An investigation of DC-link voltage and temperature variations on EV traction system design
Bibliographic record
Abstract
DC-link voltage and temperature variations are critical issues when designing an electric vehicle (EV) traction system. The paper presents an assessment of electric vehicle power-train options based on the Nissan Leaf vehicle, which is taken as a benchmark system providing same validation for the study. The Nissan Leaf traction machine is evaluated and performance assessed by considering DC-link voltage and temperature variations. An alternative traction machine design is proposed to satisfy the specification. The vehicle power-train is then modified with the inclusion of a DC/DC converter between the battery pack and DC-link to maintain the traction system DC-link voltage near constant. Additionally, inclusion of a supercapacitor system presents a much tighter input voltage specification but still doesn't completely eliminate the issue. Finally, installing a DC/DC converter to mitigate the faulted operation of electric machine drive is reported.
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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".