Multi-Objective Optimization Design and Experimental Investigation for a Prismatic Lithium-Ion Battery Integrated with a Multi-Stage Tesla Valve-Based Cold Plate
Bibliographic record
Abstract
High current rate charging causes inevitable severe heat generation, thermal inconsistency, and even thermal runaway of lithium-ion batteries. Concerning this, a liquid cooling plate comprising a multi-stage Tesla valve (MSTV) configuration with high recognition in microfluidic applications was proposed to provide a safer temperature range for a prismatic-type lithium-ion battery. Meanwhile, a surrogate model with the objectives of the cooling performance and energy cost was constructed, and the impact of some influential design parameters was explored through the robustness analysis of the model. On this basis, the multi-objective optimization design of the neighborhood cultivation genetic algorithm (NCGA) was carried out. The obtained results demonstrated that if the MSTV channel was four channels, the valve-to-valve distance was 14.79 mm, and the thickness was 0.94 mm, the cold plate had the most effective cooling performance and a lower pumping power consumption. Finally, the optimization results were verified by a numerical simulation and an experiment, and the performance evaluation was compared with the traditional serpentine channel. The results reported that the optimized design reduced the maximum temperature and standard surface standard deviation of the cold plate by 26% and 35%, respectively. The additional pump power consumption was 17.3%. This research guides the design of battery thermal management systems to improve efficiency and energy costs, especially under the high current rate charging conditions of lithium-ion batteries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".