Implementation of an Adjustable Target Modulation Index for a Variable DC Voltage Control in an Electric Delivery Truck
Bibliographic record
Abstract
This paper introduces an alternative control strategy for the variable voltage control of an electric drivetrain for a Class 4 medium-duty delivery truck and compares the resulting vehicle energy consumption over standardized drive cycles. The baseline system, S1, uses a standard electric drivetrain without a DC-DC and a battery at 460 V. The proposed system, S2, contains a DC-DC converter and a lower voltage battery with three voltage options being investigated: 200 V, 230 V, and 300 V. Previous work has shown that using a bi-directional DC-DC converter, the Fixed Target Modulation Index (FTMI) of the power electronics can be optimized in order to reduce the energy consumption across a drive cycle. In this study an Adjustable Target Modulation Index (ATMI) is proposed, which combines the best aspects of the fixed target modulation index control to attempt to improve efficiency even further. The new control strategy is shown to improve the energy consumption by up to 2.34% over a vehicle with a conventional electric drivetrain, depending on the required drive cycle.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".