Dataset: Practical considerations and limitations of online EIS-based battery internal temperature estimation in traction applications
Bibliographic record
Abstract
This research aims to find an universally applicable impedance feature and frequency range for electrochemical impedance spectroscopy (EIS)-based internal cell temperature estimation independent of of cell capacity and chemistry. Based on this meta-analysis of 68 publications comprising 83 cells, extended by our own measurements, we propose the impedance phase, evaluated at frequencies between 100 Hz and 1 kHz, as an optimal estimator for internal cell temperature. This repository contains three datasets of battery EIS data and derived parameters compiled from multiple published studies and our own experimental data: 1. Dataset A: Temperature sensitivities of 86 EIS-based internal cell temperature estimators 2. Dataset B: Nyquist curve features of 44 cells 3. EIS: EIS measurements from six cells as a function of temperature For detailed information about data structure, experimental protocols, and data processing methodologies, refer to the README.md file included with the dataset. Corresponding publication: https://doi.org/10.1016/j.jpowsour.2025.239111
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".