A modeling and experimental study of capacity fade for lithium-ion batteries
Bibliographic record
Abstract
Lithium-ion batteries are extensively used in electric vehicles, however, their significant degradation over discharge and charge cycles results in severe capacity fade, limiting driving ranges of electric vehicles over time and useful lifetime of batteries. In this study, capacity fade for lithium-ion battery has been investigated through modeling and experiment. A predictive model is developed based on first principles incorporating degradation mechanisms. The mechanisms of degradation considered include solid-electrolyte interface (SEI) growth and active material loss at both negative and positive electrodes. Battery performance including capacity is measured experimentally under discharge and charge cycling with battery operation temperature controlled. It is shown that battery capacity is reduced over battery discharge/charge cycling at a given battery operation temperature, and the model predicted battery performance, including capacity fade, agrees well with the experimental results. As the number of discharge/charge cycles are increased, battery capacity is reduced significantly; battery capacity fade is increased substantially when battery operation temperature is increased, indicating significantly accelerated aging of the battery at elevated operation temperatures and hence the importance of battery thermal management in the control of battery operation temperature for practical applications such as electric vehicles. Battery capacity fade is mainly caused by SEI film growth at the negative electrode, which is the largest contributing factor to the capacity fade, and the active material isolation at the negative electrode, which is the second largest influencing aging factor.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".