Improving the Air-Cooling Performance for Battery Packs via Electrothermal Modeling and Particle Swarm Optimization
Bibliographic record
Abstract
A novel design optimization method is proposed to optimize the air passageway for an air-cooled battery pack with a 3P4S configuration (three strings in parallel and four cells in each string). This method includes the electrothermal model for the air-cooled pack and the optimization algorithm. Unlike other thermal models for battery packs, the model established in this article considers the interaction between the state of charge (SOC), current, heat generation, and temperature at the cell level and the impact of uneven cooling on the current distribution in the parallel branches at the pack level. Experiments are conducted to verify the prediction accuracy of the electrothermal model. The results show that the proposed model can accurately predict the electrical and thermal parameters under different conditions. For example, the root-mean-square error (RMSE) of temperature is less than 0.5 °C under all test conditions. As for the optimization algorithm, the particle swarm optimization (PSO) algorithm is used. In order to increase the optimization searching speed and accuracy of PSO, the inertia factor is added to the velocity formula, and the spatial neighborhood method is used. The design optimization method is used to optimize the air passageway of an air-cooling pack. It is found that the optimized pack not only has a lower maximum cell temperature and a smaller temperature variation among cells than the original pack but also has a smaller difference of branch current and a longer lifespan.
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 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 | 0.000 | 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 | 0.000 |
| 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".