The Complex and Spatially Heterogeneous Nature of Degradation in Heavily Cycled Li-ion Cells
Bibliographic record
Abstract
As service lifetimes of electric vehicle (EV) and grid storage batteries continually improve, it has become increasingly important to understand how cells perform after extensive cycling. The multifaceted nature of degradation in Li-ion cells can lead to complex behavior that may be difficult for battery management systems or operators to model. Accurate characterization of heavily cycled cells is critical for developing accurate models, especially for cycle-intensive applications like second-life grid storage or vehicle-to-grid charging. In this study, we use operando synchrotron x-ray diffraction (SR-XRD) to characterize a commercially manufactured polycrystalline NMC622 pouch cell that was cycled for more than 2.5 years. Using spatially resolved synchrotron XRD, the complex kinetics and spatially heterogeneous behavior of such cells are mapped and characterized under both near-equilibrium and non-equilibrium conditions. The resulting data is complex and multifaceted, requiring a different approach to analysis and modelling than what has been used in the literature. To show how material selection can impact the extent of degradation, we compare the results from polycrystalline NMC622 cells to an extensively cycled single-crystal NMC532 cell with over 20,000 cycles—equivalent to a total EV traveled distance of approximately 8 million km (5 million miles) over six years.
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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".