Modeling, Simulation and Performance Comparison of Conventional Vehicle Against Three Configurations of Hybrid Vehicles
Bibliographic record
Abstract
In the last two decades, an extensive research work has been conducted in the automotive industry to develop and improve vehicles’ performance. Different vehicular powertrain configurations such as electric vehicles, hybrid ICE/battery vehicles, and recently hybrid FC/battery vehicles have been investigated to find more efficient alternatives for conventional combustion engines vehicles. Because of the many hybrid and electrical vehicle configurations and powertrain technologies, modeling and simulation of such vehicles are very important tools for final design development. Simulation saves time and cost in predicting performance, selecting powertrain components, and tuning control systems. In this paper, three hybrid vehicle models are developed and tested based on forward looking modeling technique and utilizing the Powertrain System Analysis Toolkit (PSAT) software package. Unlike most of the literature, this paper shows more details about sizing of the major components of the proposed powertrains. The main hybrid powertrain components were sized such that acceptable drivability, performance, and fuel economy are achieved. The performance of developed vehicle models is compared with an internal combustion engine (ICE) Nissan Sunny vehicle model using a non-standard driving cycle that was developed to reflect a local driving pattern. The hybrid models under investigation are hybrid fuel cell/battery vehicle, and two hybrid ICE/battery vehicles; one with series configuration, and the other with parallel configuration. The performance of the models is investigated in terms of fuel economy, drivability, emissions, and efficiency. The introduced simulation results demonstrate that the hybrid FC/battery configuration performs the best and is consequently recommended as the powertrain of choice for future vehicles.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".