Designing Multi-Mode Power Split Hybrid Electric Vehicles Using the Hierarchical Topological Graph Theory
Bibliographic record
Abstract
Power split hybrid electric vehicles (PS-HEVs) dominate the US and Japanese HEV market because of their superior fuel economy and drivability. In recent years, multi-mode PS-HEVs are offered by Toyota and GM. With multiple modes, it is possible for PS-HEVs to have both good launching/towing performance and fuel economy. Multiple modes are achieved by adding clutches or brakes. However, the corresponding design space can be quite large. To expedite the design process, a hierarchical topological graph theory approach is developed to systematically design a multi-mode PS-HEV with two planetary gear sets (PGSs). The process consists of three steps: 1) model the hybrid powertrain, 2) generate the multi-mode designs with specific modes, and 3) evaluate the performance of design candidates. In the performance screening process, designs are examined using the dynamic programming (DP) algorithm to evaluate their acceleration performance (0-100 km/h); and then a rapid dynamic programming (Rapid-DP) approach is used to compute their fuel economy under a specific driving cycle. Designs that pass the screening will then be retained as final vehicle designs. This design process ensures that the best designs are found and used.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".