Comparative Analysis of Cell Design: Form Factor and Electrode Architectures in Advanced Lithium-Ion Batteries
Bibliographic record
Abstract
This review investigates how cell form factors (cylindrical, prismatic, and pouch) and electrode architecture (jelly-roll, stacked, and blade) influence the performance, safety, and manufacturability of lithium-ion batteries (LIBs) across the main commercial chemistries LiFePO4 (LFP), Li (NiMnCo)O2 (NMC), LiNiCoAlO2 (NCA), and LiCoO2 (LCO). Literature, OEM datasheets, and teardown analyses published between 2015 and 2025 were examined to map the interdependence among geometry, electrode design, and electrochemical behavior. The comparison shows trade-offs among gravimetric and volumetric energy density, thermal runaway tolerance, cycle lifespan, and cell-to-pack integration efficiency. LFP, despite its lower nominal voltage, offers superior thermal stability and a longer cycle life, making it suitable for both prismatic and blade configurations in EVs and stationary storage applications. NMC and NCA chemistries achieve higher specific energy and power by using jelly-roll architectures that are best suited for tabless or multi-tab current collection, enhancing uniform current distribution and manufacturability. Pouch cells provide high energy-to-weight ratios and flexible packaging for compact modules, though they require precise mechanical compression. LCO remains confined to small electronics owing to safety and cost limitations. Although LFP’s safety and affordability make it dominant in cost-sensitive applications, its low voltage and energy density limit broader adoption. LiMnFePO4 (LMFP) cathodes offer a pathway to enhance voltage and energy while retaining cycle life and cost efficiency; however, their optimization across various form factors and electrode architecture remains underexplored. This study establishes an application-driven framework linking form factors and electrode design to guide the design and optimization of next-generation lithium-ion battery systems.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".