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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".