Electric vehicle battery state changes and reverse logistics considerations
Bibliographic record
Abstract
Electric Vehicles are becoming trendy and proved to have no harmful exhaust like traditional fuel-powered vehicles. As the world shifts towards electric vehicle adoption, lithium-ion batteries are becoming the primary power source for this transition. The goal of this research is to analyze the lifespan and long-term ratio composition of Lithium-Ion batteries in electric vehicles by developing two models, an Absorbing Markov Chain model, and a Markov Chain Steady-State Census model. A sensitivity analysis is also conducted to alleviate the scarcity of enough input data. This research work shows that the lifespan of batteries can be extended by 4.5 years, which will have a positive environmental impact and reap economic benefits. Moreover, the long-term composition of batteries in New, Remanufactured, Repurposed and Recycled states can be projected. By considering reverse logistics processes, it is possible to recycle batteries and recover the valuable materials otherwise lost. Not only does this support the environment but given the rising demand and finite raw material supply, there is an opportunity to capture the economic benefit of recycling. From this research, the recovered materials cobalt, lithium, and nickel are calculated, and this is especially important for the optimal planning of sustainable manufacturing
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".