Optimal Control for Thermal Management of Li-ion Batteries via Temperature-Responsive Coolant Flow
Bibliographic record
Abstract
A Battery Thermal Management system (BTMS) is responsible for cooling, heating, insulation, and ventilation of the battery pack to ensure safe, reliable, and long-lasting operation of the battery pack under controlled optimal conditions.Cold plates are one of the most popular active thermal control methods used in BTMS.Most cold plate-based cooling strategies use a constant coolant flow rate.A possible method to improve this could be a temperature-responsive coolant flow strategy.This paper focuses on the optimal control of coolant flow for an active hydraulic BTMS with mini-channel cooling plates.An appropriate cost function was formulated to optimise the coolant flow, capturing the trade-off between thermal degradation and pumping power.A software framework was created to obtain the optimal solution for flow rate as a function of time.The results obtained after optimisation showed that an optimal cooling strategy for high heat dissipation generally has three phases -i) increasing flow rate, ii) saturated flow rate, and iii) decreasing flow rate, which gives a lesser cost when compared to a constant flow rate.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".