Hierarchical thermal transport across multiple length scales in high-capacity lithium-ion batteries for stationary energy storage systems
Bibliographic record
Abstract
High-capacity (above 100 Ah) lithium-ion battery (LIB) cells are the core building units of grid-scale battery energy storage systems (BESS). For optimal thermal performance and lifespan, the temperature of hundreds of LIB cells in BESS must be maintained within a relatively narrow range ($25-40^{\circ} \mathrm{C}$) while preserving spatial uniformity within and across the cells. BESS exhibit hierarchical thermal transport across multiple length scales spanning up to six orders of magnitude. This work proposes a cost-effective hierarchical methodology for modeling thermal transport across sub-cell, cell, module, and rack domains. This methodology leverages high-fidelity finite elements simulations to hierarchically characterize relevant BESS domains through effective thermophysical properties and heat generation rates obtained from a representative number of sub-domains, without explicitly modeling all individual components within each BESS domain. The practical application of this methodology is illustrated through a case study of a $220-\mathrm{kWh}$ BESS serving as an energy buffer for an electric vehicle fast charging station. This study quantifies how changes at the smallest levels in the hierarchy, such as electrode thickness at the sub-cell level, impact the system-level thermal performance and reliability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".