Development of a Control System for a P4 Parallel-Through-The-Road Hybrid Electric Vehicle
Bibliographic record
Abstract
This thesis outlines the development of a control system for a P4-P0 Parallel-Through-The-Road Hybrid Electric Vehicle. This project was part of the EcoCAR Mobility Challenge, an Advanced Vehicle Technology Competition, sponsored by the U.S. Department of Energy, MathWorks and General Motors. The McMaster Engineering EcoCAR team is participating in its second iteration, re-engineering a 2019 Chevrolet Blazer to suit a car-sharing service located within the Greater Toronto Hamilton Area. The proposed architecture uses a 1.5L Engine together with a Belted Alternator Starter motor connected to the traditional low voltage system. The rear axle is electrified containing an Electric Machine, a power oriented Battery Pack and team-designed gear reduction as well as a clutch. The whole rear powertrain is operating at high voltage and has no connection to the traditional low voltage system. Fuel economy improvements up to 12% can be expected while maintaining stock performance targets. A vehicle simulation model was built to accompany the vehicle design process. This includes a mathematical representation of all powertrain components, the development of energy management algorithms, the design of the Hybrid Supervisory Controller structure, and validating and discussing gathered results. Furthermore, all necessary controllers were chosen and communication within them was established by designing the serial data architecture. The developed energy management algorithm is customized to utilize the strengths of all components and this specific architecture. A simple rule-based algorithm is used to operate the engine as close as possible to its most fuel efficient operation point at any time. The P4 and P0 motor are used to apply supportive torque to the engine or load the engine with a negative torque. In that way the energy can be regenerated inside the powertrain and charge sustaining operation v can be achieved. Fuel economy and performance targets are used to discuss the assumed performance of the vehicle once re-engineered. The set targets range from city and highway fuel economy to IVM – 60 mph acceleration time. Overall the developed control system suits a car-sharing service with its ability to adapt to the occurring driving situations ensuring a close to optimal operation for any known or unknown driving situation. It focuses on modularity, simplicity and functionality to allow a working implementation in future years of the EcoCAR Mobility Challenge.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".