Investigating the Applicability of Electrochemical Impedance Spectroscopy for Parallel-Connected Lithium-ion Battery Modules
Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) is a non-destructive method of testing that provides information on the internal processes within a cell. Previous studies using EIS have primarily focused on the cell level, with few studies conducted at the module or pack level. This study applies EIS to eight 4P1S modules at varying states-of-charge (100%, 50%, and 0%) and degrees of aging. At 100% SOC, the modules show relatively high impedance values and good distinguishability between modules with different states-of-health (SOH). As the overall SOH decreases, both the real and imaginary impedance values at the transition frequency show a clear increasing trend. Additionally, the transition frequencies cluster between 0.025 to 0.050 Hz, indicating that a small range of low-frequency signals may be sufficient for obtaining diagnostic information. For this study, a custom spring-loaded battery fixture was developed, allowing cells to be connected in parallel and series configurations without the need for welding. The repeatability of results with inserted and removed cells was also studied. The results indicate that module-level EIS tests on parallel-connected cells are strongly correlated with the overall SOH of the module and provide foundational information for implementing EIS tests at the module and pack level for state estimation, fault detection, and rapid battery sorting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".