Ni‐Rich/Co‐Poor Layered Cathode for Automotive Li‐Ion Batteries: Promises and Challenges
Bibliographic record
Abstract
Abstract To pursue a higher energy density (>300 Wh kg −1 at the cell level) and a lower cost (<$125 kWh −1 expected at 2022) of Li‐ion batteries for making electric vehicles (EVs) long range and cost‐competitive with internal combustion engine vehicles, developing Ni‐rich/Co‐poor layered cathode (LiNi 1− x − y Co x Mn y O 2 , x + y ≤ 0.2) is currently one of the most promising strategies because high Ni content is beneficial to high capacity (>200 mAh g −1 ) while low Co content is favorable to minimize battery cost. Unfortunately, Ni‐rich cathodes suffer from limited structure stability and electrode/electrolyte interface stability in the charged state, leading to electrode degradation and poor cycling performance. To address these problems, various strategies have been employed such as doping, structural optimization design (e.g., core–shell structure, concentration‐gradient structure, etc.), and surface coating. In this review, five key aspects of Ni‐rich/Co‐poor layered cathode materials are explored: energy density, fast charge capability, service life including cycling life and calendar life, cost and element resources, and safety. This enables a comprehensive analysis of current research advances and challenges from the perspective of both academy and industry to help facilitate practical applications for EVs in the future.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".