Effects of Fast Charging of EV Batteries at Low Temperatures Based on Temporary Lithium Plating and Temperature Gradients
Bibliographic record
Abstract
The study investigates battery degradation under high C-rates and subzero temperatures, analyzing temperature gradients (ΔT/Δt) and differential temperature rises (ΔT) on 21700 lithium nickel cobalt aluminum oxide (NCA) battery. The research findings identifies maximum ΔT at charging rates of 2.5C and 3C, reaching 24.6°C and 29.96°C, respectively, at an ambient temperature of -10°C. At -15°C, charging rate of 2C resulted in ΔT of 20.9°C. Moreover, the voltage dip during fast charging at subzero temperatures increases with lower ambient temperatures and higher C-rates. Comparative analysis underscores anode as the most heated part, exhibiting steeper ΔT/Δt curves. Although the battery management system (BMS) may regulate the total temperature rise within safe operating limits, however, often ΔT/Δt may cause accelerated battery degradation and thermal runaway condition, especially during dynamic charging/discharging conditions. Therefore, it is imperative to monitor battery ΔT/Δt, rather than only ΔT to ensure reduced accelerated degradation and thermal safety.
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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 | 0.000 | 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".