High-Fidelity Modeling of a Power-Split Plug-In Hybrid Electric Powertrain for Control Performance Evaluation
Bibliographic record
Abstract
Plug-in hybrid electric vehicle (PHEV) development seems to be essential for a sustainable transportation system along with electric vehicles. An appropriate power management strategy for a PHEV determines how to blend the engine and the battery power in such a way that leads to significant fuel economy improvement and environmental footprint reduction. To evaluate and validate the controls design, software and hardware-in-the-loop (SIL/HIL) simulations are useful approaches, especially at the early stages of controls design. To conduct SIL/HIL tests, an accurate and relatively fast mathematical model of the real powertrain is required which solely contains the essential dynamics of the plant. In this paper, a physics-based model of a power-split plug-in powertrain is developed and implemented using MapleSim software. This model contains a chemistry-based lithium-ion battery pack, which can distinguish it from other models used in the literature, since the performance of a PHEV greatly depends on its battery. The symbolic computation power of MapleSim makes the model very suitable for real-time SIL/HIL tests.
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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 | 0.001 | 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".