Low Temperature Discharge Cycle Tests for a Lithium Ion Cell
Bibliographic record
Abstract
As all drivers in cold countries know, operating HEV/EV's at cold temperature is rather difficult. Indeed, cold weather increases the internal resistance of the battery system creating a high opposing force while operating the battery: slowdown of Li+ diffusivity and decrease of ionic conductivity of electrolyte. Thereby, it limits the amount of energy extracted and reduces cell energy and power capability. Therefore, Li-Ion sensitivity to temperature remains one of the major obstacles to HEV/VE's market penetration. In fact, until now, investigations of low-temperature behaviors of Li-ion cells barely provide suitable information because they have only been extended to small battery capacities or non-currently used HEV/VE's batteries. Therefore, a complete thermal characterization of an actual HEV/VE's battery is missing. This characterization is described in this paper. Indeed; a 100 Ah lithium LiFePO4Mn HEV battery was tested under various operating conditions. The experimental process includes charging at ambient temperature, and discharging under extreme cold weather. The experimentations was conducted at four different temperatures to study the effect of seasonal changes in temperature.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".