Understanding Capacity Loss in LFP/Graphite Pouch Cells at High Temperatures through Modelling
Bibliographic record
Abstract
Lithium iron phosphate is one of the most heavily utilized cathode materials in lithium-ion batteries owing to its safety and low cost. Often applied in energy storage, lifetime improvement for lithium iron phosphate batteries over different temperatures is of great importance. This work focuses on understanding capacity loss in lithium iron phosphate cells through the modelling of capacity fade curves collected from LiFePO 4 /graphite pouch cells cycled for up to two years under a variety of testing conditions. Capacity loss modelling was completed using a novel model which accounts for capacity loss incurred through lithium inventory loss and transition metal dissolution. Further, this work is presented in comparison to low voltage lithium nickel manganese cobalt oxide pouch cells. The results show a greater ability to predict capacity loss in lithium iron phosphate cells when the novel model is utilized as compared to other simple capacity fade models. The application of the model works well over a range of testing conditions and was validated through correlations made to physical cell parameters. From this work, solid electrolyte interphase growth behavior and iron dissolution are highlighted as some of the main causes of capacity loss at high temperatures for LFP/graphite cells.
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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 | 0.001 | 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".