Thermal Conductivity, Heat Sources and Temperature Profiles of Li-Ion Batteries
Bibliographic record
Abstract
In this paper we report the thermal conductivity of several commercial and non-commercial Li-ion secondary battery electrode materials with and without electrolytesolvents. We also measure the Tafel potential, the ohmic resistance, reaction entropyand external temperature of a commercial pouch cell secondary Li-ion battery. Finallywe combined all the experimentally obtained data in a thermal model and discuss thecorresponding internal temperature effects.The thermal conductivity of dry electrode material was found to range from 0.07to 0.41 WK−1m−1 while the electrode material soaked in electrolyte solvent rangedfrom 0.36 to 1.10 WK−1m−1. For all the different materials it was found that addingthe electrolyte solvent increased the thermal conductivity by at least a factor of three. For one of the anode materials it was found that heat treatment at 3000 K increasedthe thermal conductivity by a factor of almost five.Measuring the electric heat sources of an air cooled commercial pouch cell bat-tery at up to ± 2C and the thermal conductivity of the electrode components madeit possible to estimate internal temperature profiles. Combining the heat sources withtabulated convective heat transfer coefficients of air allowed us to calculate the ambi-ent temperature profiles. At 12C charging rate (corresponding to 5 minutes completecharging) the internal temperature differences was estimated to be in the range of 4-20K, depending on the electrode thermal conductivity. The external temperature dropin air flowing at the battery surface was estimated to nearly 40K. Evaluating thermal management of batteries in the light of our measurement led to the conclusion that ex-ternal cooling is more challenging than internal, though neither should be neglected.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".