Experimental Measurements of Thermal Characteristics of LiFePO<sub>4</sub> Battery
Bibliographic record
Abstract
A major challenge in the development of the next generation electric and hybrid electric vehicle (EV and HEV) technology is the control and management of heat generation and operating temperatures. Vehicle performance, reliability and ultimately consumer market adoption are integrally dependent on successful battery thermal management designs. In addition to this, crucial to thermal modeling is accurate thermo-physical property input. Therefore, to design a thermal management system and for thermal modeling, a designer must study the thermal characteristics of batteries. This work presents a purely experimental thermal characterization of thermo-physical properties of a lithium-ion battery utilizing a promising electrode material, LiFePO4, in a prismatic pouch configuration. In this research, the thermal resistance and corresponding thermal conductivity of prismatic battery materials is evaluated. The experiment was designed to measure the thermal conductivity of both the individual electrode layers, and the overall thermal resistance of the sealed prismatic structure. For this experiment, the first step was battery dissection, the second step was sample preparation and the third step was the measurement of thermal resistance. Thermal resistance measurements were made using specially constructed battery samples. The thru-plane thermal conductivity of LiFePO4 positive electrode and negative electrode materials was found to be 1.79 ± 0.18 W/m°C and 1.17 ± 0.12 W/m°C, respectively.
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 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".