Characteristics of Open Circuit Voltage Relaxation in Lithium-Ion Batteries for the Purpose of State of Charge and State of Health Analysis
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Bibliographic record
Abstract
Open circuit voltage relaxation to a steady state value occurs, and is measured, at the terminals of a lithium-ion battery when current stops flowing. It is of interest for use in determining state of charge and state of health. As voltage relaxation can take several hours, a representative model and curve fitting is necessary for practical usage. Previous studies of lithium-ion voltage relaxation investigate four characteristics: relationship between voltage relaxation magnitude and state of charge; length of relaxation required; model complexity for state of charge estimation; and model complexity for state of health evaluation. However, previous studies have inconsistent methodology or use only one type of lithium-ion cell, making comparison and generalization difficult. To address this, we conducted 3 h and 24 h voltage relaxation experiments over a range of states of charge on three different lithium ion chemistries (nickel cobalt aluminum NCA; nickel manganese cobalt NMC532; lithium iron phosphate LFP) and fitted them with a new voltage relaxation equivalent circuit model. It was found that a 3 h relaxation period was sufficient for NMC and LFP for state of charge and state of health investigations. Voltage relaxation of the NCA cell continued to evolve past 24 h. It was shown that voltage relaxation shape and magnitude changes as a function of state of charge, and the accuracy of estimating state of charge was explored. Strategically choosing a state of charge for state of health assessment can be optimized to accentuate voltage relaxation magnitude and this differs by chemistry. This suggested technique and experimental findings can be paired with battery degradation studies to determine accuracy of assessing state of health.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 it