MPC-based battery thermal management controller for Plug-in hybrid electric vehicles
Bibliographic record
Abstract
This paper proposes a model predictive control (MPC) scheme for the battery thermal management system (BTMS) of a given plug-in hybrid electric vehicle, namely the Toyota Plug-in Prius. As temperature plays a key-role in battery life and performance, BTMS design has become a critical problem in all of the battery-based technologies. Although BTMS control design in its basic form can be well represented by a reference tracking problem, there exists only little research in the literature addressing this important control problem. Due to the importance of a prediction component in thermal systems, the idea in this paper is to design a concrete BTMS controller using the nonlinear model predictive control (NMPC) theory and examine its applicability to fill this gap. The promising simulation results indicate the prosperity of the proposed BTMS control methodology and thus pave the way for use of the model-based thermal management techniques in a wide range of upcoming battery-based devices.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".