Modeling and Simulation of Plug-In Hybrid Electric Powertrain System for Different Vehicular Application
Bibliographic record
Abstract
Plug-in hybrid electric vehicle (PHEV) presents the new trend of clean energy vehicle development due to their advantage of all electric driving, allowable external charging, lower emissions, and lower petroleum fuel consumption, comparing to the maturing HEV. Applications of advanced plug-in hybrid powertrain system technology to different type of vehicles lead to the need to identify the key characteristics of these different vehicular applications, and to develop more systematic and effective powertrain design methods. In this work two different types of PHEV powertrain architecture were investigated: a) pre-trans series-parallel multiregime plug-in hybrid electric commercial vehicle (SPMRPHEV), and b) post-trans parallel plug-in hybrid electric formula racing car. Model-based design (MBD) methods were used for powertrain system modelling of the two PHEV applications. With the powertrain system models developed using MATLAB Simulink and dSPACE Automotive Simulation Models (ASM), the powertrain configurations, control strategies and key features were investigated. The simulation results on standard driving cycles for each model using different rule-based control strategies were compared. The differences and key features of two types of vehicle in design and calibration were presented and analyzed.
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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 | 0.000 | 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".