A Comparative Study of Control-Oriented Thermal Models for Cylindrical Li-Ion Batteries
Bibliographic record
Abstract
An accurate control-oriented thermal model is of extreme importance for temperature monitoring and thermal management of lithium (Li)-ion batteries in automotive and grid applications. This article, for the first time, presents a comprehensively comparative study of seven representative control-oriented thermal models for cylindrical Li-ion batteries. These models were selected from the state-of-the-art simplified models reported in the existing literature. All these models are introduced in detail. The model assumptions of physical structure, heat generation, and heat conduction are analyzed. Particle swarm optimization (PSO) algorithm is utilized to identify model parameters. Modeling fidelity is evaluated and compared using both simulation campaigns and experimental data sets of cylindrical Li-iron phosphate batteries. The sensitivity of all 1-D models to convective heat transfer coefficient (or convective heat resistance) is quantitatively analyzed, and the computational complexity of the models is compared under a real drive cycle. All the comparisons are thoroughly discussed, and useful insights are provided.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".