Optimal Multistage Charging of NCA/Graphite Lithium-Ion Batteries Based on Electrothermal-Aging Dynamics
Bibliographic record
Abstract
Lithium-ion (Li-ion) batteries have been extensively used in electric vehicles, portable electronics, cell phones, and laptops. The charging protocol, as one of the most critical technologies for Li-ion battery systems, has a significant impact on battery performance. Charging current affects battery degradation and charging time, and therefore, it needs to be carefully optimized. To this end, a novel charging protocol using a series of constant charging currents has been developed, which considers the charging time and the battery capacity fade simultaneously. These two conflicting charging objectives are traded off by solving a multiobjective optimization problem based on battery electrothermal-aging behavior. Particle swarm optimization has been applied to obtain the optimal charging current profile. Three optimal charging strategies for minimum charging time, minimum battery aging, and balanced charging performance are obtained by changing the weight factor. The proposed balanced charging is capable of reducing the charging time significantly with a negligible increase in capacity degradation compared with the 0.5 C constant-current constant-voltage (CC-CV) strategy recommended by the manufacturer.
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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 | 0.001 | 0.000 |
| 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".