Component sizing of a plug-in hybrid electric vehicle powertrain, Part A: coupling bio-inspired techniques to meshless variable-fidelity surrogate models
Bibliographic record
Abstract
In the present investigation, the authors propose a variable fidelity optimisation framework for component sizing of a plug-in hybrid electric vehicle (PHEV) powertrain. The proposed computational framework can be divided into two different stages. At the first stage, finite element grids of different resolutions are used to capture initial information regarding the behaviour of physical system. To generate those grids, maximum power of electric motor (PEM-max) and maximum power of combustion engine (PCE-max) are fed to a specialised physical model. Based on a cumbersome computational procedure, the physical model yields fuel consumption (FC) required for a predefined drive cycle. Having such information available, the authors take the advantages of an efficient design of experiment (DoE) scheme to extract some samples from the generated grids. Thereafter, two surrogate techniques, i.e., respond surface method (RSM) and radial basis function (RBF), are used to approximate the general behaviour of both high fidelity and low fidelity models. At the second stage, the developed surrogate models are used for optimisation. To do so, a recent spotlighted memetic algorithm called scale factor local search differential evolution (SFLSDE) is used. Through a throughout comparative analysis, the authors prove the proposed model is really effective for PHEV optimisation.
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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 | 0.001 |
| 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 | 0.001 | 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".