Prevention of Accelerated Battery Capacity Degradation using Voltage-sag Analysis under Sub-zero Fast Discharging Conditions
Bibliographic record
Abstract
The health and performance of lithium-ion batteries (LIBs) degrade significantly during applications at sub-zero temperatures. Due to low temperatures, various chemical changes, such as the freezing of the electrolyte, make it difficult for the battery to provide high discharge rates. With the increasing application of high-power LIBs, especially in automotive applications, a detailed understanding of the impact of fast discharging of LIBs under sub-zero temperature is very crucial. Therefore, this study investigates the battery degradation under high discharge rates and sub-zero temperatures on 21700 Lithium Nickel Cobalt Aluminum oxide (NCA) battery. The battery was found to lose 34% capacity during 2C discharge, compared to 20.25% and 15% capacity loss during 1C and 0.5C discharge, respectively, at -20°C. The results indicate that with lower ambient temperatures and high discharge rates, a voltage sag appears in the initial stage of the discharge cycle due to rapid increase in internal impedances and decreased ionic conductivity in the battery, leading to accelerated battery aging and capacity fade. This voltage dip serves as a comparative metric for assessing the total usable capacity of the battery. The outcome of the research study reveals the actual performance of LIBs in automotive applications compared to its nominal performance claimed by manufacturers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".