Impacts of Two-Speed Gearbox on Electric Vehicle's Fuel Economy and Performance
Bibliographic record
Abstract
Recent developments of hybrid vehicle technology have promoted another wave of vehicle electrification and introduction of pure electric vehicles (PEVs), such as Nissan Leaf and Ford Transit Connect Electric. The energy efficiency of these PEVs with an electric drive can be potentially further improved by introducing a two-speed or multi-speed gearbox to ensure the electric machine to operate at peak performance. In this work, a powertrain model of the Transit Connect Electric is built to examine the powertrain efficiency improvement potentials using a two-speed gearbox. The HEV and EV powertrain modeling tool, AUTONOMIE from US Argonne National Lab, is used for the powertrain modeling, and partially verified using vehicle testing data from US Environment Protection Agency (EPA). An optimization method, whose kernel is Dynamic Programming (DP), is combined with the model to find the possible minimum energy consumption and corresponding gear ratios. The electric drive designs: a) with or without a two-speed gearbox; and b) using original rule-based gearshift controller or using DP-improved gearbox controller, are compared and analyzed. This study can facilitate a better understanding on the PEVs' powertrain efficiency and provide guidelines to cost-effective PEV electric drive design for given driving cycles using an appropriate electric machine. The need and benefit of two-speed gearbox for mixed city and highway driving are explored. The study forms a foundation for further research in this area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".