Optimal Control for Ensured Drivability of Paralell HEVs/PHEVs during Mode Transition
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Plug-in hybrid electric vehicles (PHEVs) with post-transmission parallel configuration attracted considerable attention due to their capacity to operate in either electric vehicle (EV) mode or hybrid electric vehicle (HEV) mode. Meanwhile, the added flexibility and multiple operation modes add additional challenges to vehicle control with acceptable drivability, particularly during the mode transition from the EV and HEV, since proper control is needed for the internal combustion engine (ICE), motor and coupling device to achieve smooth and fast transition, under various vehicle operation constraints such as mode-transition duration, vehicle acceleration fluctuation and friction loss of the dry clutch. In addition, the engagement of dry clutch features torque discontinuity due to slip-stick phenomenon and the dynamic behavior of the ICE further increases the nonlinearity of the powertrain system. This research introduces a method for identifying the theoretically optimal drivability during mode transition and feasible control schemes to effectively coordinate different powertrain components and achieve desirable drivability without violating vehicle operation constraints. Firstly, a post-transmission parallel PHEV is modeled using MATLAB Simulink and US DOE-ANL's AUTONOMIE, and partially verified using a research prototype PHEV-EcoCAR, developed on the GM Chevrolet Malibu platform at University of Victoria. Secondly, the problem of optimal control for drivability is formulated, considering the vehicle operation constraints. Dynamic Programming (DP) is applied to the Simulink powertrain model to identify the optimal control solutions. Simulation results for the studied vehicle are presented and analyzed to illustrate the new approach and its advantages.</div></div>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".