Battery-in-the-Loop Simulation of a Planetary-Gear-Based Hybrid Electric Vehicle
Bibliographic record
Abstract
This paper deals with the development of a hardware-in-the-loop (HIL) real-time simulation setup for vehicular systems. In modeling a vehicular drivetrain, the battery model poses the greatest challenge. Mathematical battery models suitable for real-time and transient simulation are often inaccurate due to internal electrochemical reactions in the battery and external factors such as loading conditions. To model these phenomena precisely while maintaining low computational intensity as required for real-time simulation is a prohibitively difficult task. In lieu of a mathematical model, this paper presents a simulation setup where actual batteries are interfaced with a real-time simulator. It provides a platform to test the emerging battery technologies for which dependable models may not be readily accessible. Results are presented for an HIL model of a planetary-gear-system hybrid electric vehicle with actual batteries in the loop of the simulation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".