Sensitivity and Correlation Analysis and Implications in Scalable Product Family Design for Plug-In Hybrid Electric Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Plug-in Hybrid Electric Vehicles (PHEVs) bear great promises for increasing fuel economy and decreasing greenhouse gas emissions by the use of advanced battery technologies and green energy resources. The design of a PHEV highly depends on several factors such as the selected powertrain configuration, control strategy, sizes of drivetrain components, expected range for propulsion purely by electric energy, known as AER, and the assumed driving conditions. Accordingly, design of PHEV powertrains for diverse customer segments requires thorough consideration of the market needs and the specific performance expectations of each segment. From the manufacturing perspective, these parameters provide the opportunity of mass customization because of the high degree of freedom, especially when the component sizes and control parameters are simultaneously assessed. Based on a nonconventional sensitivity and correlation analysis performed on a simulation model for power-split PHEVs in this study, the product family design (PFD) concept and its implications will be investigated, and limitations of PFD for such a complex product along with directions for efficient family design of PHEVs will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it