Modeling, optimization and hardware-in-loop simulation of hybrid electric vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This thesis investigates modeling and simulation of hybrid electric vehicles with particular emphasis on transient modeling and real-time simulation. Three different computer models, i.e. a steady state model, a fully-detailed transient model and a reduced-intensity transient model, are developed for a hybrid drive-train in this study. The steady-state model, which has low computational intensity, is used to determine the optimal battery size and chemistry for a plug-in hybrid drive-train. Simulation results using the developed steady state model show the merits of NiMH and Li-ion battery technologies. Based on the obtained results and the reducing cost of Li-ion batteries, this battery chemistry is used throughout this research. A fully-detailed transient model is developed to simulate the vehicle behaviour under different driving conditions. This model includes the dynamics of the power train components such as the engine, the power-electronic converters and vehicle controllers of all levels. The developed transient model produces an accurate representation of the drive-train including the switching behaviour of the power electronic converters. A reduced-intensity transient model (also referred to as a dynamic average model) is developed for real-time hardware-in-loop simulation of the vehicle. By reducing the computational demand of the detailed transient model using averaging techniques, the reduced-intensity model is implemented on a real-time simulator and is interfaced to an external subsystem such as an actual battery. The setup can be used to test existing and emerging battery technologies, which may not have an accurate mathematical model. Extensive tests are performed to verify the accuracy and validity of the results obtained from the developed hardware-in-loop simulation setup.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it