Analytical loss model for single- and two-speed electric vehicle gearboxes
Bibliographic record
Abstract
This paper investigates load-dependent and load-independent power losses of clutchless dual-stage single- and two-speed gearboxes for a subcompact electric vehicle (EV). The load-dependent loss includes gear meshing and bearing friction losses, whereas the load-independent loss encompasses gear churning, bearing drag and oil lip seal sources of loss. The methodology proposed estimates these losses based on the geometric parameters and dynamic characteristics of the gears and bearings and lubricant properties. A single-speed production EV gearbox was disassembled to determine the bearing, gear, and lubrication characteristics. The proposed analytical model is applied to estimate power losses for this gearbox as a function of electric machine speed and torque. Additionally, a second gear is added, making the gearbox two-speed, resulting in increased loss but providing the opportunity for increased electric machine and inverter efficiency. The single-speed gearbox has a 7.05:1 final ratio, while the two-speed variant has 7.05:1 and 4.2:1 for the first and second gears. At lower torques, where an EV would operate most, the single-speed gearbox has an efficiency between 94 and 97%, and the two-speed gearbox has between 0.1% and 0.7% lower efficiency when operating in first gear (the same 7.05:1 ratio as for the single-speed gearbox). When at low torque in second gear, the gearbox is about 1 percent more efficient for highway driving conditions, an important contribution towards extending vehicle range. The analysis also shows gear oil immersion depth significantly affects churning loss, and oil temperature has a small effect on overall gearbox loss.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".