Health-Conscious Fast Charging for Electrified Aircraft Batteries Using a Multistage-Constant-Current Temperature-Controlled Strategy
Bibliographic record
Abstract
The operational efficiency and widespread adoption of electric aircraft are highly dependent on their energy storage systems. Fast charging is essential for reducing downtime and improving turnaround times, but it can negatively impact battery health due to increased temperatures and accelerated chemical degradation. This issue becomes more pronounced under subzero conditions, where reduced chemical reaction rates increase internal impedance, leading to a greater rise in battery temperature and faster degradation. This article proposes a closed-loop multistage-constant-current, temperature-controlled (MCC-TC) fast charging strategy designed to preserve the health of aviation-grade batteries. MCC-TC algorithm modulates charging current by incorporating real-time battery temperature feedback. The experimental validation shows that the MCC-TC algorithm significantly reduces temperature rise ($\Delta {T}$) and the rate of temperature rise ($\Delta {T}$/$\Delta {t}$) compared to the conventional constant-current constant-voltage (CC-CV) method. At$- 5~^{\circ }$C and$30~^{\circ }$C, the MCC-TC algorithm achieved reductions in$\Delta {T}$and$\Delta {T}$/$\Delta {t}$of 47.68% and 65.35%, and 49.74% and 38.96%, respectively. These results highlight the potential of the algorithm to enhance battery health and improve the efficiency of the charging process.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".