Batteries for electric vehicles: Technical advancements, environmental challenges, and market perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The rapid evolution of electric vehicles (EVs) highlights the critical role of battery technology in promoting sustainable transportation. This review offers a comprehensive introduction to the diverse landscape of batteries for EVs. In particular, it examines the impressive array of available battery technologies, focusing on the predominance of lithium‐based batteries, such as lithium‐ion and lithium‐metal variants. Additionally, it explores battery technologies beyond lithium (“post‐lithium”), including aluminum, sodium, and magnesium batteries. The potential of solid‐state batteries is also discussed, along with the current status of various battery types in EV applications. The review further addresses end‐of‐life treatment strategies for EV batteries, including reuse, remanufacturing, and recycling, which are essential for mitigating the environmental impact of batteries and ensuring sustainable lifecycle management. Finally, market perspectives and potential future research directions for battery technologies in EVs are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it