Study of High-Power and High-Energy Lithium-ion Batteries: From Parameter Analysis to Physical Modeling and Experimental Validation
Bibliographic record
Abstract
This study presents a comparative analysis of Samsung lithium-ion batteries, which are the INR21700 30T high-power (HP) cell and INR21700 50E high-energy (HE) cell, examining their design differences and performance characteristics. Based on teardown data reported in literature, the HP cell features higher porosity, thicker current collectors, and thinner electrode coatings compared to the HE cell, while the HE cell incorporates approximately 6% silicon oxide in its graphite anode for increased energy density. Cell-level characterization test results demonstrated superior rate capability of the HP cell, maintaining 93.8% of its capacity at 2C discharge, while the HE cell retained 93.4% at 1.6C. The HP cell also exhibited better cycle life stability due to its silicon-free design. Pseudo-two-dimensional (P2D) models were constructed using both teardown experimental parameters and adjusted parameters. Simulation results revealed significant discrepancies using teardown parameters, particularly at high C-rates, with HP cell capacity overestimated by 6.22% at 2C and HE cell capacity underestimated by 12.38% at 1.6C. After parameter adjustment, simulation accuracy improved to within ±2% for most conditions. However, these required adjustments highlight limitations of the P2D model to fully represent actual physical processes using experimentally measured parameters.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".