Dynamic optimization method for speed ratio of electric vehicle with two-speed transmission system
Bibliographic record
Abstract
Currently, most of speed ratio optimizing methods for automotive transmission system belong to static optimization , while values of transmission ratio in the whole optimization process of velocity ratio are changing constantly. In order to obtain the best economic performance for the vehicle, corresponding gear- shift schedules should be adopted to different speed ratio. The paper has put forward a dynamic optimization method for speed ratio of the transmission system through analyzing deficiencies of static optimization method for speed ratio of transmission system. The method takes speed ratio of transmission system as design variable, and establishes objective function which considered lightweight design principles of transmission system. With ISIGHT-MATLAB/Simulink co-simulation optimization software platform and shift point judgment rules, it implementes dynamic optimization of speed ratio for transmission system, and has conducted comparative analysis on optimizing results simulated with static optimization method for speed ratio of transmission system. Ultimate optimizing results have indicated the correctness of the dynamic optimization for velocity ratio put forward in the paper as well as the correctness of considering lightweight design principle for the transmission system put forward in the paper.
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 | 0.000 |
| 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".