Modeling and Simulation of a Series Parallel Hybrid Electric Vehicle Using REVS
Bibliographic record
Abstract
This paper discusses a simulation and modeling package developed at University of Manitoba, REVS: renewable energy vehicle simulator. REVS assists in detail studies of hybrid electric vehicles (HEV) and plug in HEV (PHEV) configurations or energy management strategies through visual programming by creating components as hierarchical subsystems. REVS is composed of detailed models of seven major types of components: electric motors, internal combustion engines, batteries, chemical reactions, fuzzy control strategies, renewable energy resources and support components that can be integrated to model and simulate hybrid drive trains in different configurations. REVS was developed in the Matlab/Simulink graphical simulation language as well as IDEAS package and is portable to most computer platforms. A series parallel hybrid electric vehicle (HEV) with a conventional internal combustion engine (ICE) and a power split device have been designed using the simulation package. Fuzzy controllers have been developed to track the desired vehicle velocity and manage the energy flow of the vehicle. Simulation results of the energy management system and dynamic responses of the system are discussed for proposed vehicle.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".