Electric Vehicle Motor Sizing and Optimization with Two-Speed Gearbox
Bibliographic record
Abstract
As electric vehicles (EVs) are seen as the future of transportation, there are two significant challenges to overcome: range and cost. One way to tackle these challenges is to optimize the powertrain. In powertrain optimization, special attention is given to electric motor and gearbox optimization due to its pivotal role in the vehicle performance and efficiency of an EV. A two-speed gearbox configuration for EVs has emerged as a way to enhance dynamic performance and extend the range. However, this configuration has certain drawbacks including increased weight and costs. This has led to the wide adoption of single-speed gearboxes in the electric vehicle industry. Nevertheless, there is potential for motor size optimization by incorporating a two-speed gearbox. The crux of the matter lies in determining whether the advantages of a smaller motor facilitated by a two-speed gearbox outweigh its downsides of increased weight and cost. A systematic method for co-optimizing the electric motor’s sizing specifications and gear ratios of two-speed gearbox is proposed in this paper. This method achieved about a 13% reduction in the required motor power of the sub-compact vehicle, accompanied by a significant motor weight reduction of up to 25% translating to 86 USD in production cost savings. Additionally, energy consumption was reduced by up to 5% for the EPA drive cycle while maintaining the same 0-100 km/h acceleration.
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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".