Real-Time Performance and Driveability Analysis of a Clutchless Multi-Speed Gearbox for Battery Electric Vehicle Applications
Bibliographic record
Abstract
Due to the electric machine torque bandwidth characteristic and good efficiency throughout its operational points, battery electric vehicles (BEVs) are typically equipped with a single-speed gearbox (SSG). Nevertheless, multi-speed gearboxes have been investigated for BEVs’ powertrain application as multiple gear ratios add the possibility of keeping the EM operating in a better efficiency region, thus reducing vehicle energy consumption and increasing dynamic performance. At the same time, driving simulators have gained momentum in industry and academia. Simulators render a faster, cheaper, and safer research and development process since it is possible to analyze the project at a system level before building prototypes. In addition, driving simulators allow the driver’s perception of gear shifting times, shift hunting, and vehicle jerk to be considered during the development phase. Combining the trends mentioned above in the automotive segment, we modeled single-and two-speed BEV models in MATLAB/Simulink. We performed a performance and driveability analysis in a static driving simulator. The preliminary results of adopting an efficiency-based shifting schedule and testing different gear shifting duration times indicate the importance of considering the vehicle’s dynamic behavior when employing multi-speed gearbox in BEVs.
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 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) | 0.000 | 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) | 0.002 | 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".