Multi-length-scale thermal modeling of lithium-ion batteries from sub-cell to pack
Bibliographic record
Abstract
Lithium-ion batteries (LIBs) have become the preferred power source for electric vehicles (EVs) due to their superior characteristics compared to other storage technologies. Battery thermal management systems are paramount for optimal performance and reliability, to maintain EV battery packs operating within a tight temperature range (25–40 °C) while keeping spatial temperature uniformity. EV battery packs are advanced engineering systems that exhibit hierarchical thermal transport across multiple length scales and physical domains, ranging from electrodes to sub-cell, cell, module, pack, and vehicle domains. This paper presents a novel cost-effective multi-length-scale methodology designed for modeling thermal transport in EV battery packs hierarchically, following successive incremental sub-domain thermal analyses and transferring effective anisotropic thermophysical properties and distributed heat generation rates across sub-cell, cell, module, and pack domains. Through a detailed industry-relevant case study of a pouch-cell-based EV battery pack, this work demonstrates the implementation of this hierarchical methodology and its ability to evaluate how design modifications at different scales influence the system’s thermal performance. This hierarchical multi-length-scale methodology enables the quantification of how changes in the design of a component at smaller scales impact the overall performance and reliability of the entire system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.001 |
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".