A Critical Review on Electric Vehicle Batteries — Based on Life Cycle Assessment Method
Bibliographic record
Abstract
Currently, the production of electric vehicles and the lithium batteries that powers them is increasing significantly. Thus, this dissertation combines the literature review approach and the life cycle assessment (LCA) method to investigate the environmental or social impact of lithium batteries for electric vehicles from cradle to grave. According to the literature review, the environmental impacts of lithium batteries include carbon dioxide and harmful electrolyte emissions, water pollution, air pollution, solid waste emissions, gaseous pollutants, volatile organic compounds, nitrogen oxides, heavy metals, organic pollutants, particulate matter and CO2 emissions. Therefore, lithium batteries for electric vehicles pose a great environmental concern. In addition, the confusion surrounding the definition of lithium batteries in LCA can pose a significant concern. The in-depth analysis reveals that there is a lack of uniform data on EV lithium batteries, a low recycling rate of lithium batteries, and a lack of waste stream management. There is no uniformity in the technology and development of lithium batteries among countries such as China, Canada, and the USA. As a result, the secondary data may only represent this region. Besides, the literature consulted covers a long period of time. There is little possibility that the negative environmental impact of earlier technology like the burning of lithium batteries in incinerators can now be improved. Moreover, lithium batteries are still in the development stage. Therefore, there is still room for potential development in the future.
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.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".