Development and Implementation of a P4 Parallel Through-the-Road Hybrid Electric Vehicle
Bibliographic record
Abstract
The increasing demand for sustainable transportation solutions has led to the rapid evolution of hybrid and electric vehicles. This thesis, undertaken as part of the EcoCAR Mobility Challenge, presents the development and implementation of a control system for a P4 parallel through-the-road hybrid electric vehicle. A comprehensive vehicle model was developed using MATLAB Simulink. This model was used to model overall vehicle performance and component-specific performance throughout the EcoCAR Mobility Challenge and served as the foundation for the subsequent stages of control system development. Extensive component and vehicle testing formed the crux of this thesis. These bench tests provide invaluable data that aided in the implementation of the component control loops into the MAC Team vehicle. On-road vehicle testing further refined the energy management strategy, drivability, and charge sustaining of the high voltage battery. The vehicle control system has 10 control modules that successfully operated the MAC Team vehicle for over 1500km on public roads. The methodologies and findings can guide future projects aiming to optimize hybrid vehicle performance.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".