Modeling and Simulation of a Multiple-Regime Plug-In Hybrid Electric Vehicle
Bibliographic record
Abstract
In recent years, the automotive industry has devoted considerable resources to the research and development of hybrid vehicles. Plug-in hybrid electric vehicles (PHEV) present to be the next generation hybrid vehicles that offer the advantages in reducing fossil fuel consumption and lowering emissions without sacrifice vehicle performance, and the ability to utilize renewable energy through charge from the electric grid. In this work, the powertrain model of a series-parallel, multiple-regime plug-in hybrid electric vehicle (SPMR-PHEV) was introduced. As one of the several parallel powertrain modeling, simulation and control system design approaches at University of Victoria, the presented SPMR-PHEV model was developed using rule-based load-leveling energy management strategy (EMS) under the MATLAB/Simulink and SimDriveline environment. In order to validate the model and evaluate the fuel consumption and performance of SPMR-PHEV, a Simulink based Prius model and two different PHEV powertrain models have also been built using Autonomie — a vehicle simulation tool developed by DOE’s Argonne National Laboratory, using the default control logics. Fuel consumption from the three different models were compared using a test drive case consisting of eight times of the US06-City drive cycle. Under the static modeling and simulation method and different control strategies, the SPMR-PHEV model in Simulink/SimDriveline and rule-based load-leveling EMS showed 12.02% fuel economy and powertrain efficiency improvements over the Autonomie model. The new powertrain system model developed using Simulink and SimDrivline could also be used as a generic, modular and flexible vehicle modeling platform to support the integration of powertrain design and control system optimization.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".