching Performance Analysis algorithms. The developed design methodology is tested by first modeling and evaluating three hybrid electric vehicle designs from the state-of-art. Later, an investigation for optimal designs that can ameliorate the examined benchmarks is performed. Several millions of design options are rapidly generated and tested using the proposed procedure. The methodology is proved effective by quickly coming up with two sub-optimal designs. Fuel economy and acceleration performance are improved by 5.56% and 40.56%, respectively, compared to the corresponding best benchmarks.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".