Investigation of Critical Parameters for Selecting Energy-Optimal Cruising Speed Using a Low-Computation Framework
Bibliographic record
Abstract
This article proposes a low-computation framework for on-board calculation of energy-optimal cruising speed, and uses the framework to investigate the critical parameters for energy-optimal cruising speed determination. The main features of the proposed framework are as follows: first, inclusion of all internal and external vehicle losses, particularly the accessory loads, which have a large impact on optimal cruising speed and have not been investigated in other works, second, use of accurate motor/inverter loss look-up-tables rather than approximated polynomial models, and, third, no requirement for connected data or knowledge of the future route, as the optimal cruising speed is calculated precisely for the current state of the vehicle and surroundings. To validate the framework, three electric vehicle models are developed in MATLAB/Simulink and tuned to match real-world data for a Chevrolet Bolt, Chevrolet Spark, and Tesla Model S. Furthermore, an algorithm is developed to determine near-optimal speed transitions when the optimal cruising speed changes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".