Battery Entropic Heating Coefficient Testing and Use in Cell-Level Loss Modeling for Extreme Fast Charging
Bibliographic record
Abstract
To achieve an accurate estimate of losses in a battery it is necessary to consider the reversible entropic losses, which may constitute over 20% of the peak total loss. In this work, a procedure for experimentally determining the entropic heating coefficient of a lithium-ion battery cell is developed. The entropic heating coefficient is the rate of change of the cell’s open-circuit voltage (OCV) with respect to temperature; it is a function of state-of-charge (SOC) and temperature and is often expressed in mV/K. The reversible losses inside the cell are a function of the current, the temperature, and the entropic heating coefficient, which itself is dependent on the cell chemistry. The total cell losses are the sum of the reversible and irreversible losses, where the irreversible losses consist of ohmic losses in the electrodes, ion transport losses, and other irreversible chemical reactions. The entropic heating coefficient is determined by exposing the cell to a range of temperatures at each SOC value of interest. The OCV is recorded at each combination of SOC and temperature, and ∂OCV/∂T is calculated from the measurements. Since a ∆T of 20°C may result in a ∆OCV of 100μV or less, it is critical to have a high accuracy and high input impedance voltage sensor. Additionally, the measurement is sensitive to self-discharge of the battery and the amount of time at each test point. A test methodology is proposed which achieves an accurate OCV measurement, corrects for self-discharge, and utilizes the minimum necessary soak time. Once determined experimentally, the entropic heating coefficient map is used to model losses during several charges with rates from 1C to 5C, and the results are compared with a constant current loss model derived from experimental data for a lithium nickel manganese cobalt oxide (NMC) cell.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".