Exploring Impedance Growth in High Voltage NMC/Graphite Li-Ion Cells Using a Transmission Line Model
Bibliographic record
Abstract
A discrete transmission line model (TLM) for the impedance of the positive electrode in a Li-ion cell was studied to investigate causes of impedance increase for Li[Ni 0.42 Mn 0.42 Co 0.16 ]O 2 (NMC442) positive electrodes operated at high voltage (> 4.4 V vs. Li/Li + ). The TLM included contact resistance between the conductive carbon and the active particles (R c ), electrical path resistance through the carbon network (R e ), ionic path resistance through the bulk electrolyte (R i ) and transfer resistance/capacitance (R s , C) through the SEI layers formed on the active particles. It was found that an increase in any of R e , R i or R c was necessary to increase the high frequency intercept of the impedance spectra. A limited increase in the spectrum diameter of the TLM was achievable by increasing R e or R i , but an unlimited increase was only possible by increasing the SEI resistance R s . Comparison with experiment concluded that the high voltage impedance growth observed in NMC442/graphite cells is primarily due to increases in R s , while minor increases in R e , R i or R c may occur. A brief investigation of inhomogeneous SEI capacitance/resistance produced impedance spectra with a range of heights and asymmetries. This can explain in part the variety of shapes of impedance spectra from real impedance measurements of Li-ion 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".