Critical Understanding of Temperature Gradient During Fast Charging of Lithium-ion Batteries at Low Temperatures
Bibliographic record
Abstract
Fast charging of lithium-ion battery (LIB) packs at low temperatures can have several effects on the performance and overall health of the battery. Repetitive fast charging at low temperatures accelerates internal resistance growth, leading to inefficient charging. Slow and inefficient chemical reactions at low temperatures result in slower charging rates and increased heat generation. Furthermore, repeated fast charging at subzero temperatures accelerates degradation processes due to increased wear on the battery, significantly reducing the cycle life of the battery. This research paper presents a series of experimental studies conducted on a 21700 Lithium-Nickel-Manganese-Cobalt-Oxide (NMC) LIB cell to investigate the temperature gradient and its impact on battery performance at a wide range of ambient temperatures (-5°C to 25°C) and charging rate (1C to 2 C). The findings highlight the highest rate of change of surface temperature and differential temperature (15°C) with a charging rate of 2 C at ambient temperature of -5°C. Moreover, a reduction in battery discharge performance is observed during low-temperature charging compared to charging at 25°C with the same charging rate. These findings are crucial for the development of health-conscious fast charging algorithms, improved thermal management techniques, and the establishment of a thermal safety framework.
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 | 0.000 | 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".