An Efficient Two-Layer Optimal Torque Split Strategy for PHEV Considering Time-Delay Compensation
Bibliographic record
Abstract
Improper torque division strategy in vehicles leads to unnecessary energy consumption as well as poor driving comfort and safety. Previous research ignores the time-delay phenomenon of vehicle power systems, hindering the further improvement of related performance. In this work, an efficient two-layer torque split strategy with the consideration of the time-delay phenomenon is proposed to improve tracking accuracy, reduce fuel consumption, and avoid battery electrical abuse. In the upper layer controller, an optimal energy management strategy (EMS) is developed to coordinate between the battery state and engine fuel consumption rate by solving a nonlinear optimization problem, generating the reference torque split scheme. The bottom layer controller is based on Time-delay Model Predict Control (TMPC), comprehensively considering the reference torque split scheme, future information, and vehicle power system information. This TMPC strategy reduces both the torque tracking error and velocity tracking error by exploiting the characteristics that hydraulic braking system (HBS), motor and engine have different response times. Event-trigger (ET) mechanism is introduced in the frame of TMPC for the trade-off between control performance and computation efficiency. Stability and feasibility are theoretically guaranteed by the invariant set method. Simulation results verify the effectiveness of the proposed strategy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".