Comparison of Different Power Train Topologies for an Off-Road Electric Vehicle
Bibliographic record
Abstract
This paper deals with a comparative study between different power train topologies regarding batteries aging index factors for off-road electric vehicle (EV) applications. The studied power train topologies are battery pack supplying the motor- drive directly (original), two-stage inverter motor- drive and embedded quasi-Z-source inverter motor- drive. The comparison is conducted in terms of battery aging performance indexes for an electric vehicle considering the same on-board energy capacity. The modeling and the control design for both topologies are presented and discussed. Simulation investigations are performed to verify the different topologies. It is shown that the embedded quasi-Z-source inverter produces better aging performance index in terms of root-mean-square of the current, average values of the battery currents than other topologies. Embedded quasi-Z- source inverter and two-stage inverter reduce RMS, average values with a slight superiority for embedded quasi-Z-source inverter for standard deviation and variation coefficient. The results demonstrate that under the same operating conditions, the transient and the steady state performances of different topologies are comparable. These results prove that embedded quasi-Z-source inverter is a good candidate topology to be used in multi-source electric vehicle system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".