Quantitatively Mapping Inhomogeneous State of Charge in a Commercial Lithium-Ion Pouch Cell via Energy-Resolved Neutron Imaging
Bibliographic record
Abstract
The booming electric vehicle market is fueling demand for higher energy density and enhanced safety in lithium-ion batteries. State of charge (SOC), a critical metric for assessing battery performance, provides insights into the energy status, health, and safety of the battery. Commercial cells often exhibit heterogeneous SOC distributions that are challenging to measure. We employ advanced energy-resolved neutron imaging to achieve quantitative SOC mapping of a commercial 2.5 Ah LiFePO 4 ||graphite pouch cell charged under a controlled temperature field (0–23 °C). Our findings show that higher temperature regions have more LiC 6, indicating higher SOC levels. Synchrotron high-energy X-ray diffraction confirmed this distribution and its correlation with the cathode. A high-throughput analysis of SOC–temperature correlations using polynomial regression on 4000 experimental data points achieved an R 2 value of 0.9823, demonstrating robust modeling. This research underscores neutron imaging’s role in nondestructive mapping and machine learning applications for advancing battery detection technologies and improving performance predictions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".