Explicit Conversion between Different Equivalent Circuit Models for Electrochemical Impedance Analysis of Lithium-Ion Cells
Bibliographic record
Abstract
Despite the accuracy and non-intrusive nature of Electrochemical Impedance Spectroscopy, the impedance spectra of commercial Lithium-ion cells are notoriously hard to interpret. Consequently, the literature is filled with various equivalent circuit models, which differ greatly in their physical significance, but which produce very similar impedance spectra. In this paper, explicit formulas are given to convert between various equivalent circuits made of resistors and capacitors of the sort discussed in the literature. Furthermore, all these formulas have been implemented in a Python program, in the hope that studies done assuming one circuit might be compared to studies done with a different circuit, for instance. This paper considers cases where two different circuits can produce two impedance spectra which are identical. For instance, explicit conversions are given between Ladder circuits, Voight circuits, and Maxwell circuits for various time constants. This gives a conceptual foundation to explore the more difficult case of circuits producing impedance spectra which are similar to each other (e.g. within 5%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".