A game theoretic approach to control of a hybrid electric vehicle powertrain.
Bibliographic record
Abstract
The automotive community has long dealt with concerns over the environmental impact of personal transportation methods. Such concerns, coupled with political concerns such as gasoline prices, and increasing dependence on foreign oil has increased the research effort into alternative concepts for vehicle powertrains. One of the most prominent technologies is the hybrid electric vehicle (HEV). Many HEVs employ both an internal combustion engine and an electric motor. The goal is to provide lower emissions, while obtaining superior fuel economy and performance. The objective of this thesis is to explore the use of game-theoretic principles in the implementation of supervisory control of a Hybrid Electric Vehicle (HEV). The work consists of two projects; the first deals with design of the controller itself, and the second, is modelling several test environments to test and redesign our controller in an iterative manner. The simulation environment used is MATLAB/Simulink, a Mathworks product. Vehicle dynamics are modeled using CarSim (a Mechanical Simulation Corporation product). Real-time performance of the supervisory control algorithm is verified using RT-Lab (an Opal-RT product). One of the benchmark tools in REV research, ADVISOR (also MATLAB based), is referenced as a means of double-checking the consistency of simulation results using our customized test environment. Simulation results using the Game-Theoretic Supervisory controller are compared with more conventional, benchmark approaches. Furthermore, a study of controller optimization is presented, as well as simulation results under many drive cycles. Additionally, HEV testing methods are investigated. Complex, comprehensive modeling is compared to simplified, analytical modeling of vehicle powertrains in terms of practical utility and accuracy.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .S65. Source: Masters Abstracts International, Volume: 43-01, page: 0285. Adviser: Xiang Chen. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".